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Millimeter wave (mmWave) spectrum promises unprecedented data rates in 5G New Radio (NR). However, mmWave links are susceptible to severe path and propagation losses. Implementation of directional antennas that can manifest multiple beams becomes necessary at 5G base stations (gNBs) and User Equipments (UEs). Random access procedure (RAP), which is the first stage to establish connections to gNB, in its current state would be inadequate for such systems. To support RAP, 5G NR introduced mapping of different subsets of preambles in Physical Random Access Channel (PRACH) to different beams. We explore changes in RAP due to preamble-beam mapping and present a novel online algorithm to dynamically distribute preambles amongst beams to achieve fair access opportunities. The proposed algorithm can also eliminate overload problem of the beams with large UE population especially for high arrival rates. We extend the proposed algorithm to be applicable for RAP with access class barring mechanism which enables UEs to transmit a preamble with an optimal probability and, therefore, reduce access congestion. Through extensive simulations, we validate the efficiency of our proposed algorithms in providing fair channel access opportunities, improving throughput (or stability region), and reducing access delay when the system has high rate of UE arrivals and has unbalanced UE arrivals over different beams.

The integration of non-terrestrial networks into the 5G ecosystem is mainly driven by the possibility of provisioning service in remote areas. In this context, the advent of flying base stations at the low Earth orbit (LEO) will enable anywhere and anytime connectivity. To materialize this vision, it is of utmost importance to improve radio protocols with the aim of allowing direct satellite access. Bearing this aspect in mind, we present a new random access signal, which is based on the filter bank multicarrier (FBMC) waveform, and a computationally efficient detection scheme. The proposed solution outperforms the standardized access scheme based on single-carrier frequency division multiplexing (SC-FDM), by reducing out-of-band (OOB) emissions and reducing the missed detection probability in presence of very high carrier frequency offset (CFO), which is inherent to LEO satellite systems. The improvement is related to the fine frequency resolution of the detector and the use of pulse shaping techniques. Interestingly, the FBMC-based random access signal achieves a high level of commonality with 5G new radio, as the preamble generation method and the time-frequency allocation pattern can be kept unchanged. Concerning the practical implementation aspects, the complexity of the detector is similar in both SC-FDM and FBMC. This paper is part of the R+D+i project (PID2020-115323RB-C31) funded by MCIN/AEI/ 10.13039/501100011033. This work is supported by the grant from Spanish Ministry of Economic Affairs and Digital Transformation and the European union - NextGenerationEU (UNICO-5G I+D/AROMA3D-Space (TSI-063000-2021-70))

In this work, we consider a network, where n distributed information sources whose states evolve according to a random process transmit their time-varying states to a remote estimator over a shared wireless channel. Each source generates packets in a decentralized manner and employs a slotted random access mechanism to transmit the packets. In particular, we are interested in networks with a large number of low-complexity devices that share low-capacity random access channels. Accordingly, we investigate update strategies for remote tracking of source states that require each update to constitute as few bits as possible. To that end, we develop update strategies requiring only one-bit of information per update. We first consider a natural benchmark update policy and reveal that the benchmark policy cannot guarantee stability under all conditions. We then introduce an improvement of the benchmark policy that employs a local cancellation strategy, which makes the system always stable. We further analyze and optimize the performance of the cancellation-enabled update policy to bound the estimation error at the receiver. Through simulations, we compare the proposed cancellation-enabled one-bit update policy with zero-wait sampling and threshold-based sampling policies that require more than one-bit of information per update. The comparisons show that the cancellation-enabled update policy at its optimal threshold level outperforms the multi-bit update policies. This suggests that the cancellation-enabled one-bit update policy could be greatly beneficial for applications where transmission power or shared channel capacity are limited.

As public safety migrates from existing digital land mobile radio networks to fourth generation (4G) long-term evolution (LTE), a novel feature in the latter is the enablement of direct communications between devices. In this article, we examine the performance of the LTE device-to-device direct discovery process in out-of-coverage scenarios (Mode 2). We model individual discovery periods as a slotted random access protocol with half-duplex user equipment (UE). We outline a simulation framework using the open-source network simulator 3 (ns-3) and evaluate the discovery process’s performance as a function of the size of the resource pool, UE density, and the transmission probability. We establish that there exists an optimal transmission probability that minimizes discovery time for a given network configuration. However, because a UE cannot know the number of neighbors when it goes off-network, we develop a method to allow UEs to learn the optimal transmission probability during discovery. We compare our algorithm’s performance to the 3rd Generation Partnership Project standards prescribed method and others to demonstrate its superiority.

Random access schemes in satellite Internet of Things (IoT) networks are being considered a key technology of new-type machine-to-machine (M2M) communications. However, the complicated situations and long-distance transmission can make the current random access schemes not suitable for the satellite IoT networks. The random access problem in the satellite IoT networks is studied in this paper. A novel random access scheme for machine-type-communication devices (MTCDs) is proposed, to maximize the efficiency of random access for contention-based and contention-free random access. Under the set of random access opportunities (RAOs) and limited delay, the random access control model is designed via maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) algorithm is proposed to tackle the problem based on the random access model. Subsequently, the deep Dyna-Q learning algorithm is introduced to deal with the proposed random access control model. In this proposed scheme, the random access model-free DRL algorithm is developed using simulated experience. The proposed algorithms’ performances are discussed, and simulation results show the desirable performance of the proposed DRL methods on different system parameters.

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of things (IoT) terrestrial networks (TNs). In this context, a study item to integrate satellites into narrowband-IoT (NB-IoT) systems has been approved within the 3rd Generation Partnership Project (3GPP) standardization body. However, as NB-IoT systems were initially conceived for TNs, their basic design principles and operation might require some key modifications when incorporating the satellite component. These changes in NB-IoT systems, therefore, need to be carefully implemented in order to guarantee a seamless integration of both TN and non-terrestrial network (NTN) for a global coverage. This paper addresses this adaptation for the random access (RA) step in NB-IoT systems, which is in fact the most challenging aspect in the NTN context, for it deals with multi-user time-frequency synchronization and timing advance for data scheduling. In particular, we propose an RA technique which is robust to typical satellite channel impairments, including long delays, significant Doppler effects, and wide beams, without requiring any modification to the current NB-IoT RA waveform. Performance evaluations demonstrate the proposal’s capability of addressing different NTN configurations recently defined by 3GPP for the 5G new radio system.

