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We analyze a remote sensing system in the Internet of things, where uncoordinated nodes send status updates to a common receiver to achieve information freshness, quantified through age of information. We consider a finite horizon scheduling over a random multiple access channel, where colliding messages are lost. We show that nodes must adopt a further randomization to deviate from identical schedules and escape collision deadlocks. Moreover, we discuss the impact of feedback availability if, due to, e.g., energy expenditure, it decreases the number of transmission opportunities.

Lossless image compression techniques shrink the image size to improve the transmission efficiency and reduce the occupied storage space while ensuring the quality of the image is lossless. Among them, the LOCO‐I/JPEG‐LS algorithm benefits high lossless compression ratio and low computational complexity and thus is widely used for various real‐time applications. However, due to the problems of the context dependency in the LOCO‐I, the parallelism in the algorithm is greatly constrained, which significantly limits the throughput and the real‐time performance of hardware implementations. Existing designs achieve more parallelism by using a lot of hardware costs or straightforward chunking with losing compression ratio. In order to trade off the parallelism and the compression ratio, this paper proposes a chunk‐oriented error modeling scheme for LOCO‐I, which enables parallelism in both compression and decompression and achieves a better compression ratio in chunks. Based on the optimized algorithm, a high‐throughput flexible lossless compression and decompression architecture (HFCD) is proposed, which achieves higher pixel per clock (PPC) with less hardware cost. Additionally, HFCD introduces a parameter sharing mechanism to enable random access of image chunks to improve the flexibility for decompression. Experimental results show that, compared with state‐of‐the‐art works, HFCD achieves 3.02–13.50 times improvement for the PPC of compression. For decompression, benefiting from our optimizations, HFCD achieves 22.4 times speedup compared to the software solution. [ABSTRACT FROM AUTHOR]

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage. [ABSTRACT FROM AUTHOR]

We calculate the mean throughput, number of collisions, successes, and idle slots for random tree algorithms with successive interference cancellation. Except for the case of the throughput for the binary tree, all the results are new. We furthermore disprove the claim that only the binary tree maximizes throughput. Our method works with many observables and can be used as a blueprint for further analysis. [ABSTRACT FROM AUTHOR]

Digital information encoded in polymers has been exclusively decoded by mass spectrometry. However, the size limit of analytes in mass spectrometry restricts the storage capacity per chain. In addition, sequential decoding hinders random access to the bits of interest without full‐chain sequencing. Here we report the shotgun sequencing of a 512‐mer sequence‐defined polymer whose molecular weight (57.3 kDa) far exceeds the analytical limit of mass spectrometry. A 4‐bit fragmentation code was implemented at aperiodic positions during the synthetic encoding of 512‐bit information without affecting storage capacity per chain. Upon activating the fragmentation code, the polymer chain splits into 18 oligomers, which could be individually decoded by tandem‐mass sequencing. These sequences were computationally reconstructed into a full sequence using an error‐detection method. The proposed sequencing method eliminates the storage limit of a single polymer chain and allows random access to the bits of interest without full‐chain sequencing. [ABSTRACT FROM AUTHOR]

With the increasingly dense deployment of base stations in the internet of things (IoT), the importance of interference management becomes ever more pronounced. In IoT environments, devices often employ random access, connecting to channels in a distributed manner. In scenarios involving massive numbers of devices, severe interference may arise between nodes, leading to significant degradation in the throughput performance of the network. To address interference control issues in networks with random access, a multi-base station slotted Aloha network based on cooperative reception was considered, the reinforcement learning techniques was leveraged to design adaptive transmission algorithms that effectively managed interference, optimized network throughput performance, and enhanced network fairness. Firstly, an adaptive transmission algorithm were devised based on Q-learning, which was verified to maintain high network throughput performance under varying traffic conditions through simulation. Secondly, to improve network fairness, the penalty function method was employed to refine the adaptive transmission algorithm. Simulations confirm that the fairness-optimized algorithm significantly enhances network fairness while preserving satisfactory network throughput performance.

Abstract FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing—which takes parameters that let us tune the average length of the phrases—instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for $$\texttt {PFP-FM}$$ PFP - FM is available at https://github.com/AaronHong1024/afm .

