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During the past decade, there has been an increasing interest in the audio-based indoor localization approach, which has benefited from the high precision, high privacy, and high security of the passive time difference of arrival (TDoA) positioning. However, the limited bandwidth, and complexity in real-time ID encoding and decoding under an actual reverberate environment make it difficult to achieve wide-area (>1500m2) localization in off-the-shelf smartphones. Plenty of previous work applied to a single region localization only with a small range. In this paper, a hybrid acoustic signal transmission architecture based on frequency division multiple access (FDMA), time division multiple access (TDMA), and space division multiple access (SDMA) is perfected to cover the acoustic signal to a wider range. With the prior information of the Bluetooth and floor-plan, smartphones estimate which cell themselves are located in accurately. We further propose the strategies of data bias integrity monitoring, dynamic variance adjustment based on the micro-electro-mechanical systems (MEMS) sensors, and the floor-plan restraint to enhance the particle filter and make the smartphones tracking smarter and more robust. The proposed prototype system can provide decimeter-level localization accuracy and stable 1Hz update rate in several typical wide indoor environments with less infrastructure.

This paper investigates intelligent reflecting surface (IRS) assisted wireless powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this non-convexity, the transmission time scheduling and the bandwidth allocation are optimally designed in closed-form by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in closed-form. We also characterize the convergence behaviour of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

In this paper, we focus on a downlink cellular network, where multiple UAVs serve as aerial base stations for ground users through frequency division multiple access (FDMA). With user locations and channel parameters inaccessible, the UAVs coordinate to make decision on resource allocation and trajectory design in a decentralized way. Aiming at optimizing both overall and fairness throughput, we model resource allocation and trajectory design as a decentralized partially observable markov decision process (Dec-POMDP) and propose multi-agent reinforcement learning (RL) as solution. Specifically, we use parametrized deep Q-network (P-DQN) for the action space comprising both discrete and continuous actions and QMIX framework is leveraged to aggregate each UAV’s local critics. For fairness throughput optimization, we introduce an entropy-like fairness indicator to the reward to make the total return decomposable. In addition, we further propose a novel distributed learning framework for overall throughput optimization such that each UAV can contribute its local gradient and model training can be implemented in parallel without need of observation data sharing among the UAVs. Simulation results show that the proposed multi-agent RL approach as well as the distributed learning framework is efficient in model training and presents acceptable performance close to that achieved by deterministic optimization, which relies on convention optimization techniques with user locations and channel parameters explicitly known beforehand. For fairness throughput optimization, we also show that ground users achieve individual throughputs close to each other, which verifies the effectiveness of the proposed fairness indicator as the reward definition in the RL framework.

In this paper, a robust iterative block decision feedback equalization (DFE) algorithm is developed for uplink Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems. Three important problems that can adversely affect the performance of the DFE in a SC-FDMA system are to be addressed here, i.e., the feedback symbols reliability, the feedback correlation metric, and the phase noise due to inaccuracies in the fabrication process of the crystal oscillator. Instead of using all the detected symbols in the feedback loop of the DFE, only the highly reliable symbols are selected to be fed-back. This results in the improvement of the error propagation, one of the common problem in a DFE. Also, the feedback correlation of the detected symbols is an important metric in the design of the DFE, and hence an elegant method is proposed in this design and found to perform better than the available existing designs in the literature. Finally, the transmitter and receiver phase noise is iteratively compensated using its corresponding time and frequency domains properties. Simulation results demonstrate the robustness of our designed iterative block DFE.

Modulation classification has attracted great interest in rapid deployment in wireless radios for civilian and military applications. While there is a massive amount of works on this topic for single-signal transmissions, a few works are reported in the literature for multi-signal transmissions. Therefore, there is an urgent need to explore different modulation classification algorithms for such scenarios. In this work, we propose a novel modulation classification algorithm for multiuser uplink single-carrier frequency division multiple access systems exploiting the channel decoder’s soft information as a beneficial resource to improve the classification performance. Starting from the maximum-likelihood principle, we analytically design the proposed algorithm using a space-alternating generalized expectation-maximization approach. Channel estimation is also developed as an auxiliary task for the proposed algorithm. Simulation results indicate that the proposed algorithm is superior to the traditional algorithms with reduced processing time.