Due to recent explosion of data volume and velocity, a new array of lightweight key-value stores have emerged to serve as alternatives to traditional databases. The majority of these storage engines, however, sacrifice their read performance in order to cope with write throughput by avoiding random disk access when writing a record in favor of fast sequential accesses. But, the boundary between sequential vs. random access is becoming blurred with the advent of solid-state drives (SSDs). In this work, we propose our new key-value store, LogStore, optimized for hybrid storage architectures. Additionally, introduce a novel cost-based data staging model based on log-structured storage, in which recent changes are first stored on SSDs, and pushed to HDD as it ages, while minimizing the read/write amplification for merging data from SSDs and HDDs. Furthermore, we take a holistic approach in improving both the read and write performance by dynamically optimizing the data layout, such as deferring and reversing the compaction process, and developing an access strategy to leverage the strengths of each available medium in our storage hierarchy. Lastly, in our extensive evaluation, we demonstrate that LogStore achieves up to 6x improvement in throughput/latency over LevelDB, a state-of-the-art key-value store

Massive machine-type communication (MTC) is expected to be one of the key concerns in future B5G and 6G. However, the explosive growth of MTC devices (MTDs) in Internet of Things (IoT) poses great challenges for the initial access procedure. To address this issue, we propose a cluster-aided collision resolution random access (CACRRA) protocol for grant-based random access (RA) in the distributed massive multiple input multiple output (mMIMO) systems, which takes full advantage of the spatial multiplexing provided by distributed mMIMO. By exploiting the difference of large-scale fading coefficients between different access points (APs) and MTDs, we further propose an exclusive clustering algorithm to cluster APs for each pilot. Moreover, the pilot collision resolution is performed distributedly in each cluster by leveraging the channel hardening and the favorable propagation of mMIMO channels, which achieves a large reuse factor of pilots. Simulation results show that the proposed CACRRA can efficiently resolve the pilot collision and significantly improve the access performance. Specifically, the proposed protocol can improve the RA throughput more than 9 times compared to the conventional protocol in incredibly crowded scenarios.

The role of various internet-of-things (IoT) devices responsible for data collection and reporting becomes more important in the era of Industry 4.0. Due to the various advantages (e.g., wide coverage, robust security, etc.), the cellular networks have been continuously evolved to accommodate IoT scenario. In IoT scenario, connection establishment is essential and primary for enabling IoT devices to communicate with centralized unit (e.g., base station (BS)). This connection establishment procedure in cellular networks, random access procedure, is generally operated in a contention-based manner. So, it is vulnerable to simultaneous connection requests from multiple IoT devices to the BS, which becomes worse as the contention participants increase. In this article, we newly propose a resource-efficient parallelized random access (RePRA) procedure for resource-efficiently ensuring reliable connection establishment in cellular-based massive IoT networks. Key features of our proposed technique are twofold: (1) Each IoT device simultaneously performs multiple RA procedures in parallel to improve connection establishment success probability, and (2) the BS handles excessive use of radio resources based on newly proposed two types of redundancy elimination mechanisms. Through extensive simulations, we evaluate the performance of our proposed technique in terms of connection establishment success probability and resource efficiency under various combinations of control parameters. Consequently, we verify the feasibility of our proposed technique for reliably and radio-efficiently supporting a large number of IoT devices.

We consider a multichannel random access system in which each user accesses a single channel at each time slot to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between active users. Unlike the previous approaches adjusting channel access probabilities at each time slot, the proposed RL algorithm deterministically selects a set of channel access policies for several consecutive time slots. To effectively reduce the complexity of the proposed RL algorithm, we adopt a branching dueling Q-network architecture and propose an efficient training methodology for producing proper Q-values over time-varying user sets. We perform extensive simulations on realistic traffic environments and demonstrate that the proposed online learning improves both throughput and fairness compared to the conventional RL approaches and centralized scheduling policies., 20 pages, 12 figures

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks, where a large-number of nodes are distributed, severe interference may incur during the transmission phase which causes age of information (AoI) degradation. It is therefore important to study the performance limit of AoI as well as how to achieve such limit. In this paper, we aim to optimize the AoI in random access Poisson networks. By taking into account the spatio-temporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on explicit expressions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived. It is shown that with a given packet arrival rate (resp. a given channel access probability), the optimal channel access probability (resp. the optimal packet arrival rate), is equal to one under a small node deployment density, and decrease monotonically as the spatial deployment density increases due to the severe interference caused by spatio-temproal coupling between transmitters. When joint tuning of the packet arrival rate and channel access probability is performed, the optimal channel access probability is always set to be one. Moreover, with the sole tuning of the channel access probability, it is found that the optimal peak AoI performance can be improved with a smaller packet arrival rate only when the node deployment density is high, which is contrast to the case of the sole tuning of the packet arrival rate, where a higher channel access probability always leads to better optimal peak AoI regardless of the node deployment density. In all the cases of optimal tuning of system parameters, the optimal peak AoI linearly grows with the node deployment density as opposed to an exponential growth with fixed system parameters.

Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space-air-ground integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in the IoV, where aggregated packets can be relayed to a base station (BS). In this paper, we first propose an aggregators-aided random access (ARA) scheme for the IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a Semi-Markov chain is used to analyze the average number of aggregated packets and the metric of average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize the ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

Grant-free sparse code multiple access (GF-SCMA) is considered to be a promising multiple access candidate for future wireless networks. In this paper, we focus on characterizing the performance of uplink GF-SCMA schemes in a network with ubiquitous connections, such as the Internet of Things (IoT) networks. To provide a tractable approach to evaluate the performance of GF-SCMA, we first develop a theoretical model taking into account the property of multi-user detection (MUD) in the SCMA system. Then, the error rate performance of GF-SCMA in the case of codebook collision is analyzed to investigate the reliability of GF-SCMA when reusing codebook in massive IoT networks. For performance evaluation, accurate approximations for both success probability and average symbol error probability (ASEP) are derived.To elaborate further, the analytical results are utilized to discuss the impact of codeword sparse degree in GF-SCMA. After that, we conduct a comparative study between SCMA and its variant, dense code multiple access (DCMA), with GF transmission to offer insights into the effectiveness of these two schemes. This facilitates the GF-SCMA system design in practical implementation. Simulation results show that denser codebooks can help to support more UEs and increase the reliability of data transmission in a GF-SCMA network. Moreover, a higher success probability can be achieved by GF-SCMA with denser UE deployment at low detection thresholds since SCMA can achieve overloading gain.

