Radio frequency (RF)-based imaging plays a pivotal role in sensing applications due to its robustness against visibility constraints and environmental factors. Among various RF imaging technologies, impulse radio ultra-wideband (IR-UWB) stands out by offering both fine temporal resolution and penetration capabilities. Existing work is limited to planar or depth imaging with multipath suppression. However, achieving depth imaging exploiting multipath IR-UWB signals and further realizing penetration imaging remains a challenge. To address it, this paper proposes a novel neural network architecture that uses IR-UWB signals to reconstruct depth and penetration images. First, we give a feasibility analysis of the dual imaging tasks using IR-UWB signals in an enclosed environment with multipath effects. Then, a theoretical spatial geometric inverse model is presented to formulate depth and penetration imaging as a joint optimization problem. Next, to address the ill-posed and intractable nature of the optimization problem, we propose a neural network architecture named the attention-enhanced depth and penetration imaging network (AEDPI-Net) as an approximate solver. AEDPI-Net employs a shared encoder to extract common features from IR-UWB signals, which are subsequently mapped by two parallel decoders into depth and penetration images. Finally, extensive experimental studies on an IR-UWB imaging platform verify the effectiveness of AEDPI-Net. The results reveal that AEDPI-Net achieves superior depth and penetration imaging performance compared to the baseline methods.

Federated edge learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: 1) sensing; 2) computation; and 3) communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into integrated sensing-computation–communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters, such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a submodel through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables, such as batch size, sensing power, dropout rate, and communication power. This nonconvex problem is decomposed into two subproblems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes.

High-accuracy indoor localization is a key enabler of ubiquitous location-based services (LBSs) in the Internet of Things (IoT), with applications in mobile robots, asset tracking, and beyond. For indoor localization, it has been reported that the methods based on probabilistic models have high localization accuracy and strong generalization in the presence of nonline-of-sight (NLOS) conditions and multipath effects. To further leverage such advantages, this article proposes two fusion localization methods based on Bayesian filters which fuse an inertial measurement unit (IMU) motion model with a probabilistic model constructed by soft information (SI) framework to enhance localization performance. First, we propose a method based on particle filter (PF) to directly fit the posterior probability density distribution (PDF), called PF-SI. This method reduces accuracy loss caused by linearization and achieves high accuracy. Then, to reduce the high computational complexity of the PF-SI method, we utilize an error state Kalman filter (ESKF) to construct linearized error state transition and error observation equations and update the filter with distance residual, as ESKF-SI. This method has slightly lower localization accuracy but significantly improves computational efficiency. Finally, experimental results in a real indoor scenario based on ultrawideband (UWB) signals are presented. The results show that the two proposed fusion methods can achieve a root mean square localization error of less than 0.25 m in a complex NLOS scenario.

Efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we conduct the rounding error analysis and derive the lower bound of the achievable rate for single-input-multiple-output (SIMO) using maximal ratio combining (MRC) and multiple-input-single-output (MISO) systems using maximal ratio transmission (MRT) with finite-precision arithmetic. Then, considering the multi-user scenario, the rounding error analysis of zero-forcing (ZF) detection and precoding is derived by using the normal equations (NE) method. The corresponding lower bounds of the achievable sum rate are also derived and asymptotic analyses are presented. Built upon insights from these analyses and lower bounds, we propose a mixed-precision architecture for massive MIMO systems to offset performance gaps due to finite-precision arithmetic. The corresponding analysis of rounding errors and computational costs is obtained. Simulation results validate the derived bounds and underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods used for precise estimation of channel parameters, the proposed PF-assisted method is utilized to narrow down the search range for the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The theoretical analysis demonstrates that the PF-assisted JCD method can achieve equivalent performance to the Bayesian-optimal DF-based method, while greatly reducing the computational complexity. Simulation results are also presented to validate the analytical results.

Coded computation has attracted significant attention because it can eliminate the stragglers’ effect effectively. Most existing works of coded computation are designed for linear tasks, such as matrix multiplication. They cannot handle non-linear tasks directly, leading to high computation, transmission and decoding latency. This is not suitable for latency-sensitive services. In this paper, considering a non-linear task in wireless heterogeneous networks, we propose an efficient merged-rLuby transform (LT) coded computation scheme based on the rateless and sparse LT code. First, we give the merged-rLT coding strategy to reduce the computation and transmission costs. Then, the maximum degree decoding (MDD) strategy is proposed to speed up the decoding process. Finally, we analyze the latency performance for the whole network by designing the optimal merging parameter and sub-block size. The wireless non-linear merged-rLT coded computation (WNLMrLTCC) algorithm minimizes the total latency. Theoretical analysis and numerical simulation show that our proposed scheme has significant advantages over the existing ones for non-linear tasks.

Cloud-edge collaborative computing has emerged as a promising solution to satisfy the demands for intensive computation and low latency. However, the straggler effect in cloud-edge collaborative computing systems is serious but has not been addressed. This can be mitigated by designing an effective computing scheme, with the assistance of cloud server. Moreover, the problem of transmission failure becomes more severe in cloud-edge collaborative computing systems. Specifically, the result downloaded from the edge nodes will be affected by poor channel conditions, while the result transmitted from the cloud to edge nodes will also suffer from packet loss. In this paper, we propose a coding assisted cloud-edge collaborative computing (CA-CECC) scheme to solve these problems. To make it more tractable, we separate the transmission phase from the computation phase, and decouple cooperative transmission from single-point transmission. By dividing the set of cooperative nodes into two sets, we further propose two simplified schemes of low complexity. Their latencies are established as upper bounds on the latency of CA-CECC. Numerical simulation results verify the superiority of our proposed scheme.

Distributed computing has made it possible to satisfy the demands for large-scale matrix multiplication. A distributed computing system suffers from both straggler problem and information leakage. In a heterogeneous network consisting of multiple worker groups, information leakage can be caused by both intra-group and inter-group collusion. Besides, considering the heterogeneity of worker nodes, stronger nodes are supposed to compute more tasks to provide robustness for stragglers. However, this results in more information being leaked to stronger nodes, contradicting the principle of information protection. In this paper, we propose a multi-group heterogeneous secure coded matrix multiplication (MG-HSCMM) scheme to solve these problems in a heterogeneous multi-group network. By taking the heterogeneity of worker nodes into consideration, the corresponding recovery threshold and security constraint are obtained. To improve the performance of such a network, a low complexity task allocation policy that balances straggler mitigation and information protection is given. Compared with existing schemes, MG-HSCMM can achieve significant performance gain. Numerical simulation results verify the superiority of our proposed scheme.

Integrated sensing and communication (ISAC) based on coordinated multi-point (CoMP) can provide enhanced sensing and communication capabilities. However, existing CoMP ISAC designs are mostly based on sensing and communication with the same symbol duration and waveform, which greatly restricts the range resolution of sensing and the separation of multiple echoes of the target. In this paper, we propose a new CoMP ISAC design employing distinct symbol durations and waveforms. It can achieve high-resolution range sensing of the target with short sensing symbol durations and distinguish target echoes from different transmitting stations (TXs) with orthogonal sensing waveforms. First, we derive the average spectral efficiency of communication and the detection probability of sensing as the performance metrics of the two functionalities, respectively. Then, an efficient CoMP beamforming design is proposed to maximize the communication performance under the constraints on the maximum transmit power of each TX and the required sensing performance. Moreover, a distributed implementation of the above beamforming design is proposed to reduce the computational burden of the central controller. Finally, a low-complexity design based on linear beamforming structures is presented. Numerical results verifies the effectiveness of the proposed designs.