The ability to recognize the medium access control protocol employed by a network can facilitate the incorporation of a cognitive radio into an existing network by elucidating an integral aspect of network behavior. Since the way in which users access the electromagnetic spectrum is one of the most prominent distinctions between reservation based and contention based medium access control protocols, the first part of this work exploits the regular timing of transmissions from networks utilizing reservation based time-division multiple access (TDMA) protocols to differentiate between transmissions governed by TDMA and by contention based carrier sense multiple access (CSMA) protocols. Our approach leverages modular arithmetic to identify periodicity in transmission timings and an unsupervised $k$ -means algorithm to generate distinct TDMA and CSMA clusters. Several supervised machine learning algorithms are explored to build a protocol classifier. We then present a method of distinguishing between transmissions from multi-channel frequency division multiple access (FDMA) based networks and single channel networks. This method uses an automated machine learning clustering algorithm to obtain an estimate of the actual center frequencies of channels utilized by a network. Such information can be used to determine whether the network is employing an FDMA protocol to access the electromagnetic spectrum.

Edge computing has been considered as one of the key paradigms in the fifth-generation (5G) networks for enabling computation-intensive yet latency-sensitive vehicular Internet services. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted vehicular edge computing via underlay spectrum sharing, in which vehicular computing-users (VUs) form a NOMA-group and reuse conventional cellular user's (CU's) channel for computation offloading. In spite of the benefit of spectrum sharing, the resulting co-channel interference degrades the CU's transmission. We thus firstly focus on a single-cell scenario of two VUs reusing one CU's channel, and analyze the CU's increased delay due to sharing channel with the VUs. We then jointly optimize the VUs’ partial offloading and the allocation of the communication and computing resources to minimize the VUs’ delay while limiting the CU's suffered increased delay. An efficient layered-algorithm is proposed to tackle with the non-convexity of the joint optimization problem. Based on our study on the single-cell scenario, we further investigate the multi-cell scenario in which a group of VUs flexibly form pairs to reuse the channels of different CUs for offloading, and formulate an optimal pairing problem to minimize the VUs’ overall-delay. To address the difficulty due to the combinatorial nature of the pairing problem, we propose a cross-entropy (CE) based probabilistic learning algorithm to find the optimal VU-pairings. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms for both the single-cell scenario and multi-cell scenario. The results also demonstrate that our NOMA-assisted MEC via spectrum sharing can outperform the conventional frequency division multiple access assisted offloading scheme.

The problem of finding the route with maximum end-to-end spectral efficiency in a multihop wireless network has been subject to considerable interest in the recent literature. It has been established that the problem is polynomially solvable in networks that employ time division multiple access (TDMA) or frequency division multiple access (FDMA). The main characteristic of TDMA and FDMA is being interference-free. Motivated by the advances in full-duplex (FD) communications and self-interference (SI) cancelation, this paper considers the problem for interference-limited wireless networks, in which nodes can send and receive at the same time and using the same frequency, but all links may interfere with each other. The contribution of this paper is three-fold. We formulate the problem as a mixed integer non-linear programming problem, and provide two equivalent re-formulations that provide more insight into the problem structure. Moreover, to the best of our knowledge for the first time, we provide a formal proof that the problem is NP-complete in the interference-limited case. Our proof uses reduction from a more general link scheduling problem subject to signal-to-interference-plus-noise-ratio (SINR) constraints. Finally, using an algorithm based on searching over paths while pruning the allowed number of hops, we provide a detailed numerical study that illustrates the tradeoffs between interference-limited FD relaying and TDMA half-duplex (HD) relaying. Our results indicate that sub-optimal interference-limited FD relaying leads to up to 2.69 times higher spectral efficiencies as compared to optimal TDMA HD relaying. Moreover, the lower the signal-to-noise ratio (SNR) regime, the larger the network size and/or the higher the SI capabilities, the higher the spectral efficiency gain due to FD (over HD) relaying.