Recently, many researches on convolution neural network (CNN) based in-loop filters have been proposed to improve coding efficiency. However, most existing CNN based filters tend to train and deploy multiple networks for various quantization parameters (QP) and frame types (FT), which drastically increases resources in training these models and the memory burdens for video codec. In this paper, we propose a novel variable CNN (VCNN) based in-loop filter for VVC, which can effectively handle the compressed videos with different QPs and FTs via a single model. Specifically, an efficient and flexible attention module is developed to recalibrate features according to QPs or FTs. Then we embed the module into the residual block so that these informative features can be continuously utilized in the residual learning process. To minimize the information loss in the learning process of the entire network, we utilize a residual feature aggregation module (RFA) for more efficient feature extraction. Based on it, an efficient network architecture VCNN is designed that can not only effectively reduce compression artifacts, but also can be adaptive to various QPs and FTs. To address training data imbalance on various QPs and FTs and improve the robustness of the model, a focal mean square error loss function is employed to train the proposed network. Then we integrate the VCNN into VVC as an additional tool of in-loop filters after the deblocking filter. Extensive experimental results show that our VCNN approach obtains on average 3.63%, 4.36%, 4.23%, 3.56% under all intra, low-delay P, low-delay, and random access configurations, respectively, which is even better than QP-Separate models.

We investigate the age-of-information (AoI) in the context of random access networks, in which transmitters need to send a sequence of information packets to the intended receivers over a shared spectrum. Due to interference, the dynamics at the link pairs will interact with each other over both space and time, and the effects of these spatiotemporal interactions on the AoI are not well understood. In this paper, we straddle queueing theory and stochastic geometry to establish an analytical framework, that accounts for the interplay between the temporal traffic attributes and spatial network topology, for such a study. Specifically, we derive accurate and tractable expressions to quantify the network average AoI as well as the outage probability of peak AoI. Besides, we develop a decentralized channel access policy that exploits the local observation at each node to make transmission decisions that minimize the AoI. Our analysis reveals that when the packet transmissions are scheduled in a last-come first-serve (LCFS) order, whereas the newly incoming packets can replace the undelivered ones, depending on the deployment density, there may or may not exist a tradeoff on the packet arrival rate that minimizes the network average AoI. Moreover, the slotted ALOHA protocol is shown to be instrumental in reducing the AoI when the packet arrival rates are high, yet it cannot contribute to decreasing the AoI in the regime of infrequent packet arrivals. The numerical results also confirm the efficacy of the proposed scheme, where the gain is particularly pronounced when the network grows in size because our method is able to adapt the channel access probabilities with the change of ambient environment., arXiv admin note: substantial text overlap with arXiv:2008.07717

This paper considers a Gaussian multiple-access channel with random user activity where the total number of users $\ell_n$ and the average number of active users $k_n$ may grow with the blocklength $n$. For this channel, it studies the maximum number of bits that can be transmitted reliably per unit-energy as a function of $\ell_n$ and $k_n$. When all users are active with probability one, i.e., $\ell_n = k_n$, it is demonstrated that if $k_n$ is of an order strictly below $n/\log n$, then each user can achieve the single-user capacity per unit-energy $(\log e)/N_0$ (where $N_0/ 2$ is the noise power) by using an orthogonal-access scheme. In contrast, if $k_n$ is of an order strictly above $n/\log n$, then the capacity per unit-energy is zero. Consequently, there is a sharp transition between orders of growth where interference-free communication is feasible and orders of growth where reliable communication at a positive rate per unit-energy is infeasible. It is further demonstrated that orthogonal-access schemes in combination with orthogonal codebooks, which achieve the capacity per unit-energy when the number of users is bounded, can be strictly suboptimal. When the user activity is random, i.e., when $\ell_n$ and $k_n$ are different, it is demonstrated that if $k_n\log \ell_n$ is sublinear in $n$, then each user can achieve the single-user capacity per unit-energy $(\log e)/N_0$. Conversely, if $k_n\log \ell_n$ is superlinear in $n$, then the capacity per unit-energy is zero. Consequently, there is again a sharp transition between orders of growth where interference-free communication is feasible and orders of growth where reliable communication at a positive rate is infeasible that depends on the asymptotic behaviours of both $\ell_n$ and $k_n$. It is further demonstrated that orthogonal-access schemes, which are optimal when $\ell_n = k_n$, can be strictly suboptimal., 50 pages. Submitted to the IEEE Transactions on Information Theory. Revised paper according to the comments by the reviewers. This article supersedes our previous submissions arXiv:1904.11742 and arXiv:2005.07436

Millimeter-wave (mmW)/Terahertz (THz) wideband communication employing a large-scale antenna array is a promising technique of the sixth-generation (6G) wireless network for realizing massive machine-type communications (mMTC). To reduce the access latency and the signaling overhead, we design a grant-free random access scheme based on joint active device detection and channel estimation (JADCE) for mmW/THz wideband massive access. In particular, by exploiting the simultaneously sparse and low-rank structure of mmW/THz channels with spreads in the delay-angular domain, we propose two multi-rank aware JADCE algorithms via applying the quotient geometry of product of complex rank-$L$ matrices with the number of clusters $L$. It is proved that the proposed algorithms require a smaller number of measurements than the currently known bounds on measurements of conventional simultaneously sparse and low-rank recovery algorithms. Statistical analysis also shows that the proposed algorithms can linearly converge to the ground truth with low computational complexity. Finally, extensive simulation results confirm the superiority of the proposed algorithms in terms of the accuracy of both activity detection and channel estimation., IEEE Transactions on Wireless Communications, 2021

This paper considers NOMA-ALOHA, ALOHA implemented over NOMA (non-orthogonal multiple access), that can decode simultaneously transmitted multiple signals via successive interference cancellation (SIC). We analyze the throughput of NOMA-ALOHA from the perspective of MAC layer when the maximum two signals can be simultaneously decoded and there are no more than NSIC users, where NSIC is the smallest number K such that two-level SIC cannot work if more than K users simultaneously transmit a packet. We mathematically prove that the maximum throughput of NOMA-ALOHA is always higher than 0. 593 and lower than 0.724 when total number of users is no bigger than NSIC and that NOMA-ALOHA enhances the throughput of traditional slotted ALOHA at least by 57.8 percent if NSIC is no more than 50. We suggest practical transmission probabilities that yield a throughput close to the maximum throughput.