With the increasing demand for data exchange between nearby devices in proximity-based services, enhancing the security of wireless mutual broadcast (WMB) networks is crucial. However, WMB networks are inherently vulnerable to eavesdropping due to the open broadcast nature of their communication. This paper investigates the improvement of secrecy performance in random-access-based WMB (RA-WMB) networks by integrating physical layer security (PLS) techniques with hybrid duplex (HBD) operations under a stochastic geometry framework. The HBD method balances half-duplex (HD) receiving and full-duplex (FD) transceiving, utilizing self-interference cancellation (SIC) to enhance PLS performance. Key operational parameters, including transmission probability (TxPr), friendly jammer density, and conditions for FD operation, are designed to maximize secrecy performance. The analytical and numerical results demonstrate significant improvements in PLS performance, with SIC playing a critical role, particularly in scenarios with dense legitimate nodes, and with TxPr adjusted to balance HD receiving and FD transceiving based on SIC imperfections. The proposed design principles provide a comprehensive framework for enhancing the security of WMB networks, addressing the complex interplay of interference and SIC in various network configurations. [ABSTRACT FROM AUTHOR]

It is challenging to enable fast channel switching and packet-loss repair in low-delay live video distribution without negatively influencing the steady-state viewing performance. For example, regularly breaking the inter-frame dependency by introducing intra-predicted keyframes enables random access, but is costly in terms of rate-distortion performance. For this reason, the keyframe-injection method minimizes the impact by sending a compression-efficient normal video stream to all end-users. As accompaniment, a companion stream that solely consists of keyframes is sporadically used for only those users that switch channels or experience packet loss. This paper describes the requirements to implement keyframe injection in three video coding standard generations (H.264/AVC, H.265/HEVC, and H.266/VVC). We evaluated the impact that keyframe injection has on the quality of the video in terms of a decrease in VMAF, PSNR and SSIM. We demonstrate that the quality reduction caused by keyframe insertion is generally low, meaning that keyframe injection typically is imperceptible. However, drift-error artifacts become perceptible over time for rare outliers. Moreover, we pinpointed the cause of this worst-case artifact type to be halfpel interpolation. As a solution, codecs can disable subpel motion estimation, and future standards could design their filters more carefully. Lastly, it should be noted that keyframe injection will only be applied sporadically when users require a random access or experience packet loss, and only to those users. Most interestingly, all other users receive a compression-efficient stream wherein the inter-frame dependency is not artificially broken at regular short intervals. As such, our proposed solution makes low-latency video distribution efficient and viable in multiple coding standards. [ABSTRACT FROM AUTHOR]

FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing—which takes parameters that let us tune the average length of the phrases—instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm. [ABSTRACT FROM AUTHOR]

To address the issue of severe network congestion in the scenario of massive machine-type communications (mMTC), where machine-type communication devices (MTCD) employing traditional random access schemes often encountered challenges leading to a large number of MTCD failing to access the network successfully, a novel approach called preamble retransmission-based dynamic access class barring (PRT-ACB) was proposed.By utilizing the number of MTCD preamble retransmissions, the MTCD attempting to initiate access in each random access opportunity (RAO) were categorized into different sets of high and low priority.In conjunction with an estimation model for the number of payloads in each RAO, high and low-priority limiting factors and available preamble pools were dynamically adjusted based on the access load in each RAO.This allowed more MTCD to successfully access the network before reaching the maximum number of preamble transfers.Simulation results have demonstrated that the proposed scheme effectively enhances the MTCD successful access rate and reduces the time delay required for MTCD to access the network.The proposed scheme can serve as a solution to alleviate the congestion caused by a massive influx of communication devices attempting simultaneous network access.