The emergence of beyond-licensed spectrum sharing in FR1 (0.45–6 GHz) and FR2 (24–52 GHz) along with the multi-antenna narrow-beam-based directional transmissions demand a wideband spectrum sensing in temporal and spatial domains. We referred to it as ultrawideband angular spectrum sensing (UWAS), and it consists of digitization followed by the characterization of the wideband spectrum. In this article, we design and develop a state-of-the-art UWAS prototype using universal software radio peripheral (USRPs) and LabVIEW NXG for validation in the real-radio environment. Since 5G is expected to co-exist with LTE, the transmitter generates the multidirectional multiuser wideband traffic via LTE specific single-carrier frequency division multiple access (SC-FDMA) approach. At the receiver, the first step of wideband spectrum digitization is accomplished using a novel approach of integrating sparse antenna-array with reconfigurable sub-Nyquist sampling (SNS). The reconfigurable SNS allows the digitization of noncontiguous spectrum via low-rate analog-to-digital converters, but it needs intelligence to choose the frequency bands for digitization. We explore a multiplay multi-armed bandit-based learning algorithm to embed intelligence. Compared with previous works, the proposed characterization (frequency band status and direction-of-arrival estimation) approach does not need prior knowledge of received signal distribution. The detailed experimental results for various spectrum statistics, power gains, and antenna array arrangements validate the functional correctness, superiority, and feasibility of the proposed UWAS over state-of-the-art approaches.

This paper describes in particular Code Division Multiple Access (CDMA) applicable in Global Mobile Satellite Communications (GMSC). In satellite communication systems, as a rule, especially in GMSC networks many users are active at the same time. The problem of simultaneous communications between many single or multipoint mobile satellite users, however, can be solved by using Multiple Access Technique (MAT) system. Since the resources of the systems such as the transmitting power and the bandwidth are limited, it is advisable to use the channels with complete charge and to create a different MAT to the channel. This generates a problem of summation and separation of signals in the transmission and reception parts, respectively. Deciding this problem consists in the development of orthogonal channels of transmission in order to divide signals from various users unambiguously on the reception part.

A multi-cell network that uses orthogonal frequency division multiplexing access (OFDMA) is studied here. Unlike prior works, the proposed network consists of both (i) legacy data users that do not have energy harvesting capability and have a minimum data rate requirement and (ii) radio frequency (RF)-energy harvesting devices with a minimum energy requirement. It studies sub-band allocation to users and transmits power allocation at base stations. The authors formulate a mixed-integer non-linear program and also present two heuristics to assign sub-band to base stations. Numerical results show that RF energy harvesting devices will not affect network capacity if legacy data users require a high data rate. In addition, the results obtained from the two proposed heuristics are ∼ 95% of the optimal solution.

In infrastructure-less scenarios such as rural environments, wild emergency response, military applications and disaster relief, unmanned aerial vehicles (UAVs) are capable of providing enhanced mobile edge computing (MEC) services for ground users. Although small latency is the most important advantage of MEC system, how to provide delay aware scheduling in UAV-enabled MEC system still remains unsolved. In this study, the authors investigate the delay aware scheduling problem in UAV-enabled orthogonal frequency division multiple access (OFDMA) MEC system and formulate two non-convex optimisation problems. Moreover, they consider uplink and downlink architecture with characteristics in different UAV-ground links and traffic load. Furthermore, they propose two novel multi-stages resource allocation algorithms, i.e. the JSPA-T and JSPA-F algorithms with respect to downlink transmit power allocation and sub-carrier assignment. The mathematical frameworks with duality theory based alternative search optimisation and successive approximation method are proposed. The simulation results validate the performance improvement of the proposed solutions as well as the fast converge behaviour and small computational complexity.

Enhanced time-domain spreading is proposed for block-interleaved frequency division multiple access (B-IFDMA) with discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM). By repeating each consecutive modulation symbol prior to the spreading, we show that the DFT of the spreading sequence becomes a window function, which compared to the existing method of repeating the whole block of modulation symbols, offers a signal with both lower cubic metric (CM) and block error rate (BLER). Modulation symbol-specific spreading sequences are further shown to improve the BLER. The new transmission scheme is supported by both time- and frequency domain despreaders, as well as by two channel estimation methods for B-IFDMA.

With the multi-function need for airborne space-time adaptive processing (STAP) radar, range synthesis is usually necessary for the follow-up function of target recognition. Utilising the excellent performance on orthogonality and bandwidth synthesis of the frequency division multiple access (FDMA) waveform and the longer accumulation time of multiple-input multiple-output (MIMO) radar, a robust low-sidelobe target synthesis method for airborne FDMA–MIMO STAP radar is developed. The spectra characteristics of target and clutter are first analysed. Then, the keystone transform is employed to mitigate velocity migration. To avoid gain fluctuation induced by multiple independent adaptive weight vectors, the robust STAP beamformer is developed. Finally, to decouple the range–angle coupling relationship resulted from the FDMA waveform, angle compensation is added in the robust STAP beamformer. Numerical examples are given to demonstrate the effectiveness of the presented method.