Low latency is a prerequisite for real-time applications, and random access is almost an inevitable choice for large-scale wireless networks. In this article, we consider how to decrease access delay in random access manners for the industrial Internet of Things. We propose a novel scheme by integrating multiple subchannels on the user equipment (UE) side with message-level successive interference cancellation (SIC) on the sink side. Specifically, for any UE that starts an uplink transmission, it just stochastically chooses parts of its subchannels and then simultaneously transmits the same message on them without any power control. On the sink side, the sink decodes signals from UEs using SIC across multiple subchannels. Based on the proposed multichannel SIC scheme, minimizing the access delay in the random access manner is equivalent to minimizing the probability of decoding deadlock. Based on the mathematical method of density evolution and the help from the differential evolution algorithm, an optimal distribution for choosing subchannels is presented. With the optimal distribution, the average access delay is minimized.

To meet the tremendous demand of Internet of Things (IoT) applications, the 3rd Generation Partnership Project (3GPP) has specified the Narrowband IoT (NB-IoT) standard. However, collisions in the random access (RA) channel of NB-IoT can be severe due to mismatch between frequent random access attempts by a huge number of devices and the limited radio resources. In this paper, a new random access mechanism called time-alignment-value based random access (TARA) is designed to improve the efficiency of random access of NB-IoT. The key mechanism of TARA is to conduct quick retries upon failure by exploiting the difference of time-alignment (TA) values in collided preambles. To analyze TARA rigorously, a theoretical model of TARA is derived, and its validity is verified via simulations. Further simulations are carried out to evaluate TARA with respect to different system parameters. Comparisons with existing schemes are also conducted. Both analytical and simulation results show that, under various system parameters, TARA achieves 30% higher success probability of random access, 75% higher throughput, and 40% lower access delay than the original slotted Aloha mechanism of NB-IoT. It also highly outperforms other random access schemes.

Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vectors (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading.

Static random access memories (SRAMs) are prone to a single-event upset (SEU), also known as soft errors, due to transient noise caused by a single strike of radiation. Beam testing has been extensively used to measure the SEU cross section of SRAMs as a function of the linear energy transfer (LET) of charged particle radiation. The evolution of the cross section as a function of LET is called the cross section curve, which plays a vital role in upset rate analysis for hardness assurance. Various analytical models have been developed to describe SRAM SEU cross-section curves, and they have proven to be useful in reducing the cost of beam testing as well as revealing the physics behind test results. However, they involve arbitrary parameters, which make it challenging to predict cross-section curves without any beam results. Moreover, the current method of analyzing cross-section curves or the LET dependence of cross sections relies on a model different from that is used in the analysis of power-supply-voltage dependence, which is becoming increasingly important because of the demand for low-power operation. To overcome these problems, this paper proposes a unified equation that describes both LET and the power-supply-voltage dependence of SRAM SEU cross sections. It comprises only parameters that are physically clear and familiar to SEU researchers. As well as giving possible constraints, comparisons with data from the literature suggest it can be applied to SRAMs fabricated in bulk and silicon-on-insulator (SOI) processes across generations from the early 1000-nm-scale to the current 10-nm-scale technology nodes.

Focal stack images (FoSIs) are a set of 2D images that captured one scene with serial focal depths. The redundancies of FoSIs mainly come from gradual focused depths changes rather than motion of objects. Conventional coding schemes cannot fully exploit such redundancies, leading to coding inefficiency. In this paper, we propose a new Gaussian-Wiener representation to model the gradual focused depths changes among FoSIs. In the representation, image degradation-restoration relations are utilized to describe the focus-defocus changing characteristics of FoSIs. Based on this representation, we propose a new hierarchical coding scheme for fully exploiting the inter-frame redundancies of FoSIs. In the scheme, a Gaussian-Wiener representation based inter prediction (GWR-IP) is presented by embedding Gaussian convolution and Wiener deconvolution into normal video encoder. Block-wise focus-defocus changing of FoSIs can be predicted in bi-directional manner by solving optimization problem. For higher coding efficiency, a Gaussian-Wiener representation based hierarchical prediction structure (GWR-HPS) is also designed and applied in the coding scheme. The proposed coding scheme is performed on 10 test sequences, including 5 synthetic scenes and 5 realistic scenes. Experimental results show that proposed coding scheme can obtain 2.640 dB PSNR gains and 51.830% bitrate savings on average of all test sequences in Low Delay P configuration, 2.123 dB PSNR gains and 43.975% bitrate savings in Low Delay B configuration, and 1.044 dB PSNR gains and 26.078% bitrate savings in Random Access configuration. Particularly, it achieves up to 65.544% bit rate savings and 3.901 dB PSNR increments for test sequence I09 in Low Delay P configuration. Furthermore, ablation test demonstrates that Gaussian representation contributes more on coding performance than Wiener representation and GWR-HPS.