Rapid advancements in Internet of Things (IoT) technology have promoted numerous novel applications that are sensitive to information timeliness. With different applications coexisting in one wireless network, each of which has heterogeneous traffic characteristics, it is of paramount importance while challenging to address how to characterize and optimize the information freshness performance network-wide, particularly in the massive access scenario. This paper casts attention on the heterogeneous slotted Aloha network and uses the Peak Age of Information (PAoI) metric to quantify information freshness. By assuming that the sensors in each group are equipped with unit-sized buffers and Bernoulli packet arrival, we derive the network steady-state point and PAoI. Depending on whether one group or all groups are age-sensitive, we focus on single-group PAoI and global PAoI optimization respectively. When only one single group is age-sensitive, we derive its optimal transmission probability. When all groups are age-sensitive, we propose a heuristic algorithm based on the particle swarm optimization method, where the bi-stability of Aloha network is considered for avoiding the risk of rapid performance deterioration. Extensive simulation results are presented to verify our analysis and the effectiveness of the proposed algorithm.

Narrowband Internet of Things (NB-IoT) is one of the most promising technologies for enabling reliable communication among low-power, and low cost devices present in massive machine-type communications (mMTC). In NB-IoT, random access (RA) is implemented in the medium access control (MAC) layer to resolve access contention among massive IoT devices. Efficient network access techniques are required to effectively solve the massive access issues in NB-IoT, guaranteeing increased throughput and high spectrum utilization. In this paper, we present a comprehensive overview of NB-IoT towards supporting mMTC, with focus on the NB-IoT coexistence with 5G, as well the design challenges and requirements of RA in NB-IoT. Moreover, available literature is reviewed to highlight the RA congestion control schemes proposed during the past few years to alleviate RA collisions. While existing RA approaches mainly focus on conventional contention-based techniques for performing RA, intelligent learning based and grant-free Non-Orthogonal Multiple Access (NOMA) have been identified as a potential candidates to increase the transmission efficiency of mMTC applications.

The swift progress of 6G cellular networks responds to the urgent demand for seamlessly integrating Internet of Things (IoT) devices on a large scale. Among various emerging technologies, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a standout, offering distinct advantages over traditional networks in accommodating extensive connectivity needs. GF-NOMA optimizes resource usage by reallocating each time-frequency slot to multiple devices with different power levels. Furthermore, it reduces the coordination burden typically associated with uplink communication by broadcasting random access channels. Nonetheless, GF-NOMA faces a significant challenge: the risk of multiple devices inadvertently selecting the same resource and power, resulting in data loss, particularly problematic during emergencies marked by uncoordinated communications. Additionally, the expanding deployment of IoT devices demands a proportional increase in resources, despite advancements in network technology. To address these challenges, this paper introduces an innovative architecture aimed at significantly boosting spatial capacity through the establishment of autonomous social interactions among IoT devices. The proposed SAFE-GF-NOMA aggregation scheme facilitates resource sharing in an independent and ad-hoc trust management environment by ensuring trustworthy sharing. The proposed Social IoT (SIoT) framework reduces uplink access by grouping devices based on trust metrics, resulting in a notable 50% reduction in collision probability, and over a 50% increase in success probability, and a threefold capacity increase compared to conventional systems. Additionally, our system achieves a substantial reduction in energy consumption, cutting it from 17 J to just 5 J per device within the cluster.

The requirement of the upcoming sixth-generation (6G) wireless communication systems to significantly elevate the services of enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC) necessitates the design and investigation of appropriate multiple access schemes. This paper investigates the impact of random source deployment on the performance of uplink systems, emphasizing the implications of non-orthogonality in contention-free and contention-based access schemes. For the latter scheme, we combine the strengths of slotted ALOHA and successive interference cancellation. Considering the advantages of breaking orthogonality in scenarios with random source deployment, we propose distinct policies tailored for mMTC, eMBB, and hybrid mMTC-eMBB scenarios by splitting the cell into rings. Furthermore, we derive closed-form expressions for the outage probability, which play a pivotal role in extracting the throughput of the sources in the considered scenarios. The paper offers a comprehensive analysis of the proposed approach, corroborated through simulations, shedding light on the potential of such protocols in future 6G wireless communication systems.