In cluster-based cooperative cognitive radio networks (CCRNs), spectrum sensing and decision making processes to determine whether the primary user (PU) signal is present or absent in the network are very important and vital issues to the utilisation of the idle spectrum. The reporting time delay is a very important matter to make quick and effective global decisions for the fusion center (FC) in a cluster-based CCRNs. In this paper, we propose the concept of multiple reporting channels (MRC) for cluster-based CCRNs to better utilize the reporting time slot by extending the sensing time of secondary users (SUs). A multiple reporting channels concept is proposed based on frequency division multiple access to enhance the spectrum sensing performance and reduce the reporting time delay of all cluster heads (CHs). In this approach, we assign an individual reporting channel to each cluster for reporting purposes. All the SUs in each cluster sequentially pass their sensing results to the corresponding cluster head (CH) via the assigned single reporting channel, which extends the sensing time duration of SUs. Each CH uses the dedicated reporting channel to forward the cluster decision to the FC that makes a final decision by using the “K-out-of-N” rule to identify the presence of the PU signal. This approach significantly enhances the sensing time for all SUs than the non-sequential as well as minimize the reporting time delay of all CHs than sequential single channel reporting approach. These two features of our proposed approach increase the decision accuracy of the FC more than the conventional approach. Simulation results prove that our proposed approach significantly enhances the sensing accuracy and mitigate the reporting time delay of CH compared to the conventional approach.

In this paper, we develop task offloading and resource allocation scheme for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system with channel estimation errors over Rician fading channels. The objective is to maximize the system utility with constrained network stability, transmit power, and data arrival rate. We consider a general multi-user UAV-assisted MEC system based on frequency division multiple access (FDMA), and we assume that the computation tasks are split into separate tasks and offloaded to the server for computing. We study stochastic computational resource management based on the Lyapunov optimization algorithm. The optimal transmit power and bandwidth allocation for computation offloading are obtained alternately, and the optimal computation task admission at each time slot and the optimal value of the auxiliary variable are derived. Simulation results verify the effectiveness of the proposed scheme in the paper and evaluate the influence of various parameters to the system performance.

Device-to-device (D2D) communications in a cellular network can be implemented on vehicular communications. However, high mobilities of vehicles generate fast channel variations in the communications. Channel description has to be specified in the D2D-enable vehicular communications network. In this paper, the vehicle speed and the sampling time are involved to the channel description such that the dynamic information of vehicle is integrated with the channel gain. By doing so, the channel description is more practical. The power control and spectrum sharing problem is formulated in order to maximize the energy-efficiency (EE) of total vehicle-to-infrastructure (V2I) links. It also attempts to ensure reliable communications in a multi-cellular users multi-D2D users frequency division multiple access (FDMA) cellular environment. The reformulated objective function in the fractional form is proved to be quasi-concave, and it can be solved by the Dinkelbach method which requires low computational complexity. In addition, the Hungarian algorithm is used to determine the optimal match of cellular user equipments (CUEs) and V2V pairs. User fairness is further considered under the goal of maximizing overall energy efficiency. Then, the approach of minimum EE maximization is implemented on all V2I links in order to improve system fairness. Finally, the system performance of the proposed scheme is validated by numerical simulations. The results show that the algorithm is effective to improve the energy efficiency and robustness in the dynamic communications environment.

Initial ranging (IR) is a process of performing power adjustment, timing offset estimation, and synchronisation between the base station (BS) and all users. It plays a vital role in mobile WiMAX standard, which establishes a communication link between BS and ranging subscriber stations (RSSs). Since RSS is located far away from the BS and uplink signal is corrupted by noise, the IR process becomes inferior. Thus, the authors propose an improved dynamic threshold-based IR method comprising three phases: (i) code detection phase, (ii) filtering phase, and (iii) parameter estimation phase. In the first two phases, the received signal is pre-processed to eliminate noise and unselected codes from the codeset by employing adaptive filter and weight-based code detection method. Then, the noise-free signal is considered for the final phase, which improves accuracy. The iterative expectation maximisation algorithm is involved in the estimation of channel coefficient and timing offset. All possible active paths are detected by setting a dynamic threshold value for each channel by the enhanced dynamic threshold method. For all active channels, round trip delay, and power level are estimated by considering particular channel coefficient and timing offset. Extensive simulation of proposed work shows considerable improvements in performance.