This paper investigates a new medium access control (MAC) protocol for multi-channel heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to as multi-channel deep-reinforcement learning multiple access (MC-DLMA). Specifically, we consider a HetNet where different radio networks adopt different MAC protocols to transmit data packets to a common access point on different wireless channels. Three key challenges for the MC-DLMA node are (i) no environmental knowledge is known in advance; (ii) the channels in HetNets are allocated to nodes using different MAC protocols; (iii) the capacities of different channels may be different. The main goal of MC-DLMA is to find an optimal access policy to transmit on those pre-allocated channels and expedite more efficient spectrum utilization. Due to the complex temporal correlation of spectrum states in HetNets, the traditional DRL technique, e.g., original deep Q-network (DQN) algorithm, is no longer applicable to our problem. In our MC-DLMA design, an advanced class of recurrent neural network, termed as Gated Recurrent Unit (GRU), is embedded into the original DQN technique to aggregate observations over time and reason the underlying temporal feature in multi-channel HetNets. Furthermore, we analytically give the optimal spectrum access patterns and derive the optimal throughputs in various HetNet scenarios. With judicious definitions of the state, action, and reward function in the parlance of the DRL framework, simulation results show that MC-DLMA can (i) find the optimal spectrum access strategies in various HetNets, (ii) outperform the random access policy, the whittle index policy, and the original DQN, (iii) perform cooperative transmission in a fully distributed manner in the presence of multiple agents, and (iv) adapt well to the environmental changes.

Age of Information (AoI) has turned to be an outright metric to evaluate information freshness in Internet-of-Things (IoT) networks. In this work, we evaluate the average AoI of an uplink IoT monitoring network where multiple end-devices have independent status updates to transmit to a common Access Point (AP). More specifically, we propose the so-called successive interference cancellation (SIC)-aided age-independent random access (AIRA-SIC) scheme, where the AP performs SIC aiming at recovering collisions of multiple packets that are transmitted simultaneously by different devices in a slotted ALOHA fashion. Our results show that the proposed scheme not only achieves considerable lower AoI levels than the standard AIRA, but also outperforms a recently proposed threshold-based age-dependent random access (ADRA) scheme, where the channel access probability of each device is dynamically adapted based on each devices’ AoI. Finally, we provide some insights on the optimal channel access probability that minimizes the network average AoI, as well on the influence of imperfect channel state information (CSI) in the performance of the proposed scheme.

This article evaluates the impact of backoff time through the Backoff Indicator’s (BI) configuration on the random-access channel (RACH) under different massive traffic scenarios considering network performance metrics such as the probability of successful access, the delay in access, and the average number of preamble transmissions. A discrete-event simulation model of the contention-based random-access procedure was developed using MATLAB software. Based on the results, an optimal range of BI values was characterized for each scenario through different reliability conditions. It was observed that with a suitable configuration of the RACH parameters, the performance of the system network could reach a successful access probability more significant than 85% with a moderate increase in the access delay. It was concluded that dynamic modification of the backoff time could alleviate channel congestion in delay-tolerant applications with massive traffi

Geometric partitioning has attracted increasing attention by its remarkable motion field description capability in the hybrid video coding framework. However, the existing geometric partitioning (GEO) scheme in Versatile Video Coding (VVC) causes a non-negligible burden for signaling the side information. Consequently, the coding efficiency is limited. In view of this, we propose a spatio-temporal correlation guided geometric partitioning (STGEO) scheme to efficiently describe the object information in the motion field of video coding. The proposed method can economize the bits consumed for side information signaling, including the partitioning mode and motion information. We firstly analyze the characteristics of partitioning mode decision and motion vector selection in a statistically-sound way. Based on the observed spatio-temporal correlation, we design a mode prediction and coding method to reduce the overhead for representing the above mentioned side information. The main idea is to predict the STGEO modes and motion candidates that have higher selection possibilities, which can guide the entropy coding, i.e., representing the predicted high-probability modes and motion candidates with fewer bits. In particular, the high-probability STGEO modes are predicted based on the edge information and history modes of adjacent STGEO-coded blocks. The corresponding motion information is represented by the index in a merge candidate list, which is adaptively inferred based on the off-line trained merge candidate selection probability. Simulation results show that the proposed approach achieves 0.95% and 1.98% bit-rate savings on average compared to VTM-8.0 without GEO for Random Access and Low-Delay B configurations, respectively.

Racetrack memory, an emerging low-power magnetic memory, promises a competitive replacement for traditional memory in the accelerators. However, random access in racetrack memory is time and energy expenditure for CNN accelerators because of its large amount of invalid-shifts. In this work, we propose an automatic-addressing architecture that builds a novel data layout to guarantee that the next round of memory access can be always satisfied at the in-situ or rigorously adjacent cells of current round, producing a fully serialized access footprint that can drive instant port-alignment without any invalid-shifts in racetrack memory. By this way, original address-based access degrades to the selections repeated among the three candidates, i.e., one in-situ cell and two neighbor cells. Based on this simplification, a lightweight access management can generate the sequence of one-out-three selections according to the deterministic access behaviors defined by CNN hyper-parameters. The evaluation shows that, when deploying the five popular CNN applications to our architecture, the physical shifts of racetrack is curtailed by 74.64% over legacy layout, which achieves 54.2% and 42.1% energy reduction on read and write respectively. A case study of YOLOv2 indicates that our architecture performs 6.503 GOp/J that achieves 18.5× improvement to server-level GPUs.

This letter analyses both static and dynamic resource allocation methodologies of the Multi-User Orthogonal Frequency Division Multiple Access (MU-OFDMA) feature of the Wi-Fi 6 network. In contrast to static MU-OFDMA, in the case of dynamic MU-OFDMA system, resource units (RUs) are allocated dynamically between the schedule and random access (RA) RUs based on the heterogeneous traffic requirements of the stations (STAs). In this letter, we first analyze the static MU-OFDMA system using the Fluid limit approach and derive the transmission probability of a STA in a time slot denoted as τ as a function of the number of RA RUs (NRA). Later the dynamic MU-OFDMA system is modeled as a Discrete-Time Markov Chain (DTMC) wherein the NRA changes with every time slot. The DTMC is solved by using the parameter τ as a function of NRA that is derived previously using the Fluid limit approach. The trade-off between throughput and RA capability (rate of new users accessing the system) of a dynamic system is studied and validated against simulation.