The planning and deployment of mega constellations has posed increasing pressure on the existing telemetry, track and command (TTC) resource scheduling strategy. The current plan-based scheduling strategy distributes resources according to needs, however, it cannot address sudden resource requests with prompt responses. By analyzing the status quo of resource scheduling systems, we have proposed a distributed collaborative scheduling technology for random access in real-time, based on the theory of low costs and rapid transfers of scheduling task resources. First, we use knowledge graph technology to map the link between scheduling tasks and idle resources and develop a graph network. Furthermore, by analyzing and evaluating the resource transfer capability of a multi-target TTC system in hemispherical coverage (MT-TTC), we propose the concept of a virtual resource pool and develop a two-layer scheduling framework in which the central scheduling system coordinates with multiple regional systems. This scheduling strategy works well when a sudden TTC request occurs. The central system manages the satellite requests and make plans, while regional systems are responsible for handling the sudden requests and report to the central system on its resource change. With regional systems sharing some degree of autonomy, the central system making a final decision and systems across the framework coordinating in real-time, we design a model on resource transfers for MT-TTC equipment and a collaboration mechanism that coordinates the resource transfers across MT-TTC equipment and other single-beam equipment within and between regions. We do experiments in simulation scheduling environments. The results show that the regional scheduling system based on MT-TTC equipment is able to manage continuous random-access requests in real-time, and avoid resource conflicts across regions by collaborating with the central scheduling system. It is of value to be applied in real scenarios. [ABSTRACT FROM AUTHOR]

The multimedia Tactile Internet is a network that provides ultra-low latency and ultra-high reliability for two-way multimedia services. Since random access in uplink dominates the delay and there exist variations in target delays and traffic volumes among connections, it is much more efficient to perform a joint uplink and downlink resource allocation. In this paper, we provide uplink random access schemes with joint uplink and downlink resource allocation with multichannel architecture for multimedia Tactile Internet. By doing so, one can significantly increase the resource utilization efficiency and improve the reliability of the multimedia Tactile Internet as well. The cores of resource management are two-fold. First, the joint resource allocation dynamically adjusts the ratio of the number of uplink channels to the number of downlink channels and performs access control based on the collision ratios and the queue lengths. Machine learning techniques such as deep reinforcement learning can be applied to maximize the throughput. Secondly, the efficient uplink random access for multichannel architecture increases the throughput, where each transmitting node selects several time slot/channel pairs based on either theory of the Finite Projective Plane, the random numbers, or the hybrid. In the performance evaluation, we compare the performance of different selection schemes for the multichannel architecture in terms of throughput for the multimedia Tactile Internet through simulation runs and analysis. The hybrid selection scheme performs the best since it has the advantages of both the Finite Projective Plane structure and random numbers. Lastly, we compare and contrast the proposed joint uplink/downlink random access scheme with other known schemes. [ABSTRACT FROM AUTHOR]

In the process of the vigorous development of satellite internet of things, random access protocol is regarded as the most suitable communication protocol due to its characteristics of low signaling overhead, high transmission efficiency, high flexibility and easy implementation.However, due to drastic load changes, conventional random access mode only has advantages in the specific load range.Dynamic allocation of random access resources requires the accurate number of time slot access applications.In view of the above problems, a random access adaptive scheme was proposed, which could adaptively selected the appropriate random access mode according to the future load size, so as to improved the system throughput.The load size could be obtained through the proposed load estimation and prediction scheme.When narrowband interference exists in the transmission environment, the spread-spectrum communication means was introduced to made the adaptive scheme of random access run successfully.Moreover, the proposed adaptive scheme also improved the system throughput.

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage.

As IoT services become more active, the density of IoT devices is increasing, and massive connectivity technology is needed to support numerous devices simultaneously. In addition, IoT devices are often battery-powered, and during random access, it is necessary to reduce the power consumption to extend the lifetime of the devices. In particular, devices with poor channels need to send at a very low transmission rate through a large number of repetitions, and longer packet lengths can increase the probability of collisions, increasing the power consumption while shortening the lifetime of the IoT system. Dividing devices into groups based on the number of repetitions and allocating different resources to each group can reduce collisions for bad-channel devices, but it can be difficult to support large connections, due to the inefficient use of resources. This paper proposes schemes to reduce the collision probability of bad-channel devices while allowing IoT devices to use shared resources, instead of dividing resources by groups. There are two versions of the proposed schemes. The first method reduces collisions by delaying the response of a bad-channel device, and in the meantime, eliminating interference from other devices, assuming that the bad-channel device is not sensitive to delay. Instead of checking the response, and then, performing a random backoff when no acknowledgement packet is received, the second proposed method reverses the order of response checking and random backoff, that is, it first performs a random backoff, and then, checks the response to decide whether to retransmit. The proposed method can increase the lifetime of the IoT system by reducing the collision probability of a bad-channel device, without degrading the performance of other devices. [ABSTRACT FROM AUTHOR]