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to enablenear-fieldcommunications for achieving enhanced spectrum efficiency and spatial resolution, by drastically increasing the number of antennas. However, this also inevitably incurs higher hardware and energy cost, which may not be affordable in future wireless systems. To address this issue, we propose in this paper two types ofsparse arrays(SAs) for enabling near-field communications. Specifically, we first consider thelinear sparse array(LSA) and characterize its near-field beam pattern. It is shown that LSAs can achieve the near-field beam-focusing gain with lower hardware cost and energy consumption, while it introduces several undesiredgrating-lobes, which are focused on specific regions exhibiting comparable beam power with the main-lobe. An efficient hybrid beamforming design is then proposed for the LSA to deal with the potential strong inter-user interference (IUI). Next, we further consider another form of SA, calledextended coprime array(ECA), which is composed of two LSA subarrays with different (coprime) inter-antenna spacing. By characterizing the ECA near-field beam pattern, we show that compared with the LSA of the same array sparsity, ECAs can greatly suppress the beam power of near-field grating-lobes thanks to theoffseteffect of the two subarrays, albeit generating more low-power grating-lobes. This thus motivates us to propose a customized two-phase hybrid beamforming design for ECAs. Finally, numerical results are presented to demonstrate the energy-efficiency gain of the proposed two SAs over dense uniform linear arrays.

The efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we propose a mixed-precision architecture for massive MIMO systems based on blocked matrix computations. Then the corresponding analysis of rounding errors and computational costs is derived. Finally, simulation results underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods, the proposed PF-assisted method is utilized to capture the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The simulation results show that the PF-assisted JCD method can achieve near the same performance as the Bayesian-optimal DF-based method, while greatly reducing the computational complexity.

Prior works on near-field beam training mostly assume dedicated polar-domain codebooks and on-grid range estimation, however, this may incur large training overhead and deteriorated estimation accuracy. In this paper, we propose a new and efficient beam training scheme with off-grid range esti-mation based on conventional discrete Fourier transform (DFT) codebook, which greatly reduces the beam training overhead. In particular, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and reveal an interesting result that this beam pattern contains useful user angle and range information. Then, an efficient scheme was proposed to jointly estimate the user angle and range using DFT codebook. This scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width. Finally, numerical simulations show that our proposed scheme significantly reduces the near-field beam training overhead and improves the range estimation accuracy compared with various benchmark schemes.

For the sake of enhancing the covertness and sensing performance in the integrated sensing and covert communications (ISCC) system, we design a beamforming framework for reconfigurable intelligence surface (RIS) assisted ISCC. Specifically, the system intends to transmit information to a legitimate receiver (Bob) covertly and sense the target simultaneously while avoiding being detected by a warden (Willie). RIS can be applied to both traditional single-connected networks and a broad fully-connected networks. By jointly optimizing the beamforming vector of the communication and the autocorrelation matrix of the sensing, and the phase shift matrix of the RIS, both the convert rate and the target’s probing power are maximized. And the covertness and the constant-mode constraints are considered. A multi-strategy alternate optimization (MSAO) algorithm is proposed to solve the optimization problem based on quadratic constraint quadratic programming (QCQP) and semidefinite relaxation (SDR). Furthermore, we consider a more realistic application scenario where the legal party has imperfect channel state information of Willie. Simulation results show that deploying RIS in a generalized fully-connected mode can achieve better transmission of beampatterns and increase upper limit of covert communications rate than conventional single-connected mode.

Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cramér-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.

In wireless networks with distributed computing, the computational performance is limited by stragglers. To mitigate the stragglers’ effect, coded computation is adopted through computational redundancy. Moreover, in wireless transmission, transmission errors may occur due to noise, channel fading and so on. Existing works design coded computation and error detection separately. However, this leads to frequent encoding and inefficient allocation. In this paper, we propose a joint computation and transmission coding (JCTC) scheme to design coded computation and error detection jointly. The coded computation is based on Luby transform (LT) code and linear error-detecting codes are applied for the re-transmission mechanism. To achieve the low dynamic encoding, two-layer encoding is adopted. Then, the performances of JCTC scheme are analyzed in terms of latency and computation reliability. Finally, in order to achieve efficient task and redundancy allocation, the wireless LT coded computation with error detection (WLTCC-ED) algorithm is given from both iterative and low-complexity perspectives respectively. Through theoretical analysis and numerical simulation, it shows that our proposed JCTC scheme has significant advantages over separate designs.

Efficient implementation of Tomlinson-Harashima precoding (THP) is crucial in massive multiple-input-multiple-output (MIMO) systems with a large number of antennas at the base station (BS) serving many user equipments (UEs). To address the high computational complexity of THP, in this paper, we first propose novel THP update algorithms that can avoid recomputing the THP filters when a new UE arrives or departs. Specifically, by using the Gram-Schmidt process and a series of Givens matrices, the THP filters are computed without full matrix operations. Then we extend the THP update algorithms to a more general scenario when multiple multi-antenna UEs arrive or depart. In this case, the proposed algorithms use both direct and iterative approaches. Moreover, the computational complexity of the proposed algorithms is derived and compared with that of the conventional THP. Finally, to further align with the practical scenario, we analyze and derive the approximate close-form expressions for the sum achievable rate of the proposed algorithms under imperfect channel state information (CSI). Simulation results are provided to illustrate the effectiveness of the proposed algorithms. The impact of quasi-static fading and slow time-varying scenarios with imperfect CSI on the communication performance of the proposed algorithms is also evaluated.

Semantic communication shows great promise in reducing network traffic and alleviating spectrum shortage. While many semantic theories have been put forward, how to measure the importance of semantic information theoretically remains an open issue. In this paper, we propose semantic value, a metric that measures the importance of semantic information, for text transmission. First, we model a semantic communication system for text transmission, in which semantic information is represented by semantic triplets. Then, we propose a hybrid communication mechanism to ensure the success of text transmission. Finally, we compare the performances of the conventional mode and the semantic mode in terms of latency and derive conditions leading to minimum latency.

Coded computing has received significant attention thanks to its advantage in alleviating the straggler effect in distributed computation framework, which would be one of the key fundamental techniques to enable the distributed and decentralized network architectures towards 5G-advanced and 6G era. Specifically, considering the scenario that multiple tasks randomly arrive at the network, the additional task queuing makes the delay analysis of coded computing more challenging. In this paper, we consider the impacts of task queuing and characterize the end-to-end delay for coded computing systems under the multi-task scenario. To this end, we first model the end-to-end coded computing system. Then, based on the redundant task processing strategies, we consider both purging and non-purging coded computing schemes. Although the expected end-to-end delay for both schemes are intractable, we obtain closed-form expressions for their respective lower and upper bounds, which generalizes the delay results of the single-task scenario. Moreover, we show that the multi-task coded computing has a coding gain of Θ(logn) wherendenotes the number of worker nodes, even with task queues considered. Simulation results verify the accuracy of the derived delay bounds and show the effectiveness of coded computing in the multi-task scenario.