Machine-type communications (MTC) should account for half the connections to the internet by 2030. The use case massive MTC (mMTC) allows for applications to connect a massive number of low-power and low-complexity devices, leading to challenges in resource allocation. Not only that, mMTC networks suffer under rigid random access schemes due to mMTC ultra-dense nature resulting in poor performance. In this sense, this paper proposes a Q -Learning-based random access method for massive machine-type communications, with device clustering and non-orthogonal multiple access (NOMA). The traditional NOMA implementation increases spectral efficiency, but at the same time, demands a larger Q -Table, thus slowing down convergence, which is known to be a highly detrimental effect on massive networks. We use pre-clustering through short-range device-to-device technology to mitigate this drawback, allowing devices to operate with a smaller Q -Table. Furthermore, the previous selection of partner devices allows us to implement a full-feedback-based reward mechanism so that clusters avoid time slots already successfully allocated. Additionally, to cope with the negative impact of system overload, we propose an adaptive frame size algorithm to run in the base station (BS). It allows adjusting the frame size to the network load, preventing idle slots in an underloaded scenario, and providing extra slots when the network is overloaded. The results show the great benefits in terms of throughput of the proposed method. In addition, the impact of the use of clustering and the size of the clusters, as well as the frame size adaptation, are analyzed.

In this brief, we present FracTCAM, an efficient methodology for ternary content addressable memory (TCAM) emulation on Xilinx field-programmable gate arrays (FPGAs) by leveraging primitive architectural resources. The proposed methodology exploits the fracturable nature of lookup table random access memories (LUTRAMs) and built-in slice flip-flops for deeper pipelining. Multiple slices can be combined together to build deeper and wider TCAMs using ANDing operations. This results in TCAM implementations that achieve lower resources utilization, lower delay, and power consumption. A comparison with the existing schemes shows that FracTCAM consistently achieves the best performance per area (PA) and performance per area per watt (PAW).

Постановка задачи: Наличие деструктивных воздействий на канальном уровне сети цифровой радиосвязи актуализирует вопросы оценки эффективности его функционирования. Известные модели синхронизации, случайного множественного доступа к среде, зарезервированного доступа к среде и управления мощностью передатчика элемента сети цифровой радиосвязи являются частными моделями функционирования канального уровня такой сети и не позволяют интегрально оценить негативный вклад деструктивных воздействий. Целью работы является оценка эффективности функционирования канального уровня сети цифровой радиосвязи при ее эксплуатации в условиях деструктивных воздействий. Используемые методы: Решение задачи оценки эффективности функционирования канального уровня сети цифровой радиосвязи основано на использовании цепи Маркова теории массового обслуживания, в которой состояния определяют уровни обслуживания элемента такой сети, а переходы – совокупность независимых событий, определяющих возможности синхронизации ведомых элементов с ведущим элементом сети цифровой радиосвязи, их энергетической доступности и успешной передачи кадров данных. Новизна: Для достижения поставленной цели разработана аналитическая модель канального уровня сети цифровой радиосвязи, а также с использованием полученной модели – методика оценки эффективности функционирования канального уровня такой сети. Предлагается при оценке эффективности функционирования канального уровня сети цифровой радиосвязи учесть взаимоувязанные процессы синхронизации, случайного множественного доступа к среде, зарезервированного доступа к среде и управления мощностью передатчика элемента сети цифровой радиосвязи, протекающие в условиях деструктивных воздействий. Результат: Проведенное моделирование показало, что в сети цифровой радиосвязи установление и проведение сеанса связи даже при условии отсутствия деструктивного воздействия затрудненно, незначительное такое воздействие способно вывести из строя целую сеть. При этом резкое снижение эффективности функционирования канального уровня свидетельствовало о наличии деструктивного воздействия в сети цифровой радиосвязи. Практическая значимость: модель применима при проектировании сетей цифровой радиосвязи, при их оптимизации в ходе эксплуатации и при обнаружении деструктивных воздействий. Программную реализацию методики предлагается включить в состав существующего программного комплекса диагностирования сетей цифровой радиосвязи., Problem statement. The presence of destructive impacts at the digital radio network data link layer actualize the issues of evaluating its functioning effectiveness. The well-known synchronization, random multiple access to the medium, reserved access to the medium and power control of the transmitter subscriber terminal models are private models of the functioning data link layer digital radio networks and do not allow an integrated assessment at the destructive impact contribution. The goal of the paper is evaluation of the effectiveness of the data link layer of digital radio networks in their operation under conditions of destructive impacts. Methods. The data link layer digital radio network assessing functioning effectiveness problem solution is based on the queuing theory Markov chain using. Markov chain states determine subscriber terminal service levels. Markov chain transitions determine the totality of independent events that determine the ability to synchronize such terminals with a means of switching and control, their energy availability and successful transmission of data packets. Novelty. To achieve this goal, an analytical model of the data link layer of digital radio networks has been developed, as well as a method for evaluating the effectiveness of the data link layer of such networks using the resulting model. In assessing the data link layer digital radio network functioning effectiveness is proposed to take into account the interconnected processes of synchronization, random multiple access to the medium, reserved access to the medium and power control of the transmitter of the subscriber terminal, occurring under the destructive impact conditions. Results. The simulation showed that in digital radio networks the establishment and conduct of a communication session is difficult even if there is no destructive impact. Insignificant destructive impact can disable the whole network. At the same time a sharp decrease in the data link layer operational efficiency indicated the presence of a destructive impact in the digital radio network. Practical relevance. The model is applicable in the design of digital radio networks, during their optimization during operation and in the destructive impact detection. The software implementation of the method is proposed to be included in the existing software package for diagnosing digital radio networks.

To provide massive connectivity in massive machine type communications (mMTC) for the Internet of Things (IoT) system, a novel grant free random access protocol, called multi-slot pilot allocation (MSPA) is proposed in this paper, where the user equipments (UEs) are permitted to jointly transmit randomly chosen pilot sequences along with their data packets over multi-slot to resolve intra-cell pilot collision. In addition, by utilizing the belief propagation tool for the MSPA protocol, the closed-form expressions to the access failure probability and system throughput in a finite length regime are derived, which are highly desired for practical-interest mMTC network. Further, a guideline for certain mMTC scenarios that target on urgent serving requirement UEs is also proposed to minimize the access latency and maximize the system throughput under diverse access failure probability constraints. Finally, the parametrical analysis for MSPA protocol is given by theoretical proof and simulation verification, which shed light on the advantages of our MSPA protocol over the existing protocols in terms of achieving high throughput and shortening the access latency.