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Due to the significant propagation delay in underwater sensor networks, conflict retransmission in channel access protocols comes at a high cost. This poses a challenge in scenarios where multiple sensor nodes generate data frames with strong temporal correlations, such as in disaster warning applications. Traditional channel allocation and timeout-based retransmission mechanisms lead to considerable access delays, making it difficult to meet the requirements. To tackle this issue, we propose the asynchronous pattern-designed random access (APDRA) protocol. This protocol enhances the access probability by designing retransmission time intervals for data frames based on pattern design. Additionally, we introduce a successive interference cancellation (SIC) mechanism at the receiver for decoding. This mechanism facilitates the transformation of the conventional method of discarding conflicted data frames into iterative decoding, thereby enhancing transmission efficiency. Via the utilization of simulations, we compare the APDRA protocol conventional underwater medium access control (MAC) protocols and existing retransmission mechanisms. The results demonstrate that the APDRA protocol has the ability to improve both the transmission success ratio (TSR) and reduces the access delay to some extent. [ABSTRACT FROM AUTHOR]

Currently, network requirements are placed on the efficiency and size of the networks. These conditions can be ensured by modern converged networks that integrate the functions of both data and telecommunication networks. Line or router failures have always been a part of transmission networks, which is no different from converged networks. As a result of outages, which can take from ms to tens of seconds, packets are lost. These outages cause degraded transmission quality, which is undesirable when transmitting real-time multimedia services (Voice over IP, video). To solve the mentioned problems, the IETF organization has developed IP Fast Reroute mechanisms to minimise the time to restore the connection after a line or node failure and, consequently, less packet loss. The article reviews and compares the latest IP Fast Reroute mechanisms deployed in the last three years. First, we have Optimistic Fast Rerouting, which calculates optimistic and fallback scenarios. The second is Post-processing Fast Reroute, which decomposes the network according to metrics such as load and route length. Third, Local Fast Reroute focused on low congestion and random access.

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We consider compact representations of collections of similar strings that support random access queries. The collection of strings is given by a rooted tree where edges are labeled by an edit operation (inserting, deleting, or replacing a character) and a node represents the string obtained by applying the sequence of edit operations on the path from the root to the node. The goal is to compactly represent the entire collection while supporting fast random access to any part of a string in the collection. This problem captures natural scenarios such as representing the past history of an edited document or representing highly-repetitive collections. Given a tree with n nodes, we show how to represent the corresponding collection in O(n) space and O (log n / log log n) query time. This improves the previous time-space trade-offs for the problem. Additionally, we show a lower bound proving that the query time is optimal for any solution using near-linear space. To achieve our bounds for random access in persistent strings we show how to reduce the problem to the following natural geometric selection problem on line segments. Consider a set of horizontal line segments in the plane. Given parameters i and j, a segment selection query returns the j th smallest segment (the segment with the j th smallest y-coordinate) among the segments crossing the vertical line through x-coordinate i. The segment selection problem is to preprocess a set of horizontal line segments into a compact data structure that supports fast segment selection queries. We present a solution that uses O(n) space and support segment selection queries in O (log n / log log n) time, where n is the number of segments. Furthermore, we prove that that this query time is also optimal for any solution using near-linear space. [ABSTRACT FROM AUTHOR]

Thanks to its capability of providing seamless massive access and extended coverage, satellite communication has been envisioned as a promising complementary part of the future 6G network. Due to the large satellite-to-ground propagation loss, noise mitigation is one of the most important considerations for implementing key interface technologies on boarding a satellite, e.g., random access (RA). This paper aims at developing an effective preamble detection method with noise suppression for 5G-ntegrated satellite RA systems. Specifically, according to the satellite ephemeris and user equipment location, we first perform the pre-compensation of timing and frequency offset before preamble transmission to determine all the possible correlation peak positions in advance. By leveraging the advantage of the wavelet transform in signal-to-noise separation, we further design a novel detection framework based on wavelet denoising, which can efficiently reconstruct preamble signature from the noisy power delay profile. Simulation results validate the feasibility of the proposed method, and show that our method can achieve a notably improved detection performance under extremely low signal-to-noise ratio conditions, in comparison with the conventional one. [ABSTRACT FROM AUTHOR]