Peak-to-average power ratio (PAPR) is the main parameter affecting the implementation cost of wireless transmitters utilising carrier aggregation in the end-users' channels. In each frequency band, single-carrier frequency division multiple access (SC-FDMA) is exploited as the interface from the transmitter to the receiver, and carrier aggregation is implemented by SC-FDMA on each band. Hence, the power amplifier simultaneously amplifies signals of multiple bands, meaning that its PAPR is affected by the use of multiple SC-FDMA channels. In this study, a novel method has been proposed for the delay and PAPR reduction in carrier aggregation systems. Also, a novel recursive method is proposed to reduce the computational complexity of the proposed partial transmit sequence (PTS) algorithm. Utilising computer simulations, it has been shown that the PAPR can be reduced by 3.5 dB for the time-domain PTS method in mapping for the three-band SC-FDMA comparing to existing methods.

In this study, the sum throughput maximisation problem for cognitive radio networks (CRNs) based on the multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) structure has been investigated. Hence, the intention is to maximise the downlink sum throughput of the CRN in a way that the sum power of the cognitive base station (CBS) remains below the given power limit and the induced interference on each subcarrier guarantees the interference threshold level predefined by primary users. Here, due to combinatorial constraints, the complex problem should be resolved. Hence, the core purpose of this study is to prepare a novel resource assignment algorithm that tries to fulfil these combinatorial constraints simultaneously. Therefore, this study makes progress towards solving the problem theoretically based on convex optimisation framework. An optimal solution has been implemented by proposing the iterative algorithm in which an optimal level of Lagrangian coefficients is obtained. Then, because of the optimal algorithm intricacy, the two low complicated algorithms are further suggested based on the solutions of two simplified versions of the original problem. Numerical results demonstrate that the suggested algorithms improve sum throughput considerably in comparison with classical algorithm. The proposed simplified algorithms converge to the optimal solutions' performance while their complexities are desirable for practical implementation.

This article is devoted to the analysis of the total spectral radiation density of LTE subscriber terminals. The LTE network uses orthogonal frequency division multiple access (OFDMA) technology for downlink communications, and multiple access technology with single-carrier frequency division multiple access (SC-FDMA) for uplink communications. In a downlink, the occupied radio frequency band is determined by the number of resource blocks and the size of the step of placement of subcarrier oscillations in the frequency domain. Features of the technologies used for organizing multiple access in LTE networks determine the nature of the spectral density of the total radio signal. Thus, in a downlink, the spectral density approaches uniformity, since the power distribution in the spectral region does not depend on the number and location of resource blocks allocated to the subscriber, and also does not depend on the territorial location of the subscriber itself. For an uplink, the determination of spectral density characteristics is not so unambiguous, since they depend on a number of factors (traffic parameters, the nature of radio conditions, etc.). Тo conduct a detailed analysis of the spectral radiation density of a set of LTE subscriber terminals, a scheme of the measuring unit was developed and experimental studies of the spectral density were conducted on the basis of the BSUIR research laboratory. The article presents spectrograms of a set of signals in the uplink of the LTE communication network, obtained for different time intervals and different places of observation. The conclusion is made about the possibility of using an equivalent uniform spectral density to describe interference effects from groupings of subscriber terminals of LTE communication networks.

With the advancements in next-generation communications systems, the demand for integration of services, such as video, audio, text, image of variable rates, etc., is a challenging issue. Internet of Things needs to integrate billions of devices and sensor nodes with different sampling rates. To integrate these services of multiple devices with variable rates, group orthogonal–orthogonal frequency division multiple access (GO-OFDMA) is an efficient multicarrier scheme. But to the authors’ knowledge and literature survey, there is no hardware architecture of GO-OFDMA for real-time testing. Therefore, a new architecture is proposed for GO-OFDMA baseband transmitter and receiver and prototyped on commercially available virtex5 field-programmable gate array (FPGA) device. Further challenges of this proposed architecture are integration of multiple user data with variable rates in the baseband transmitter and decoding of multiple user data within the same class at the baseband receiver. To validate the GO-OFDMA baseband transmitter and receiver, the transmitted and received bits in real time are captured using chipscope analyzer. The bit error rate and peak-to-average power ratio performance of the proposed architecture are presented. The hardware resource utilization of the GO-OFDMA architecture along with the maximum operating frequency is also reported.