The performance of multiple-input multiple-output (MIMO) integrated sensing and communication systems (ISAC) can be evaluated from the perspectives of information theory and estimation theory to provide more fundamental insights. In this paper, we study the relationship between mutual information (MI) and minimum mean square error (MMSE) by characterizing the Pareto boundary for a general ISAC scenario, a dual-functional BS simultaneously estimates the target response matrix while communicating with a user. First, optimization problems are formulated to achieve MI Pareto boundary and MMSE Pareto boundary, respectively. Then, we show that under the same maximum transmit power constraint and set of transmit filters, MI Pareto bounary can be transformed to MMSE Pareto boundary with optimized MSE-weights in ISAC with colored Gaussian noise. Subsequently, based on unified MI and MMSE performance, we propose Data-dependent alternate algorithm (DDA) to obtain the MI Pareto boundary with colored Gaussian noise. In order to reduce complexity, we propose Data-independent alternate algorithm (DIA) when noise degenerates into white Gaussian noise. Finally, simulation results show DDA almost achieves the MI Pareto boundary with colored Gaussian noise and DIA achieves almost the same performance as DDA with white Gaussian noise at a lower cost to implement.

In wireless distributed computing, coded computation is adopted through computational redundancy to mitigate the stragglers’ effect. Moreover, transmission errors may occur due to noise, channel fading and so on. Existing works design coded computation and error detection separately. However, this leads to frequent encoding and inefficient allocation. In this paper, we propose a joint computation and transmission coding (JCTC) scheme to design coded computation and error detection jointly. The coded computation is based on Luby transform (LT) code and linear error-detecting codes are applied for the retransmission mechanism. To achieve the low dynamic encoding, two-layer encoding is adopted. Then, the performances of JCTC scheme are analyzed. Finally, in order to achieve efficient task and redundancy allocation, the wireless LT coded computation with error detection (WLTCC-ED) algorithm is given. Through the simulation, it shows that our proposed JCTC scheme has significant advantages over separate designs.

The focus of this study is on the waveform design of the radar system that shares spectrum with the communication system in multi-path environments. The main challenge comes from combining useful multi-path target echoes received at the radar system while suppressing radar interference to communication users. Specifically, we formulate the waveform design problem as a maximization of the signal-to-noise ratio (SINR) subject to constraints of communication rate and the similarity degree with some standard waveform. The multi-path propagation complicates the expressions of the radar SINR and communication rate, increasing the difficulty of solving the problem. We propose a sub-optimal algorithm based on the successive convex approximation and semi-definite programming methods to solve the problem. Simulation results are provided to demonstrate the effectiveness of the proposed design.

Pulse-based integrated sensing and communication (ISAC) systems have the advantages of high ranging resolution and strong resistance to self-interference, compared with continuous wave (CW) based systems. However, for pulse-based ISAC systems, multi-path channels pose various challenges to data recovery and ranging by providing diversity gain for data recovery but incurring the interference to the identification of the first path in ranging. In this paper, we design a pulse-based ISAC receiver for multi-path channels. The designed receiver can obtain the diversity gain by correlating the received signal with the estimated template signal. Meanwhile, it can detect the arrival of the first path by using a threshold detection method based on a constant false alarm rate (CFAR). Furthermore, we extend the pulse-based ISAC design to a low-resolution analog-to-digital converter (ADC) scenario. A low-cost receiver design is provided for the pulse-based ISAC system that can recover data and estimate range simultaneously considering the non-linear effect caused by the low-resolution ADC. Simulation results show that compared with the generalized maximum likelihood (GML) based receiver, the proposed full-resolution pulse-based ISAC receiver has 1dB signal-to-noise ratio (SNR) loss in bit error rate (BER) and almost the same mean squared error (MSE) performance with the significantly reduced computational complexity. Also, compared with the full-resolution ISAC receiver, the ISAC receiver with 3-level quantization incurs only 0.8dB SNR loss in BER and 1dB SNR loss in MSE.

To improve both sensing and communication performances, this paper proposes a coordinated multi-point (CoMP) transmission design for a dual-functional radar-communication (DFRC) system. In the proposed CoMP-DFRC system, the central processor (CP) coordinates multiple base stations (BSs) to transmit both the communication signal and the dedicated probing signal. The communication performance and the sensing performance are both evaluated by the signal-to-interference-plus-noise ratio (SINR). Given the limited backhaul capacity, we study the waveform and clustering design from both the radar-centric perspective and the communication-centric perspective. Dinkelbach's transform is adopted to handle the single-ratio fractional objective for the radar-centric problem. For the communication-centric problem, we adopt quadratic transform to convexitify the multi-ratio fractional objective. Then, the rank-one constraint of communication beamforming vector is relaxed by semidefinite relaxation (SDR), and the tightness of SDR is further proved to guarantee the optimal waveform design with fixed clustering. For dynamic clustering, equivalent continuous functions are used to represent the non-continuous clustering variables. Successive convex approximation (SCA) is further utilized to convexitify the equivalent functions. Simulation results are provided to verify the effectiveness of all proposed designs.

The incomplete and imperfect essence of the battlefield situation results in a challenge to the efficiency, stability, and reliability of traditional intention recognition methods. For this problem, we propose a deep learning architecture that consists of a contrastive predictive coding (CPC) model, a variable-length long short-term memory network (LSTM) model, and an attention weight allocator for online intention recognition with incomplete information in wargame (W-CPCLSTM). First, based on the typical characteristics of intelligence data, a CPC model is designed to capture more global structures from limited battlefield information. Then, a variable-length LSTM model is employed to classify the learned representations into predefined intention categories. Next, a weighted approach to the training attention of CPC and LSTM is introduced to allow for the stability of the model. Finally, performance evaluation and application analysis of the proposed model for the online intention recognition task were carried out based on four different degrees of detection information and a perfect situation of ideal conditions in a wargame. Besides, we explored the effect of different lengths of intelligence data on recognition performance and gave application examples of the proposed model to a wargame platform. The simulation results demonstrate that our method not only contributes to the growth of recognition stability, but it also improves recognition accuracy by 7%-11%, 3%-7%, 3%-13%, and 3%-7%, the recognition speed by 6- 32× , 4- 18× , 13-* × , and 1- 6× compared with the traditional LSTM, classical FCN, OctConv, and OctFCN models, respectively, which characterizes it as a promising reference tool for command decision-making.

In time-division duplexing (TDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the reciprocity mismatch severely degrades the performance of the hybrid beamforming (HBF). In this work, to mitigate the detrimental effect of the reciprocity mismatch, we investigate reciprocity calibration for the mmWave-HBF system with a fully-connected phase shifter network. To reduce the overhead and computational complexity of reciprocity calibration, we first decouple digital radio frequency (RF) chains and analog RF chains with beamforming design. Then, the entire calibration problem of the HBF system is equivalently decomposed into two subproblems corresponding to the digital-chain calibration and analog-chain calibration. To solve the calibration problems efficiently, a closed-form solution to the digital-chain calibration problem is derived, while an iterative-alternating optimization algorithm for the analog-chain calibration problem is proposed. To measure the performance of the proposed algorithm, we derive the Cramér-Rao lower bound on the errors in estimating mismatch coefficients. The results reveal that the estimation errors of mismatch coefficients of digital and analog chains are uncorrelated, and that the mismatch coefficients of receive digital chains can be estimated perfectly. Simulation results are presented to validate the analytical results and to show the performance of the proposed calibration approach.