We introduce Titchy, which is a compression method for time-series data generated by the Internet of Things. Our proposed method is flexible and has several advantages when applied in the IoT ecosystem: 1) it is able to compress even when only a small amount of memory can be allocated to it; 2) it compresses data in tiny chunks, so it introduces very little latency during online operation and thus, enables frequent and timely updates with compressed data; and 3) it facilitates efficient data storage and retrieval by enabling low-cost random access to the compressed data, eliminating the need to decompress large chunks when only a small amount of data is requested. To evaluate each of these advantages, we have implemented the compressor and conducted extensive experiments with long-term real-world data captured over days or weeks. We also present results for seven state-of-the-art compression methods to act as a baseline. Our evaluation shows that Titchy not only outperforms all seven on random access capability and for frequent transmissions but also provides great compression ratios as well as high compression and decompression speeds.

Internet of things (IoT) has attracted considerable attention in recent years due to its potential of interconnecting a large number of heterogeneous wireless devices. However, it is usually challenging to provide reliable and efficient random access control when massive IoT devices are trying to access the network simultaneously. In this paper, we investigate methods to introduce intelligent random access management for a massive cellular IoT network to reduce access latency and access failures. Towards this end, we introduce two novel frameworks, namely local device selection (LDS) and intelligent preamble selection (IPS). LDS enables local communication between neighboring devices to provide cluster-wide cooperative congestion control, which leads to a better distribution of the access intensity under bursty traffics. Taking advantage of the capability of reinforcement learning in developing cooperative multi-agent policies, IPS is introduced to enable the optimization of the preamble selection policy in each IoT clusters. To handle the exponentially growing action space in IPS, we design a novel reinforcement learning structure, named branching actor-critic, to ensure that the output size of the underlying neural networks only grows linearly with the number of action dimensions. Simulation results indicate that the introduced mechanism achieves much lower access delays with fewer access failures in various realistic scenarios of interests.

Background Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality. Results This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads. Conclusion The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

IEEE 802.11ax partitions a regular 20MHz channel into smaller sub-channels called resource units to support simultaneous multiuser operation using orthogonal frequency division multiple access (OFDMA). Uplink OFDMA random access (UORA) in IEEE 802.11ax allows stations to transmit via a scheduled random access mechanism. UORA is initiated via a trigger frame which aside from serving as a synchronization mechanism, also informs stations which resource units are allowed for random access. Using the trigger frame information, the stations engage in an OFDMA backoff process to win access to a resource unit. Similar to slotted ALOHA, the maximum normalized throughput of UORA is only 37 percent due to high probability of collisions at high loads. To reduce collisions, we equip UORA with carrier sensing capability resulting in a new uplink hybrid UORA (H-UORA) OFDMA access mechanism. Unlike other multi-carrier CSMA methods previously proposed in literature, H-UORA is an easily implementable modification to current 802.11ax WLANs. We show that H-UORA can achieve a normalized throughput of at least 80 percent (which increases further depending on the buffering capabilities of the access point) using various numerical analysis and simulations.

Coded Slotted ALOHA (CSA) is a random access scheme based on the application of packet erasure correcting codes to transmitted packets and the use of successive interference cancellation at the receiver. CSA has been widely studied and a common assumption is that interference cancellation can always be applied perfectly. In this paper, we study the design of CSA protocol, accounting for a non-zero probability of error due to imperfect interference cancellation (IC). A classical method to evaluate the performance of such protocols is density evolution, originating from coding theory, and that we adapt to our assumptions. Analyzing the convergence of density evolution in asymptotic conditions, we derive the optimal parameters of CSA, i.e., the set of code selection probabilities of users that maximizes the channel load. A new parameter is introduced to model the packet loss rate of the system, which is non-zero due to potential IC errors. Multi-packet reception (MPR) and the performance of 2-MPR are also studied. We investigate the trade-off between optimal load and packet loss rate, which sheds light on new optimal distributions that outperform known ones. Finally, we show that our asymptotic analytical results are consistent with simulations obtained on a finite number of slots.

In this work, we introduce the pattern-domain pilot design paradigm based on a "superposition of orthogonal-building-blocks" with significantly larger contention space to enhance the massive machine-type communications (mMTC) random access (RA) performance in massive multiple-input multiple-output (MIMO) systems.Specifically, the pattern-domain pilot is constructed based on the superposition of $L$ cyclically-shifted Zadoff-Chu (ZC) sequences. The pattern-domain pilots exhibit zero correlation values between non-colliding patterns from the same root and low correlation values between patterns from different roots. The increased contention space, i.e., from N to $\binom{N}{L}$, where $\binom{N}{L}$ denotes the number of all L-combinations of a set N, and low correlation valueslead to a significantly lower pilot collision probability without compromising excessively on channel estimation performance for mMTC RA in massive MIMO systems.We present the framework and analysis of the RA success probability of the pattern-domain based scheme with massive MIMO systems.Numerical results demonstrate that the proposed pattern division random access (PDRA) scheme achieves an appreciable performance gain over the conventional one,while preserving the existing physical layer virtually unchanged. The extension of the "superposition of orthogonal-building-blocks" scheme to "superposition of quasi-orthogonal-building-blocks" is straightforward.

Identification and authentication are two essential features for traditional random access protocols. In ALOHA-based random access, the packets usually include a field with a unique user address. However, when the number of users is massive and relatively small packets are transmitted, the overhead of including such field becomes restrictive. In unsourced random access (U-RA), the packets do not include any address field for the user, which maximizes the number of useful bits that are transmitted. However, by definition an U-RA protocol does not provide user identification. This letter presents a scheme that builds upon an underlying U-RA protocol and solves the problem of user identification and authentication. In our scheme, the users generate a message authentication code (MAC) that provides these functionalities without violating the main principle of unsourced random access: the selection of codewords from a common codebook is i.i.d. among all users.

In this article, a cybertwin-assisted nonorthogonal random access (RA) system is presented, where the cybertwins of the physical devices at the access point (AP) collect the devices’ information and decide the transmission parameters on behalf of the devices to achieve the maximum system performance. Specifically, the system performance of a $p$ -persistent slotted CSMA system with nonorthogonal multiple access (NOMA) is analyzed, in which wireless devices transmit data to the ensure the received signal strength at the AP side is either high power or low power with certain probabilities. We first develop an analytical framework to quantify the successful transmission probability and the sum data rate as a function of the above probabilities. Accordingly, the feasible region of the number of high-power and low-power devices to ensure successful transmission is derived. With the analysis, nonconvex optimization problems are then formulated to maximize successful transmission probability and the sum data rate, respectively. To tackle the nonconvexity, an effective and fast-convergent iterative algorithm is designed to obtain the optimal transmission probabilities for the devices. Extensive simulations are conducted to validate our analytical results and demonstrate the benefits of NOMA in RA networks.

In this article, we propose the Raptor coded random access (RCRA) scheme to enable reliable transmission in the decentralized unmanned aerial vehicle (UAV) network. The considered network is composed of several overlapped random access systems with interference nodes, and the proposed RCRA scheme reduces bit-error ratio (BER) of the random access systems by three steps. First, we choose the number of slots based on a derived lower bound, which is necessary for reliable random access. Second, error-correcting codes are incorporated as the precode before random access, and then the access probability is optimized to achieve the minimum BER. Third, by correlating two consecutive slots, an idle-slot-filling approach is designed to further improve the efficiency of the random access systems. Numerical results show that the proposed RCRA scheme reduces significantly both block-error ratio (BLER) and BER at moderate- and high-signal-to-noise ratio (SNR) region. With $E_{s}/N_{0}$ equal to 0 dB, the RCRA scheme saves 20% slots, compared with the existing frameless ALOHA scheme, to achieve a target BER of 10−4.

Random access (RA) or preamble collision is one of the crucial problems in massive internet-of-things (IoT) at the network entry stage. Since a massive number of IoT nodes simultaneously attempt RAs on the same physical random access channel (PRACH), preambles may be selected by multiple nodes, incurring preamble collisions at the first step of the RA procedure. However, conventional RA models are limited to binary preamble detections which poses severe RA performance loss in the massive IoT environment. In this paper, we propose a deep learning (DL)-based end-to-end RA framework which has detection and resolution abilities for the collided preambles. In particular, advanced preamble classification and timing advance (TA) classifications are performed using deep neural networks (DNNs) for improving the probability of RA success while reducing the delay of the entire RA procedure. The effectiveness of the proposed DNN-based preamble and TA classifiers are demonstrated through extensive simulations. We further evaluate the system-level performance of the proposed DL-based RA model. It shows a significantly higher probability of instant RA success, which makes every node succeed in RA with very limited reattempts, and also maintains a significantly lower RA delay in massive IoT environment.

Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large., Comment: 12 pages, 9 figures. Published in IEEE Transactions on Communications, Vol. 69, No. 11, pp. 7520 - 7530, November 2021

In this article, a Co-existing Preamble and Data (CoPD) random access approach for machine-type communications (MTC) is proposed and studied in massive multiple input multiple output (MIMO) systems. Unlike conventional grant-free random access in which preambles are time-multiplexed with data, the proposed approach enables CoPD transmissions by different groups of devices, which leverages favourable propagation of massive MIMO and successive interference cancellation (SIC) technique. To fully understand the performance behavior of the proposed approach, the error characteristics of channel estimation (CE) due to SIC error propagation are investigated. To avoid the CE error variance growing arbitrarily with time that leads to data transmission failure, key conditions, with respect to preamble length $L$ , traffic intensity $\lambda $ , and average number of data packets per active device $\bar b$ , are derived. Under the conditions, we demonstrate that the throughput of the proposed CoPD approach cannot be arbitrarily high by increasing $\bar b$ and $\lambda $ even though the antenna array size, denoted by $M$ , grows arbitrarily in massive MIMO. Accordingly, its maximum throughput and success probability are theoretically obtained. Simulation results verify our analysis results and confirm that the maximum throughput of the proposed CoPD approach can be at least 4 times than that of the conventional one. Furthermore, its success probability approaches 1 when $M \to \infty $ as long as the derived conditions are met.

This paper presents finite-blocklength achievability bounds for the Gaussian multiple access channel (MAC) and random access channel (RAC) under average-error and maximal-power constraints. Using random codewords uniformly distributed on a sphere and a maximum likelihood decoder, the derived MAC bound on each transmitter’s rate matches the MolavianJazi-Laneman bound (2015) in its first- and second-order terms, improving the remaining terms to $\frac {1}2\frac {\log {n}}{n}+{O} \left ({\frac {1}{n}}\right)$ bits per channel use. The result $\vphantom {\sum ^{R}}$ then extends to a RAC model in which neither the encoders nor the decoder knows which of ${K}$ possible transmitters are active. In the proposed rateless coding strategy, decoding occurs at a time ${n}_{t}$ that depends on the decoder’s estimate ${t}$ of the number of active transmitters ${k}$ . Single-bit feedback from the decoder to all encoders at each potential decoding time ${n}_{i}$ , ${i} \leq {t}$ , informs the encoders when to stop transmitting. For this RAC model, the proposed code achieves the same first-, second-, and third-order performance as the best known result for the Gaussian MAC in operation.

With impressive density and coding capacity, DNA offers a promising solution for building long-lasting data archival storage systems. In recent implementations, data retrieval such as random access typically relies on a large library of non-interacting PCR primers. While several algorithms automate the primer design process, the capacity and scalability of DNA-based storage systems are still fundamentally limited by the availability of experimentally validated orthogonal primers. In this work, we combine the nested and semi-nested PCR techniques to virtually enforce multidimensional data organization in large DNA storage systems. The strategy effectively pushes the limit of DNA storage capacity and reduces the number of primers needed for efficient random access from very large address space. Specifically, our design requires k * n unique primers to index nk data entries, where k specifies the number of dimensions and n indicates the number of data entries stored in each dimension. We strategically leverage forward/reverse primer pairs from the same or different address layers to virtually specify and maintain data retrievals in the form of rows, columns, tables, and blocks with respect to the original storage pool. This architecture enables various random-access patterns that could be tailored to preserve the underlying data structures and relations (e.g., files and folders) within the storage content. With just one or two rounds of PCR, specific data subsets or individual datum from the large multidimensional storage can be selectively enriched for simple extraction by gel electrophoresis or readout via sequencing.Abstract Figure