The IEEE 802.11ax amendment for the next generation of Wireless local area network (WLAN) provides the Orthogonal frequency division multiple access (OFDMA) mechanism. The Uplink OFDMAbased random access (UORA) supports multiple users to transmit uplink data simultaneously in different Resource units (RUs). With UORA, Access point (AP) uses trigger frame to dynamically announce eligible Random access RUs (RA-RUs) for the associated mobile stations (STAs), and a STA is allowed to select an RU to transmit when it picks an OFDMA-based random access backoff (OBO) counter no more than the number of the eligible RARUs. The UORA requires the STA with an unsuccessful transmission to double its OFDMA contention window (OCW) size for the followed retransmission, which causes the STA to endure a longer delay for retransmission(s). We propose the Retransmission number aware channel access (RNACA) scheme for IEEE 802.11ax-based WLAN. In the RNACA, a failed STA makes decision on whether to double its OCW size or not by using a probability that considers the number of retransmissions, number of RUs, and number of STAs. Simulation results show that the proposed RNACA can gain a higher throughput and a lower packet delay.

Existing high-precision inter-satellite radio frequency (RF) measurement systems were mostly based on two-satellite formations, and there lacks further research on measurement schemes for multi-satellite formations. The frequency division multiple access (FDMA) and the code division multiple access based schemes are widely used, but not applicable for distributed applications due to their poor scalability. The time division multiple access (TDMA) based scheme can overcome this weakness, and has been applied in the global positioning system (GPS) IIR/IIF inter-satellite link. However, the atomic clock used in GPS is not suitable for microsatellites. If a miniaturised frequency source instead of the atomic clock is utilised, the two way ranging (TWR) method adopted in this system would encounter a sharp decrease of measurement accuracy. To this end, this Letter aims to propose a novel TDMA based distributed RF measurement scheme for multi-microsatellite formations. A TDMA based distributed broadcast protocol is employed in the media access control layer, closely integrated with the asymmetric double-sided two-way ranging method adopted in the physical layer. Numerical and simulation results demonstrate the superiority of the proposed scheme over the conventional TDMA scheme. The proposed scheme can be recommended for future multi-microsatellite formation missions.

In a wideband full-duplex system, the residual self-interference (SI) channel exhibits significant frequency selective fading characteristics after radio frequency SI cancellation. Resource allocation using the difference between the signal channel and the residual SI channel is a new method of suppressing residual SI and increasing system capacity. In this work, the residual SI channel and the user uplink channel are modelled as multi-path frequency selective fading channels that satisfy the Rayleigh distribution. To improve system capacity, the authors propose to allocate the spectrum resource avoiding occupation of subcarriers with high residual SI, while guaranteeing a fair subcarrier assignment (SA) and power allocation (PA). A sum rate of uplink maximisation is formulated, and an algorithmic solution is developed to effectively conduct SA and PA. Numerical results demonstrate the effectiveness of the scheme in increasing system capacity and suppressing residual SI.

The authors investigate secure resource allocation for an orthogonal frequency division multiple access two-way relay network, which has several pairs of mobile users (MUs) and one energy harvesting (EH) relay. In this network, the relay is untrusted and each MU pair may also overhear the other MU pairs' confidential information. In particular, to forward information for each MU, the relay adopts power splitting technique to harvest energy from radio frequency signals emitted by each MU. For this scenario, which has hardly been considered, the joint problem of subcarrier allocation, power control, and power splitting ratio selection is formulated, with the aim to maximise the total secrecy rate of all MUs. A suboptimal algorithm with low complexity and satisfactory performance is proposed to solve the formulated non-convex problem.

To remove the need for signaling overhead of feedback channels for transmitter channel state information (CSI) in Frequency Division Duplexing (FDD), we propose using convolutional neural networks and generative adversarial networks (GANs) to infer the downlink (DL) CSI by observing the uplink (UL) CSI. Our data-driven scheme exploits the fact that both DL and UL channels share the same propagation environment. As such, we extracted the environment information from UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel. To prevent incorrect latent domain and the problem of oversimplistic assumptions, we did not use any specific parametric model and, instead, used data-driven approaches to discover the underlying structure of data without any prior model assumptions. To overcome the challenge of capturing the UL-DL joint distribution, we used a mean square error-based variant of the GAN structure with improved convergence properties called boundary equilibrium GAN. For training and testing we used simulated data of Extended Vehicular-A (EVA) and Extended Typical Urban (ETU) models. Simulation results verified that our methods can accurately infer and predict the downlink CSI from the uplink CSI for different multipath environments.