Spatial beamforming is an efficient way to realize dual-functional radar-communication (DFRC) for integrated sensing and communications towards 6G network. In this paper, we study the DFRC design for a general scenario, where the dual-functional base station simultaneously detects the target as a MIMO radar while communicating with multiple multi-antenna communication users (CUs). This necessitates a joint transceiver beamforming design for both MIMO radar and multi-user MIMO communication. In order to avoid iterative optimization with high complexity, two low-complexity beamforming designs based on CU-selection and zero-forcing are proposed, where the closed-form expressions of the low-complexity beamforming designs are derived. Simulation results are provided to verify the effectiveness of the proposed low-complexity designs.

Spatial beamforming is an efficient way to realize dual-functional radar-communication (DFRC). In this paper, we study the DFRC design for a general scenario, where the dual-functional base station (BS) simultaneously detects the target as a multiple-input-multiple-output (MIMO) radar while communicating with multiple multi-antenna communication users (CUs). This necessitates a joint transceiver beamforming design for both MIMO radar and multi-user MIMO (MU-MIMO) communication. In order to characterize the performance tradeoff between MIMO radar and MU-MIMO communication, we first define the achievable performance region of the DFRC system. Then, both radar-centric and communication-centric optimizations are formulated to achieve the boundary of the performance region. For the radar-centric optimization, successive convex approximation (SCA) method is adopted to solve the non-convex constraint. For the communication-centric optimization, a solution based on weighted mean square error (MSE) criterion is obtained to solve the non-convex objective function. Furthermore, two low-complexity beamforming designs based on CU-selection and zero-forcing are proposed to avoid iteration, and the closed-form expressions of the low-complexity beamforming designs are derived. Simulation results are provided to verify the effectiveness of all proposed designs.

Computation over multiple access channels (CoMAC) has been proposed to solve the problem of spectrum scarcity in wireless networks, which combines communication and computation efficiently using the superposition property of wireless channels. In this paper, we consider a multi-cluster CoMAC network, whose performance is affected by the inter-cluster interference and the non-uniform fading. To minimize the sum mean squared error of signals aggregated at different fusion centers (FCs), we propose a transceiver design for multi-cluster CoMAC. Specifically, we adopt a uniform-forcing transmitter design to formulate the receiver design as a quadratic sum-of-ratios problem with nonconvex quadratic constraints. Then, we propose a branch-and-bound algorithm to find its optimal solution with a given error tolerance. To solve the problem in a decentralized way, we develop a distributed algorithm based on the primal decomposition theory. Each subproblem is solved by using the successive convex approximation method. Further combining Lagrange duality, we derive the optimal solution structure of each subproblem, based on which we can find the solution with lower complexity. Simulation results demonstrate the effectiveness of the proposed distributed transceiver design.

The performance of distributed computing is restricted by the slowest worker nodes, known as stragglers, in the system. Coded computation has emerged as an efficient technique to mitigate the straggler effects in distributed computing. Most existing works only considered the computation straggler for single-hop networks. However, in multi-hop networks, the straggler effects will occur not only on worker nodes but also on relay nodes. In this paper, we consider a heterogeneous multi-hop network. The nodes in the network are heterogeneous, i.e., their computation capacities and transmission capacities are different. We propose a hierarchical coding scheme for such a network. Firstly, we reorganize it into a hierarchical network containing multiple layers. Each layer in the network consists of several groups. Then, a new hierarchical coding scheme is proposed, where coding is applied to each group to mitigate the stragglers. By taking both the computation time and transmission time into consideration, the overall task completion time is derived. To improve the performance of the network, heterogeneous hierarchical coded computation (HHCC) algorithm is proposed to provide an asymptotically optimal task allocation strategy. Compared with existing uniform uncoded, load balanced uncoded, and heterogeneous coded matrix multiplication schemes, HHCC has significant improvement.

Intelligent reflecting surface (IRS) is a promising technology for enhancing wireless communication systems. It adaptively configures massive passive reflecting elements to control wireless channel in a desirable way. Due to hardware characteristics and deploying environments, an IRS may be subject to reflecting element blockages and failures, and hence developing diagnostic techniques is of great significance to system monitoring and maintenance. In this paper, we develop diagnostic techniques for IRS systems to locate faulty reflecting elements and retrieve failure parameters. Three cases of channel state information (CSI) availability are considered. In the first case where full CSI is available, a compressed sensing based diagnostic technique is proposed, which significantly reduces the required number of measurements. In the second case where only partial CSI is available, we jointly exploit the sparsity of the millimeter-wave channel and the failure, and adopt compressed sparse and low-rank matrix recovery algorithm to decouple channel and failure. In the third case where no CSI is available, a novel atomic norm is introduced as the sparsity-inducing norm of the cascaded channel, and the diagnosis problem is formulated as a joint sparse recovery problem. Finally, the proposed diagnostic techniques are validated through numerical simulations.

Time-division-duplexing massive multiple-input multiple-output (MIMO) systems estimate the channel state information (CSI) by leveraging the uplink-downlink channel reciprocity, which is no longer valid when the mismatch arises from the asymmetric uplink and downlink radio frequency (RF) chains. Existing works treat the reciprocity mismatch as constant for simplicity. However, practical RF chains consist of nonlinear components, which leads to nonlinear reciprocity mismatch. In this work, we examine the impact of the nonlinear reciprocity mismatch on the performance of massive MIMO systems. To evaluate the impact of the nonlinear mismatch, we first derive the closed-form expression of the ergodic achievable rate for the multi-user massive MIMO system. Then, we analyze the performance loss caused by the nonlinear mismatch to show that the impact of the mismatch at the base station (BS) side is much larger than that at the user equipment side. Simulation results are presented to examine the system performance and to verify the analytical results.

In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the unmanned aerial vehicle (UAV) via uplink nonorthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into N time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

Reinforcement learning, as an effective method to solve complex sequential decision-making problems, plays an important role in areas such as intelligent decision-making and behavioral cognition. It is well known that the sample experience replay mechanism contributes to the development of current deep reinforcement learning by reusing past samples to improve the efficiency of samples. However, the existing priority experience replay mechanism changes the sample distribution in the sample set due to the higher sampling frequency assigned to a specific transition, and it cannot be applied to actor-critic and other on-policy reinforcement learning algorithm. To address this, we propose an adaptive factor based on TD-error, which further increases sample utilization by giving more attention weight to samples of larger TD-error, and embeds it flexibly into the original Deep Q Network and Advantage Actor-Critic algorithm to improve their performance. Then we carried out the performance evaluation for the proposed architecture in the context of CartPole-V1 and 6 environments of Atari game experiments, respectively, and the obtained results either on the conditions of fixed temperature or annealing temperature, when compared to those produced by the vanilla DQN and original A2C, highlight the advantages in cumulative rewards and climb speed of the improved algorithms.