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We consider the random access problem in wireless networks such as cellular networks and massive machine type communication networks, where a large number of devices/users are connected to a base station. In order to establish communication with the base station, as a part of the random access procedure, active users select and transmit codes (pilot sequences) randomly from a codebook. Since the active users are typically a small fraction of the total users, sparse signal recovery algorithms are employed to detect the transmitted codes. In this paper, we use multiple measurement vector model for the received signal and develop a parallel greedy search algorithm and it's stopping criteria to detect the active users. We show through simulations that the proposed scheme, compared with the existing greedy algorithms, gives better detection performance at lower false alarms levels. We study how the code length and the code word size need to be chosen in order to detect a given number of active users. We also address the scenario when there are different timing offsets for signals received from different users. [ABSTRACT FROM AUTHOR]

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Abstract Background In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/ .

Low power wide area network (LPWAN) technologies have become an integral part of Internet-of-Things (IoT) applications due to their ability to meet key requirements such as long range, low cost, massive device numbers, and low energy consumption. Among all available LPWAN technologies, LoRaWAN has garnered significant interest from both industry and academia. Due to wide communication range of LoRaWAN, the coverage of gateway (GW) may overlap and the packet transmissions from nodes in the overlapping area would collide, which deteriorates the network performance. How to tune the backoff parameter settings to reduce collision and maximize the network throughput in multi-GW LoRaWAN is still an open issue. To solve this issue, in this paper, we first propose a low-complexity model for multi-GW LoRaWAN, which divides nodes into different groups based on GWs they can communicate with. Key performance metrics, i.e., the network throughput, access delay and the probability of successful transmission, are derived as functions of backoff parameter and input rate. As the throughput performance crucially depends on whether each group is saturated or not, we propose an iterative algorithm to analyze the stability of the multi-GW LoRaWAN, based on which we further develop an iterative algorithm to tune the backoff parameter of each group iteratively for the network throughput optimization. The simulation results validate the effectiveness of the proposed algorithms and reveal the trade-off between fairness and efficiency.

In 5th generation (5G) systems, two-step random access has been introduced for machine-type communication (MTC) to lower signaling overhead. It is also shown that when a base station (BS) is equipped with a large number of antennas, the notion of massive multiple-input multiple-output (MIMO) can be exploited to improve the performance in terms of throughput and spectral efficiency. In this paper, we consider the case that a BS is equipped with a small number of antennas, in which a sufficiently high spatial selectivity cannot be obtained, and propose an approach to two-step random access based on beam selection that can perform well with a small number of antennas. In the proposed approach, spreading sequences are also used in conjunction with beam selection to mitigate interference due to limited spatial selectivity. To analyze the performance of the proposed approach, the distribution of the signal-to-interference-plus-noise ratio (SINR) is derived as a closed-form expression and the throughput is found. We compare the throughput of the proposed approach with those of conventional two-step random access approaches through analysis and simulations. While the theoretical results agree with simulation results, we can see that the proposed approach outperforms conventional ones when the number of antennas is small for a wide range of traffic intensity.

In order to satisfy the higher demand raised by 6G, the major part of which refers to further extension of the coverage and achievements of ubiquitously massive connections, the integration of Non-Terrestrial Networks (NTNs) and the evolved version of Narrow Bandwidth Internet of Things (NB-IoT) are introduced as a potential solution. However, one of the main challenges still open is the Random Access (RA) procedure which refers to preamble detection and uplink synchronization. With the large Time of arrival (ToA) and Carrier frequency offset (CFO), traditional methods designed for Terrestrial Networks (TNs) can not provide accurate estimation performance. Some studies adopt Global Navigation Satellite System (GNSS) to pre-compensate the large ToA and CFO but it may not work well for NB-IoT devices in practice. Consequently, a complete system without GNSS is designed for RA in this paper. A system-level method is firstly applied to pre-compensate the CFO. Then we focus on tackling the ToA and residual CFO. A pre-process method for received signal is proposed and its phase series are analyzed. Then we propose a novel random access scheme based on change point detection (CPD) of phase series with machine learning to estimate ToA and CFO. Simulation results demonstrate that the designed overall system is practical in NTNs. Meanwhile, the proposed random access scheme achieves higher estimation accuracy of ToA and CFO compared with existing methods with the same range of ToA and CFO under low signal-noise ratio (SNR) conditions.