OFDMA femtocell is a promising technology to improve indoor cellular network coverage cost-effectively. Large-scale deployment of femtocells in the urban area is expected to be realized in the near future. However, inter-femtocell interference significantly limits the achievable throughput of an OFDMA femtocell system, which calls for interference management tailored for femtocell networks. A typical approach to mitigate inter-femtocell interference is known as resource isolation, which aims at assigning non-overlapping resources to interfering femtocells. One of the main challenges for interference mitigation in femtocell networks is that end consumers often install the femtocells. Very limited information about the femtocells is available, making it hard to decipher the inter-femtocell interference. Previous studies either take time to resolve collisions online or adopt a conservative approach to identify interferers. Although the latter approach avoids wasting time on resolving collisions, it may result in resource underutilization. In this paper, we propose an efficient method to identify inter-femtocell interference by analyzing the received patterns observed by mobile stations. We conducted experiments on GNU Radio/USRP to demonstrate that the proposed interference identification method can successfully identify real interferers while excluding non-interfering femtocells from suspect femtocells. Based on the proposed interference identification, we propose a weighted vertex-coloring based resource assignment algorithm to allocate resources with better fairness and higher throughput.

The paper presents a novel approach for tactical networks in which radios form self-regulatory virtual sub-nets on the basis of control messages. The design uses hybrid of frequency division multiple access (FDMA) and time division multiple access (TDMA) approaches with less call setup delays and increases in network throughput. This multiple sub-nets based distributed algorithm exploits available spectrum resources, provides collision-free simultaneous transmissions and autonomous selection of time slots over different frequency sub-bands with self-organizing and self-forming network capabilities. We propose two schemes which provide multi-channel medium access control with autonomous scheduling of time slots for software-defined radios (SDRs). The schemes use request and acknowledgment frames as control messages to devise a distributed solution. These control messages alone help to form virtual sub-nets, preserve frequency channels and schedule transmissions over multiple time slot intervals, depend on the type of data messages. In order to demonstrate the behavior of sub-nets for tactical networks, theoretical findings are exploited with experimental analysis to provide practical implementation of schemes using contention-free time slotted common control channel for nodes coordination and time slotted collision-free multi-channel environment for data transmissions. The results show the effective coexistence of multiple transmissions in a network with increase in network throughput and lower the data latency up-to 76.8%, when compare to conventional MAC protocols. The design is workable for time sensitive, mission critical networks and expected to support multi-hop transmissions in similar manner.

Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art., Comment: This paper appears in the Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS) 2022. Please feel free to contact us for questions or remarks

The frequency division multiple access (FDMA) strategy used in GLONASS causes inter-frequency phase bias (IFPB) and inter-frequency code bias (IFCB) between receivers from different manufacturers. The existence of IFPB and IFCB significantly increases the difficulties of fixing GLONASS ambiguity and limits the accuracy and reliability of GLONASS positioning. Moreover, the initial value of IFPB and IFCB may be unavailable or unreliable with the increasing number of receivers from different manufacturers in recent years. In this study, a real-time and reliable calibration algorithm of IFPB and IFCB based on multi-GNSS assistance is proposed by providing a fixed solution. Real-time IFPB rate and IFCB can be obtained using this algorithm without the initial IFPB and IFCB. The IFPB rate for all GLONASS satellites and IFCB for each GLONASS satellite are estimated due to different characteristics of IFPB and IFCB. IFPB calibration can be divided into constant and real-time IFPB calibrations to meet the different positioning requirements. Results show that constant IFPB rate has only 2 mm difference from the mean value of real-time IFPB rate. The IFPB rate and IFCB estimated by this algorithm have excellent stability, and the change in reference satellite cannot affect the results of IFPB rate and the stability of IFCB. The centimetre-level positioning results can be obtained using two calibration methods, and the positioning results with real-time calibration method are 10%–20% better than those with the constant calibration method. Under satellite-deprived environments, the improvements of multi-GNSS positioning accuracy with constant inter-frequency bias calibration gradually increase as the satellite cut-off elevation angle increases compared with GPS/BDS, which can reach up to 0·9 cm in the vertical direction.