Utilizing the sparsity of observations, compressive sensing (CS) is a promising technique to observe physical quantities in the Internet of Things (IoT). The existing CS-based IoT networks require the distributed nodes to realize the CS measurements before transmission, which leads to the heavy computation burden at nodes and the low network performance, especially in large-scale IoT networks. To tackle this problem, in this article, we propose a multicluster cooperative CS (CCS) scheme for large-scale IoT networks to observe physical quantities efficiently, which utilizes the cooperative observation and coherent transmission to realize CS measurement. Specifically, CCS-based IoT first utilizes the intracluster cooperation to observe physical quantities and then exploits the intercluster coherent transmission to obtain CS measurements. To evaluate the performance of CCS-based IoT, the upper bound of average recovery error is derived, which is closely associated with the node clustering and the power allocation. Therefore, under the total power constraint, we jointly optimize the node clustering and power allocation to improve recovery performance. With the optimized result, the cooperation gain of the CCS scheme relative to others is derived, which indicates that the CCS scheme is superior as long as total power exceeds a certain threshold. Furthermore, the exact and asymptotic outage probability and average throughput of CCS-based IoT are derived. Finally, simulation results illustrate the correctness of the theoretical analysis.

Computation over multiple access channel (CoMAC) scheme provides a promising solution to future large-scale wireless networks by utilizing the superposition property of the wireless channel to compute a class of functions with a summation structure (e.g., mean, norm, etc.). However, its implementation usually requires all nodes' channel state information (CSI) and its performance is limited by the channel condition of the worst node. In order to avoid massive CSI aggregation and improve the limited performance, we propose an automatic repeat request (ARQ)-aided CoMAC scheme in this paper. The transmitters and signaling procedures are designed to achieve the tradeoff between the achievable function rate and the transmission delay. The corresponding performance of the proposed ARQ-aided CoMAC scheme and the traditional ARQ-aided communication scheme are compared for both homogeneous networks and heterogeneous networks. By optimizing the ARQ level, we further maximize the achievable function rate of the proposed scheme. Asymptotic closed-form expressions are derived by resorting to the extreme value theory and point mass approximation. Monte Carlo simulations are given to illustrate and verify the performance of the proposed designs.

Energy-efficient designs play an important role in the Internet of Things (IoT) that monitors multiple phenomena, due to the limited power supply and complicated observation. In this paper, taking into account the power consumptions of observation, coding, and communication, we propose a joint sparse observation and coding scheme for energy-efficient monitoring of multiple phenomena using IoT. Through the analysis of outage performance, we find that the sparse observation and coding scheme can achieve the performance of the full observation scheme in which all nodes observe all phenomena with lower power consumption due to the dynamic and selective observation and coding. With the derived achievable rates and network power consumption, we study the trade-off between achievable rates and network power consumption that is determined by both the observation matrix and the coding matrix. For given rate constraints, we propose an optimization problem to minimize the network power consumption by jointly designing the observation and coding matrices. To solve this NP-hard problem efficiently, we propose a low-complexity algorithm with the convex-concave procedure. Moreover, to improve performance in high noise environment, we adopt collaboration among nodes to suppress observation noises and equalize bad observations by utilizing observation diversity. Finally, simulation results illustrate the superior performance of the proposed schemes.

Due to the mobility and line-of-sight conditions, unmanned aerial vehicle (UAV) is deemed as a promising solution to sensor data collection. On the other hand, it is vital to guarantee the timeliness of information for UAV-assisted data collection. In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the UAV via uplink non-orthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into$N$time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

For future wireless networks, enormous numbers of interconnections are required, creating a multi-hop topology and leading to a great challenge on data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, it is still an open problem on the implementation of CoMAC in multi-hop wireless networks considering fading channel and resource allocation. In this paper, we propose multi-layer CoMAC (ML-CoMAC) by combining CoMAC and orthogonal communication to compute functions in the multi-hop network. Firstly, to make the multi-hop network more tractable, we reorganize it into a hierarchical network with multiple layers that consists of subgroups and groups. Then, in the hierarchical network, the implementation of ML-CoMAC is given by computing and communicating subgroup and group functions over layers, where CoMAC is applied to compute each subgroup function and orthogonal communication is adopted for each group to obtain the group function. The general computation rate is derived and the performance is further improved through time allocation and power control. The closed-form solutions to optimization problems are obtained, which suggests that orthogonal communication and existing CoMAC schemes are generalized.

Time-division-duplexing massive multiple-input multiple-output (MIMO) systems estimate the channel state information (CSI) by leveraging the uplink-downlink channel reciprocity, which is no longer valid when the mismatch arises from the asymmetric uplink and downlink radio frequency (RF) chains. Existing works treat the reciprocity mismatch as constant for simplicity. However, the practical RF chain consists of nonlinear components, which leads to nonlinear reciprocity mismatch. In this work, we examine the impact and the calibration approach of the nonlinear reciprocity mismatch in massive MIMO systems. To evaluate the impact of the nonlinear mismatch, we first derive the closed-form expression of the ergodic achievable rate. Then, we analyze the performance loss caused by the nonlinear mismatch to show that the impact of the mismatch at the base station (BS) side is much larger than that at the user equipment side. Therefore, we propose a calibration method for the BS. During the calibration, polynomial function is applied to approximate the nonlinear mismatch factor, and over-the-air training is employed to estimate the polynomial coefficients. After that, the calibration coefficients are computed by maximizing the downlink achievable rate. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

With the rapidly increasing scale and complexity, millimeter-wave communication systems are getting more powerful, but are also less reliable. Due to hardware characteristics and connecting structures, millimeter-wave communication systems may experience device faults which result in performance degradation or even failure. Therefore, system diagnosis techniques for fault detection and localization are essential to system monitoring and maintenance. In this paper, two compressed sensing based diagnosis techniques are proposed, which can jointly detect and locate faulty antennas, phase shifters, and RF chains. The first technique is applicable to the diagnosis in a multipath-free environment where the channel state information can be obtained based on spatial coordinates. Furthermore, we propose the second technique that does not require the channel state information, which is suitable for outdoor online diagnosis in a multipath scattering environment. Finally, numerical simulations show that the proposed techniques can jointly detect all faulty devices with high probability.

In this paper, we propose a codebook for the baseband of hybrid beamforming systems to reduce the feedback bits. The baseband codebook is designed with the long-term information, so that the space distribution of codewords in proposed codebook is same as the equivalent channel between baseband and users. Then we analyze the performance of the proposed baseband codebook. We provide the rate gap between the ideal case with the perfect equivalent channel and the practical case with quantized equivalent channel from the proposed baseband codebook. Finally, a lower bound of the required number of feedback bits is derived to limit the rate gap within a constant value.

In large-scale wireless sensor networks (WSN), a large number of spatially dispersed sensors and distributed signal estimation schemes provide ubiquitous sensing. However, low-cost sensors are insufficient to support conventional distributed estimation schemes, since the channel training process causes an enormous resource consumption in the large-scale WSN. This paper proposes a distributed blind estimation scheme that consists of two components: stochastic coding and statistical inference. The stochastic coding turns the desired vector signal into statistical parameters to govern the quantized symbols. At the fusion center (FC), statistical inference based on unsupervised clustering algorithms is utilized to recover the vector signal. The channel information is not required in the proposed distributed estimation. Besides, we investigate the asymptotic properties of the estimation error. Simulation results demonstrate the effectiveness of the proposed blind estimation scheme.

Federated learning provides a communication- efficient training process via alternating between local training and averaging updated local model. Nevertheless, it requires perfectly acquisition of the model which is hard to achieve in wireless communication practically, and the noise will cause serious effect on federated learning. To tackle this challenge, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in communication steps, we first formulate the problem as the parallel optimization for each node under worst-case model. We utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme, due to the unavailable maxima noise condition and non-convex issue of the objective function. In addition, the convergence rate of proposed design are analyzed from a theoretical point of view. Finally, the prediction accuracy improvement and loss function value reduction of the proposed design are demonstrated via simulation.

For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, the implementation of CoMAC in disorganized networks with multiple relays (hops) is still an open problem. In this paper, we combine CoMAC and orthogonal communication in the disorganized network to attain the computation of functions at the fusion center. First, to make the disorganized network more tractable, we reorganize the disorganized network into a hierarchical network with multiple layers that consists of subgroups and groups. In the hierarchical network, we propose multi-layer function computation where CoMAC is applied to each subgroup and orthogonal communication is adopted within each group. The general computation rate is derived and the performance is further improved through time allocation.

Digital beamforming (DBF) is considered as an efficient method to overcome the high propagation loss of millimeter wave (mmWave) communication, but the acquisition of channel state information (CSI) brings huge training overhead, especially in high mobility scenarios. To tackle this challenge, we consider using attitude information from motion sensors to reduce the training overhead of DBF in this paper. We first analyze the characteristics of mmWave uplink channel when the attitude of user equipment (UE) rotates, and it shows that only the precoder needs to be redesigned after the rotation. Therefore, we develop a novel attitude information aided precoding algorithm, which approaches the performance of conventional singular value decomposition (SVD) algorithm. The proposed algorithm reduces the channel estimation and feedback overhead significantly compared to the conventional one. Finally, the simulation results show that the proposed algorithms allow mmWave systems to approach their performance limits.

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs.

The next generation wireless network is expected to connect billions of nodes, which brings up the bottleneck on the communication speed for distributed data fusion. To overcome this challenge, computation over multiple access channel (CoMAC) was recently developed to compute the desired functions with a summation structure (e.g., mean, norm, etc.) by using the superposition property of wireless channels. This work aims to maximize the achievable function rate of reliable CoMAC in wireless networks. More specifically, considering channel fading and transceiver design, we derive the achievable function rate adopting the quantization and the nested lattice coding, which is determined by the number of nodes, the maximum value of messages and the quantization error threshold. Based on the derived result, the transceiver design is optimized to maximize the achievable function rate of the network. We first study a single cluster network without inter-cluster interference (ICI). Then, a multi-cluster network is further analyzed in which the clusters work in the same channel with ICI. In order to avoid the global channel state information (CSI) aggregation during the optimization, a low-complexity signaling procedure irrelevant with the number of nodes is proposed utilizing the channel reciprocity and the defined effective CSI.

Cooperative beamforming can provide significant performance improvement for relaying systems with the help of the channel state information (CSI). In time-division duplexing (TDD) mode, the estimated CSI will deteriorate due to the reciprocity mismatch. In this work, we examine the impact and the calibration of the reciprocity mismatch in relaying systems. To evaluate the impact of the reciprocity mismatch for all devices, the closed-form expression of the achievable rate is first derived. Then, we analyze the performance loss caused by the reciprocity mismatch at sources, relays, and destinations respectively to show that the mismatch at relays dominates the impact. To compensate the performance loss, a two-stage calibration scheme is proposed for relays. Specifically, relays perform the intra-calibration based on circuits independently. Further, the inter-calibration based on the discrete Fourier transform (DFT) codebook is operated to improve the calibration performance by cooperation transmission, which has never been considered in previous work. Finally, we derive the achievable rate after relays perform the proposed reciprocity calibration scheme and investigate the impact of estimation errors on the system performance. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize first-order gradient descent (GD) and do not consider the preceding iterations to gradient update which can potentially accelerate convergence. In this article, we consider momentum term which relates to the last iteration. The proposed momentum federated learning (MFL) uses momentum gradient descent (MGD) in the local update step of FL system. We establish global convergence properties of MFL and derive an upper bound on MFL convergence rate. Comparing the upper bounds on MFL and FL convergence rates, we provide conditions in which MFL accelerates the convergence. For different machine learning models, the convergence performance of MFL is evaluated based on experiments with MNIST and CIFAR-10 datasets. Simulation results confirm that MFL is globally convergent and further reveal significant convergence improvement over FL.

Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually, computation over multi-access channels (CoMAC) provides an intelligent solution by computing a desired function over the air based on the signal-superposition property of wireless channels. To improve the spectrum efficiency in conventional CoMAC, we propose the use of non-orthogonal multiple access (NOMA) for functions in CoMAC. The desired functions are decomposed into several sub-functions, and multiple sub-functions are selected to be superposed over each resource block (RB). The corresponding achievable rate is derived based on sub-function superposition, which prevents a vanishing computation rate for large numbers of nodes. We further study the limiting case when the number of nodes goes to infinity. An exact expression of the rate is derived that provides a lower bound on the computation rate. Compared with existing CoMAC, the NOMA-based CoMAC not only achieves a higher computation rate but also provides an improved non-vanishing rate. Furthermore, the diversity order of the computation rate is derived, which shows that the system performance is dominated by the node with the worst channel gain among these sub-functions in each RB.

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of multi-access channel (MAC). Due to frequency selective fading, wide-band CoMAC is more challenging and has never been studied before. In this work, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation and reconstruction of functions. To prevent a vanishing computation rate from the increase of the number of nodes, a novel sub-function allocation (SFA) of sub-carriers is derived. The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.

Mobile edge computing (MEC) plays an important role in next-generation networks. It aims to enhance processing capacity and offer low-latency computing services for Internet of Things (IoT). In this paper, we investigate a resource allocation policy to maximize the available processing capacity (APC) for MEC IoT networks with constrained power and unpredictable tasks. First, the APC which describes the computing ability and speed of a served IoT device is defined. Then its expression is derived by analyzing the relationship between task partitioning and resource allocation. Based on this expression, the power allocation solution for the single-user MEC system with a single subcarrier is studied and the factors that affect the APC improvement are considered. For the multiuser MEC system, an optimization problem of APC with a general utility function is formulated and several fundamental criteria for resource allocation are derived. By leveraging these criteria, a binary-search water-filling algorithm is proposed to solve the power allocation between local CPU and multiple subcarriers, and a suboptimal algorithm is proposed to assign the subcarriers among users. Finally, the validity of the proposed algorithms is verified by Monte Carlo simulation.

Distributing data aggregation through multiple access channel (MAC) has been challenging in large wireless networks. In order to tackle the challenge, a computing over the MAC (CP-MAC) scheme has been proposed as a promising communication-computation integrated way for function-centric networks. In this paper, we analyze the performance of the CP-MAC scheme, compared with the traditional communication-computation separated way, i.e., a communicating over the MAC (CM-MAC) scheme. Function-centric wireless networks are considered, where the fusion center (FC) does not need the individual data of each node but only the target function. We begin with the ideal uniform-MAC scenarios, where the CP-MAC scheme is always better than the CM-MAC scheme. Then, practical non-uniform MAC scenarios are studied for both homogeneous networks with Rayleigh fading and heterogeneous networks with a different path loss. Closed-form expressions of the achievable function rate are provided using the asymptotic theory of ordered statistics. It is found that the CP-MAC scheme is not always superior to the CM-MAC scheme. Simulation results are provided to verify and illustrate our derived results.

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of wireless channel. Due to frequency-selective fading, wide-band CoMAC is more challenging and has never been studied before. In this paper, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation, and reconstruction of functions. An achievable rate without any adaptive resource allocation is derived. To prevent a vanishing computation rate from the increase in the number of nodes, a novel sub-function allocation of sub-carriers is derived. Furthermore, we formulate an optimization problem considering power allocation. A sponge-squeezing algorithm adapted from the classical water-filling algorithm is proposed to solve the optimal power allocation problem. The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.

To support future IoT networks with dense sensor connectivity, a technique called over-the-air computation (Air-Comp) was recently developed to enable a data-fusion center to receive a desired function (e.g., mean value) of sensing data from concurrent sensor transmissions. This is made possible by exploiting the superposition property of a multi-access channel. This work aims at further developing AirComp for next-generation multi-antenna multi-modal sensor networks where a multi-modal sensor monitors multiple environmental parameters such as temperature, pollution and humidity. To be specific, we design beamforming techniques for AirComp of multiple functions, each corresponding to a particular sensing-data type. Given the objective of minimizing sum mean-squared error of computed functions, the optimization of receive beamforming for multi-function AirComp is a NP-hard problem. The approximate problem based on tightening transmission-power constraints, however, is shown to be solvable using differential geometry. The solution is proved to be the weighted centroid of points on a Grassmann manifold, where each point represents the subspace spanned by the channel matrix of a sensor. Simulation results demonstrate the effectiveness of the proposed solution.

With the application of large-scale antenna systems in upcoming 5G networks, the overhead of channel measurement and feedback explodes exponentially. This paper aims to investigate an effective conversion method to converse the channel covariance from uplink to downlink for uniform planar array (UPA) and avoid the unnecessary overhead in FDD systems. First, a multiuser channel model is established according to the geometric structure of UPA. Then, by analyzing the relationship between the downlink channel covariance matrix (DCCM) from the uplink channel covariance matrix (UCCM), a general estimation algorithm is proposed for UPA. To reduce the computation complexity, a simple Kronecker algorithm is derived, which decomposes the DCCM of UPA to the DCCMs of the horizontal and vertical channel responses. Finally, both two algorithms are applied to the statistical beamforming algorithm in a multiuser FDD system with UPA. The Monte Carlo simulation verifies their validity.

Multipath transmission control protocol (MPTCP) allows TCP (transmission control protocol) connections to operate across multiple paths concurrently to help improve resource utilization as well as connection robustness. Existing congestion algorithms for MPTCP are mainly loss-based, thus leading to severe performance degradation in wireless environment and buffer bloat on network devices. In this paper, to get rid of these shortcomings, we modify a congestion based TCP congestion control algorithm (bottleneck bandwidth and round-trip propagation time, or BBR) and propose a new congestion control algorithm for MPTCP based on bottleneck estimation, whose name is weighted BBR (wBBR). We introduce weight factors to subflows in MPTCP flows to control convergence bandwidth of each subflow, and then propose an iterative algorithm to lead the network to a fairer convergence when BBR/wBBR flows sharing bottleneck links. Simulation results show that our design has better performance in terms of bottleneck fairness compared with other MPTCP congestion control algorithms. And of course, it inherits the advantages of the BBR algorithm - let the network work at the point with max bandwidth and min queue.

Over-the-air computation combines communication and computation efficiently by utilizing the superposition property of wireless channels, when Internet of Things (IoT) networks focus more on the computed functions than the individual messages. In this paper, we study the computation of multiple linear functions of Gaussian sources over-the-air using antenna arrays at both the IoT devices and the IoT access point (AP). The key challenges in this paper are the intranode interference of multiple functions, the nonuniform fading between different IoT devices and the massive channel state information (CSI) required at the IoT AP. We propose a novel transmitter design at the IoT devices with zero-forcing beamforming to cancel the intranode interference and uniform-forcing power control to compensate the nonuniform fading. In order to avoid massive CSI requirement, receive antenna selection is adopted at the IoT AP and a corresponding signaling procedure is proposed utilizing the “OR” property of the wireless channel. The performance of the proposed transceiver design is analyzed. The closed-form expression for the mean squared function error (MSFE) outage is derived. Due to the complexity of the expression, an asymptotic analysis of the MSFE outage is further provided to demonstrate the diversity order in terms of the transmit power constraint and the number of IoT devices. Simulation results are presented to show the performance of the proposed design.

For an energy harvesting wireless network (EHWN), power oscillation will occur in uplink signal‐to‐interference‐plus‐noise ratio‐based power control if some energy‐non‐supported nodes exist. Power oscillation will destroy power control algorithm's convergence and influence the system's stabilisation no matter the EHWN is feasible or infeasible. Unfortunately, existing algorithms cannot avoid the power oscillation. Therefore, the authors propose a new distributed algorithm which contains a soft removal mechanism to solve this problem in this study. Some energy harvesting nodes should be removed softly both in terms of their energy state information and channel state information. The convergence of the authors’ proposed algorithm can be guaranteed and power oscillation can be avoided in both feasible and infeasible EHWN. Simulation results verify their analysis and show that their algorithm brings less outage ratio than other algorithms.

A transmission scheme's energy efficiency (EE) is closely related to the number of active transmit antennas. Activating more transmit antennas can reduce transmit power at the expense of higher circuit power. This trade-off may become more complicated with cooperative diversity considered. In this paper, we consider a dual-hop amplify-and-forward (AF) relay network and its EE performance is analyzed when different transmission schemes are deployed. For each scheme, the analytical expression for the probability density function (PDF) of receive signal-to-noise ratio (SNR) at the destination is derived. The corresponding spectrum efficiency (SE) and EE performance of system are also evaluated. We further discuss the impact of total antenna number, target SE and large-scale fading on the system's EE based on numerical results.

In this paper, we study the power control problem for energy-harvesting wireless network (EHWN). In an EHWN, if there are some nodes whose energy- harvesting rate is less than its transmit power, power oscillation will be caused even when the system is feasible. In order to avoid the power oscillation, we take energy-harvesting node's energy status, i.e., energy-harvesting rate, transmit power and stored energy, into account and present a distributed power control algorithm. In our algorithm, some energy-harvesting nodes should be soft removed according to their inappropriate channel and energy status. Theoretical analysis, which is verified by simulation results, shows that our algorithm can avoid power oscillation, converge to a unique fixed point and bring less outage in both feasible and infeasible systems.

This paper considers the model that each base station (BS) is connected to a central processor (CP) via a noiseless and rate-limited backhaul link. The theoretical capacity of this model is still left open. In our work, we use data splitting to explore the achievable rate region under given capacities of backhaul links. The performance of the data splitting is analyzed, and the optimal power allocation can be derived by solving the Karush-Kuhn-Tucker (KKT) conditions. An optimization method is also used to obtain the boundary of achievable rate region instead of complex exhaustive search. In the numerical simulations, the boundary of achievable rate region is presented. The result reveals that the rate region of the proposed scheme is superior to that of joint decoding scheme and Interference Channel (IC). The sum rate of our proposed scheme is also larger than that of the other two schemes.

This article studies the performance of the cooperative system where each base station (BS) is connected to a central processor (CP) via a noiseless and rate‐limited backhaul link. The single‐user compress‐and‐forward scheme is deployed at the BSs, and the problem for jointly setting the quantisation noise and the transmit power levels is left open. The authors formulate the weighted sum‐rate (WSR) problem of optimising quantisation and transmit power levels for K‐users and B‐BSs situation. However, the problem is non‐convex and difficult to be solved directly by conventional convex methods. In this work, an algorithm is proposed to solve the optimisation problem and achieve its global optimality. More importantly, the authors reveal the reason for the effect of the quantisation levels on the performance of cooperative system. Lower and upper bounds of quantisation levels are derived under the given signal to interference noise ratio (SINR) requirements. The lower and upper bounds are then demonstrated to be rather close to the numerical results. It is also numerically shown that the proposed algorithm can achieve the global optimality and is robust with respect to different multi‐user situations.

This paper considers the model that two mobile users communicate with a central processer (CP) via two base stations (BSs). The BSs are connected to the CP via orthogonal finite-capacity links. The theoretical capacity of this model is still an open problem. For given capacity of backhaul links, we use private message, common message and joint decoding message to explore a new achievable rate. We also propose an optimization method to obtain the boundary. A better achievable rate region is acquired by our proposed scheme, and optimal power allocation for private message, common message and joint decoding message is derived. In our simulations, the results illustrate how the capacity of backhaul links determines the power allocation of private message, common message and joint message.

An important problem in channel estimation of time-varying channels is how to reduce the influence of the channels time variation on the channel estimation error. And previous works give us some hints in a time-varying Gauss-Markov Rayleigh fading channel by using both the training sequences at the each boundaries of a data sequence to train the channels in-between, while traditionally a training sequence is only intended for the training of the upcoming channels. In this paper, by analyzing the source and expression of the channel estimation, we compare our error model with the model given in [11] and we find that our strategy outperforms it in both estimation quality and capacity lower bound. And we reach the conclusion that the estimation error model in [11] is a special case of our unified error model in high SNR scenario.

We study the uplink Multi-Cell Processing (MCP) model where each base station (BS) is connected to a centralized processor (CP) via a noiseless backhaul link with fixed and finite capacity. User data is decoded in the CP. In this paper, a fixed-order Successive Interference Cancellation (SIC) decoding scheme is adopted in the CP and the rate region is also derived. Further, we extend the MCP model with fixed-order SIC scheme to include the constraint that each user has the requirement of signal-to- interference-and-noise ratio (SINR). We analyze the option of quantized levels and give the theoretical lower bound and upper bound of the quantized levels. The analysis reveals that the quantized levels can affect whether each user uses its own maximized power to achieve the SINR requirement or not. Finally, numerical simulation shows the lower bound and the upper bound are very closed to the theoretical results.

Increasing the number of transmit antennas can improve the diversity gain of the system, and accordingly reduce the transmit power dissipation. However, the high circuit power dissipation incurred cannot be ignored. Generalised selection combining, which could provide a certain spatial diversity in the transmit diversity systems, performs a good balance between system performance and practical implementation cost. In this study, the authors propose an energy efficient generalised selection combining (EE‐GSC) scheme which obtains improved transmitter energy efficiency (EE) by providing a best tradeoff between the diversity gain and the circuit power dissipation of multiple antennas. Based on the classical results of order statistics, a theoretical analysis of EE‐GSC performance is carried out in detail over Rayleigh fading channels. Based on this analysis, the average number of active branches as well as the average power dissipation of the proposed scheme is also derived. Numerical results are also given to further illustrate the EE performance of the proposed scheme.

In this paper, we present and analyze utilizing generalized selection combining (GSC) scheme to enhance the physical layer (PHY) security of a wireless communication system consisting of a single antenna transmitter, a multi-antennas receiver and a multi-antennas eavesdropper. We consider a practical scenario where GSC scheme is applied to the receiver considering both the complexity and the energy dissipation while maximal ratio combining (MRC) scheme is applied to the eavesdropper in order to maximize its instantaneous signal to noise ratio (SNR). This work bridges the gap between the existing works utilizing MRC scheme and transmit antenna selection (TAS) scheme to enhance the PHY security. Closed-form expressions for both the probability of non-zero secrecy capacity and the exact secrecy outage probability are derived over Rayleigh fading channels. The security capacity performances are also shown and analyzed through numerical results. The impacts of the number of selected branches, the average SNR of transmitter's channel and eavesdropper's channel are discussed.

In this work, we propose a distributed energy efficiency (EE) optimization scheme for cooperative virtual multiple input and multiple output (V-MIMO) system. User equipments (UEs) with single antenna form cooperative V-MIMO groups to achieve their target spectral efficiency (SE) and improve their EE in a distributed manner. We decompose the scheme into two sections. Firstly, we deduce closed-form expressions of the optimal power and target SE allocations for each UE on each RB within a V-MIMO group. Then, based on this deduced expressions, a coalition formation game is utilized to choose a proper set of UEs to form V-MIMO groups. A convergent iteration of merge-split operations is adopted according to the Pareto order of UEs' EE. Simulation results show that this proposed scheme increases the EE of UEs, especially when the number of UEs is large and their target SE is high.

Over the past decades, spectral efficiency has attracted many attentions and has a mature development. However, in recent years, energy scarcity problem is more and more urgent and the concern on energy saving has received significant attentions. In the paper, we share a new light on energy saving problem of minimizing the number of active base stations (BSs) with guarantee of users' rate, and formulate the problem as an integer programming problem. We prove that the problem is NP-hard and no algorithms can obtain optimal solution in polynomial time complexity. In order to solve the problem efficiently, an iterative minimal set cover (IMSC) algorithm is proposed. The IMSC algorithm supposes all the BSs are active initially and executes a greedy algorithm iteratively to turn off part of BSs into sleep mode. Finally, we develop the simulation platform. The numerical results prove that our proposed energy saving algorithm outperforms other algorithms.

Soft fractional frequency reuse (SFR) is adopted as a main inter-cell interference coordination technique in the orthogonal frequency division multiplexing access(OFDMA) networks. In the paper, unlike the fixed power ratio configuration in other published papers, an adaptive power ratio updating in SFR scheme (APR-SFR) is explored. In each time slot, users measure the interference form other cells and report the signal to interference plus noise ratio to the associated cell. Then each base station updates the power ratio of SFR scheme adaptively and distributively based on the current interference situation to maximize the sum-rate of cell. The convergency characteristic of APR-SFR scheme is discussed and we prove APR-SFR is convergent with a proper updating ratio. Finally, we develop a LTE simulation platform with 7 cells to evaluate the proposed APR-SFR algorithm. The numerical results show that APR-SFR algorithm is flexible for different number of users and can get the better system performance than other algorithms.

In this study, the authors discuss energy‐efficient power allocation for training‐based multiple‐input multiple‐output (MIMO) system with and without feedback. Firstly, they consider the non‐feedback MIMO system, where data power is equally allocated to transmit antennas. With pilot power fixed, the energy efficiency (EE)‐optimal data power is derived based on the pseudo‐concavity of the EE expression. When data power and pilot power are both variable, a convergent alternating optimisation algorithm is proposed to obtain EE‐suboptimal data power and pilot power. Then, they consider the feedback MIMO system where data power is water‐filling allocated to transmit antennas. Although the effective transmit antennas number at this scenario is discrete, they propose an algorithm to calculate it and its corresponding EE‐optimal data power with pilot power fixed. When data power and pilot power are both variable, a similar convergent alternating optimisation algorithm is given. The numerical results demonstrate the EE performance of the training‐based MIMO system with and without feedback. The impacts of multi‐antennas configuration, circuit power and block length are also shown and analysed.