In this paper, we analyze the throughput and reliability of an indoor optical wireless communication (OWC)-based Internet of Things (IoT) system based on Slotted ALOHA (SA) where IoT devices exchange data with an access point (AP). Assuming that the OWC receiver at the AP exploits the capture effect, we derive the error probability of decoding a short-length data packet that originates from a randomly selected OWC IoT device in the presence of interfering users. The analysis is based on the derivation of the signal-to-interference-plus-noise-ratio (SINR) statistics and the application of the finite block-length (FBL) information theory. Using these analytical results, we derive relevant performance parameters such as the system throughput and reliability expressed in terms of the outage probability of a user transmission. The main trade-offs between the system performance and the OWC system setup parameters are investigated, in particular, by stressing how the indoor OWC-based system geometry plays an important role in the system performance.Using extensive numerical results, we clearly describe how the presented results are used to optimize the SA-based indoor OWC IoT system design.

The importance of unlicensed spectrum is highlighted in terms of the flexibility of network deployment for various services envisioned in 5G and beyond. Since listen before talk is mandatory for channel access in unlicensed spectrum and it causes an unavoidable waste of resources due to collisions, an efficient random backoff mechanism is required. In the existing backoff schemes that impose waiting penalties on collided packets, a degraded fairness performance is observed. In this work, we analyze how prioritizing collided packets can improve performance compared to existing schemes. To this end, we devise a random backoff scheme called the Collision Priority Backoff (CPB) under the concept of giving priority to collided packets. We apply Bayesian optimization to carefully determine channel access parameters of the CPB to maximize network throughput. Since the optimized access parameters require the number of stations in the network, we also devise an adaptive version of the CPB called the Adaptive CPB (ACPB). We deal with an environment where the number of stations changes as a switching bandit problem, and employ a variant of upper confidence bound policy in the ACPB. Various simulation results validate that the proposed backoff scheme shows high throughput and fairness performance.

To enhance the massive machine-type communications (mMTC) random access (RA) performance, a pattern division random access (PDRA) scheme was proposed for massive multiple-input multiple-output (mMIMO) systems.In this scheme, the pilot contention space was expanded to the pattern-domain.To enlarge the size of contention space without resorting to increasing the physical resources, the pattern-domain pilot was constructed based on the superposition of L cyclically-shifted Zadoff-Chu (ZC) sequences.Simulation results illustrate that the PDRA scheme can reduce pilot collision probability significantly, and improve the access success probability compared with the conventional RA scheme, without compromising excessively on channel estimation and data detection performance.

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. [ABSTRACT FROM AUTHOR]

In this paper, an asynchronous collision-tolerant ACRDA scheme based on satellite-selection collaboration-beamforming (SC-ACRDA) is proposed to solve the avalanche effect caused by packet collision under random access (RA) high load in the low earth orbit (LEO) satellite Internet of Things (IoT) networks. A non-convex optimization problem is formulated to realize the satellite selection problem in multi-satellite collaboration-beamforming. To solve this problem, we employ the Charnes-Cooper transformation to transform a convex optimization problem. In addition, an iterative binary search algorithm is also designed to obtain the optimization parameter. Furthermore, we present a signal processing flow combined with ACRDA protocol and serial interference cancellation (SIC) to solve the packet collision problem effectively in the gateway station. Simulation results show that the proposed SC-ACRDA scheme can effectively solve the avalanche effect and improve the performance of the RA protocol in LEO satellite IoT networks compared with benchmark problems. [ABSTRACT FROM AUTHOR]

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

Abstract Background Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access. Results In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores. Conclusions CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic .