Existing semantic communication schemes primarily focus on single-hop scenarios, overlooking the challenges of multi-hop wireless image transmission. As semantic communication is inherently lossy, distortion accumulates over multiple hops, leading to significant performance degradation. To address this, we propose the multi-hop parallel image semantic communication (MHPSC) framework, which introduces a parallel residual compensation link at each hop against distortion accumulation. To minimize the associated transmission bandwidth overhead, a coarse-to-fine residual compression scheme is designed. A deep learning-based residual compressor first condenses the residuals, followed by the adaptive arithmetic coding (AAC) for further compression. A residual distribution estimation module predicts the prior distribution for the AAC to achieve fine compression performances. This approach ensures robust multi-hop image transmission with only a minor increase in transmission bandwidth. Experimental results confirm that MHPSC outperforms both existing semantic communication and traditional separated coding schemes.

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework, abbreviated as WVSC, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, multi-frame compensation (MFC) is proposed to produce compensated current semantic frame with a multi-frame fusion attention module. With both the reference frame transmission and MFC, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC over other DL-based methods e.g. DVSC about 1 dB and traditional schemes about 2 dB in terms of PSNR.

This paper proposes a unified sparse transmission framework for coded modulation-based unsourced random access (URA) systems, referred to as sparse linear precoded URA (SLP-URA). The proposed design generalizes and unifies a variety of existing URA schemes including random spreading, sparse interleave division multiple access (IDMA), and on-off division multiple access (ODMA) as special cases of a broader design space characterized by tunable sparsity structures. To support this flexible framework, we derive a consistent generalized log-likelihood ratio (LLR)-based active user detection (AUD) method, and further propose message passing (MP)-based AUD and multi-user detection (MUD) algorithms that accommodate arbitrary sparsity patterns. An approximate low-complexity implementation is also introduced to reduce computational burden. Through rigorous symbol-level signal-to-interference-plus-noise ratio (SINR) analysis, we establish that increasing the column weight of the SLP matrix reduces SINR variance and outage probability, providing rigorous theoretical grounding for the performance gains. Simulations demonstrate that the proposed SLP-URA framework consistently achieves improved performance over conventional ODMA schemes, even when the latter are equipped with enhanced detection algorithms. These results suggest that SLP-URA provides a robust foundation for future URA system design and optimization.

The soft-output successive cancellation list (SOSCL) decoder provides a methodology for estimating the aposteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder of polar codes. However, the sequential decoding nature of SCL introduces high decoding latency to SOSCL. In this paper, we incorporate node-based fast decoding into the SO-SCL framework. After addressing the challenge of soft output extraction in special node decoding, we proposed the soft-output fast SCL (SO-FSCL) decoding algorithm, along with its log-domain implementation and hardware-friendly version. The proposed SO-FSCL decoder can be regarded as an addon extension to FSCL decoder, enabling us to autonomously choose whether to output only hard decisions like FSCL or to provide additional soft outputs. Latency and complexity analyses demonstrate that SO-FSCL can significantly reduce, for example, decoding time steps by 81.8% (with unlimited resources), the number of additions by 41.3%, and the number of comparisons by 46.4%. Meanwhile, simulation results indicate that SO-FSCL delivers almost the same soft-output performance as SO-SCL, outperforming other soft-output polar decoders, especially in scenarios involving iterative decoding.

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework with decoupled diffusion multi-frame compensation (DDMFC), abbreviated as WVSC-D, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC-D first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, DDMFC is proposed to generate compensated current semantic frame by a two-stage conditional diffusion process. With both the reference frame transmission and DDMFC frame compensation, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC-D over other DL-based methods e.g. DVSC about 1.8 dB in terms of PSNR.

Pinching antennas are a promising technology for enhancing the performance of future indoor communication systems by leveraging spatial degrees of freedom. This paper pioneers the application of pinching antenna arrays in integrated sensing and communication (ISAC) systems and investigates dynamic array partitioning strategies. To maximize the secrecy sum rate (SSR), a partitioned array optimization problem under binary constraints is formulated, while satisfying sensing performance requirements and transmit power limitations. Specifically, the antenna partitioning constraints are modeled as minimum and maximum numbers of transmit antennas, along with binary constraints determining whether each antenna element functions in transmit or receive mode. To solve the non-convex optimization problem, a beamforming algorithm integrating semidefinite relaxation, generalized Rayleigh quotient, and minimum mean square error is proposed. Then, an element-wise iterative optimization method and a deep Q-network (DQN)-based partitioning approach are respectively developed to optimize the array configuration, thereby enhancing security performance under guaranteed sensing constraints. Simulation results demonstrate that the DQN-based approach outperforms the conventional iterative optimization method. In terms of security performance, the pinching antenna array can achieve a 69.70% reduction in the number of antennas and a 30.16% saving in transmit power compared to conventional fixed-position antenna (FPA) systems. Moreover, the pinching antenna system attains a 35.72% improvement in SSR performance, surpassing traditional FPA configurations.

This paper studies a novel movable antenna (MA)-enhanced multiuser multiple-input multiple-output downlink system designed to improve wireless communication performance. We aim to maximize the average achievable sum rate through two-timescale optimization exploiting instantaneous channel state information at the receiver (I-CSIR) for receive antenna position vector (APV) design and statistical channel state information at the transmitter (S-CSIT) for transmit APV and covariance matrix design. We first decompose the resulting stochastic optimization problem into a series of short-term problems and one long-term problem. Then, a gradient ascent algorithm is proposed to obtain suboptimal receive APVs for the short-term problems for given I-CSIR samples. Based on the output of the gradient ascent algorithm, a series of convex objective/feasibility surrogates for the long-term problem are constructed and solved utilizing the constrained stochastic successive convex approximation (CSSCA) algorithm. Furthermore, we propose a planar movement mode for the receive MAs to facilitate efficient antenna movement and the development of a low-complexity primal-dual decomposition-based stochastic successive convex approximation (PDD-SSCA) algorithm, which finds Karush-Kuhn-Tucker (KKT) solutions almost surely. Our numerical results reveal that, for both the general and the planar movement modes, the proposed two-timescale MA-enhanced system design significantly improves the average achievable sum rate and the feasibility of the formulated problem compared to benchmark schemes.

This paper presents an innovative movable antenna (MA)-enhanced multi-user multiple-input multiple-output (MIMO) downlink system. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the precoding matrix and the antenna position vectors (APVs). To solve the resulting stochastic problem, we first resort to deterministic equivalent (DE) tecnology to formulate the deterministic minorizing function of the system EE and the deterministic function of each user terminal (UT)’s average achievable rate w.r.t. the transmit variables (i.e., the precoding matrix and the transmit APV) and the corresponding receive APV, respectively. Then, we propose an alternating optimization (AO) algorithm to alternatively optimize the transmit variables and the receive APVs to maximize the formulated deterministic objective functions, respectively. Finally, the above AO algorithm is tailored for the single-user scenario. Our numerical results reveal that, the proposed MA-enhanced system can significantly improve the system EE compared to several benchmark schemes based on the S-CSI and the optimal performance can be achieved with a finite size of movement regions for MAs.

This paper considers multi-view imaging in a sixth-generation (6G) integrated sensing and communication network, which consists of a transmit base-station (TBS), multiple receive base-stations (RBSs) connected to a central processing unit (CPU), and multiple extended targets. Our goal is to devise an effective multi-view imaging technique that can jointly leverage the echo signals at all the RBSs to precisely construct the image of these targets. To achieve this goal, we propose a two-phase framework. In Phase I, each RBS recovers an individual image of all the targets from its own view, which is obtained via utilizing its received signals’ sample covariance matrix to detect the grids with non-zero effective scattering intensity in the region of interest. Moreover, the shape of each grid is adjusted to conform to target geometries. In Phase II, the CPU fuses the individual images of all the RBSs to construct a higher-quality image of all the targets. To this end, we first design an edge-preserving natural neighbor interpolation (EP-NNI) method and then formulate an optimization problem to fuse the interpolated results. Extensive numerical results show that the proposed scheme significantly enhances imaging performance, facilitating high-quality environment reconstruction for future 6G networks.

This paper presents an initial investigation into the combination of integrated sensing and communication (ISAC) and massive communication, both of which are largely regarded as key scenarios in sixth-generation (6G) wireless networks. Specifically, we consider a cell-free network comprising a large number of users, multiple targets, and distributed base stations (BSs). In each time slot, a random subset of users becomes active, transmitting pilot signals that can be scattered by the targets before reaching the BSs. Unlike conventional massive random access schemes, where the primary objectives are device activity detection and channel estimation, our framework also enables target localization by leveraging the multipath propagation effects introduced by the targets. However, due to the intricate dependency between user channels and target locations, characterizing the posterior distribution required for minimum mean-square error (MMSE) estimation presents significant computational challenges. To handle this problem, we propose a hybrid message passing-based framework that incorporates multiple approximations to mitigate computational complexity. Numerical results demonstrate that the proposed approach achieves high-accuracy device activity detection, channel estimation, and target localization simultaneously, validating the feasibility of embedding localization functionality into massive communication systems for future 6G networks.

Semantic communication is emerging as an effective means of facilitating intelligent and context-aware communication for next-generation communication systems. In this paper, we propose a novel metric called Age of Incorrect Semantics (AoIS) for the transmission of video frames over multiple-input multiple-output (MIMO) channels in a monitoring system. Different from the conventional age-based approaches, we jointly consider the information freshness and the semantic importance, and then formulate a time-averaged AoIS minimization problem by jointly optimizing the semantic actuation indicator, transceiver beamformer, and the semantic symbol design. We first transform the original problem into a low-complexity problem via the Lyapunov optimization. Then, we decompose the transformed problem into multiple subproblems and adopt the alternative optimization (AO) method to solve each subproblem. Specifically, we propose two efficient algorithms, i.e., the successive convex approximation (SCA) algorithm and the low-complexity zero-forcing (ZF) algorithm for optimizing transceiver beamformer. We adopt exhaustive search methods to solve the semantic actuation policy indicator optimization problem and the transmitted semantic symbol design problem. Experimental results demonstrate that our scheme can preserve more than 50% of the original information under the same AoIS compared to the constrained baselines.

Existing semantic communications have exhibited satisfactory performance in many tasks, but secure image transmission has not been adequately investigated. In this paper, we propose a novel secure semantic image transmission (SSIT) framework over multiple-input single-output (MISO) wiretap channels. To enhance image transmission security for a legitimate semantic user (SU) while interfering with the eavesdropper (Eve), a type of beneficial semantic noise map, which is determined by both source and SU channel states, is produced by a semantic noise-aided conditional variational autoencoder (SN-CVAE) in an unsupervised manner. Furthermore, to improve the secure image reconstruction quality, we propose an efficient transmit beamformer optimization algorithm and leverage the constrained stochastic successive convex approximation (CSSCA) to solve the optimization problem. Numerical results demonstrate that our method effectively protects image information from eavesdroppers while ensuring high-fidelity image reconstruction at the legitimate receiver.

In this work, we introduce the integration of generalized low-density parity-check (GLDPC) codes with short polar component codes, termed GLDPC codes with polar component codes (GLDPC-PC). A recently proposed soft-input soft-output (SISO) decoder for polar-like codes enables effective iterative belief propagation decoding for GLDPC-PC. This SISO decoder after a post-processing exhibits little performance loss to the optimal SISO decoder when all the variable nodes have relatively low degrees. A three-step method is introduced to design protograph-based GLDPC codes. The constructed GLDPC codes are compared with 5G LDPC codes. They exhibit little performance loss in the waterfall region and possess better error floor with less iterations.

This paper investigates joint device activity detection and channel estimation for grant-free random access in Lowearth orbit (LEO) satellite communications. We consider uplink communications from multiple single-antenna terrestrial users to a LEO satellite equipped with a uniform planar array of multiple antennas, where orthogonal frequency division multiplexing (OFDM) modulation is adopted. To combat the severe Doppler shift, a transmission scheme is proposed, where the discrete prolate spheroidal basis expansion model (DPS-BEM) is introduced to reduce the number of unknown channel parameters. Then the vector approximate message passing (VAMP) algorithm is employed to approximate the minimum mean square error estimation of the channel, and the Markov random field is combined to capture the channel sparsity. Meanwhile, the expectation-maximization (EM) approach is integrated to learn the hyperparameters in priors. Finally, active devices are detected by calculating energy of the estimated channel. Simulation results demonstrate that the proposed method outperforms conventional algorithms in terms of activity error rate and channel estimation precision.

This paper investigates the impact of mechanical fluid antenna (FA) trajectory on enhancing secure wireless communication, aiming to mitigate the effects of delays caused by antenna movement. To achieve this, we jointly optimize the FA trajectory and transmit beamformer to maximize the secrecy rate. For any given trajectory, we derive the optimal transmit beamformer. Additionally, we study two typical scenarios: continuous trajectory and discrete trajectory. In the continuous trajectory design, we reformulate the time-averaged secrecy rate maximization problem into a mean squared error (MSE) minimization problem and employ the block successive upper bound minimization (BSUM) algorithm for optimization. For the discrete trajectory, we express the beamformer as a function of the FA trajectory which means the secrecy rate only depends on the FA trajectory. Then we apply successive refinement to iteratively optimize the FA position in each time slot. Numerical results demonstrate that our trajectory-aware FA design significantly enhances secure wireless communication, particularly under varying power budgets and antenna speeds.

The soft-output successive cancellation list (SO-SCL) decoder provides a methodology for estimating the a-posteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder for polar codes. However, the sequential nature of SCL decoding leads to a high decoding latency for the SO-SCL decoder. In this paper, we propose a soft-output fast SCL (SO-FSCL) decoder by incorporating node-based fast decoding into the SO-SCL framework. Simulation results demonstrate that the proposed SO-FSCL decoder significantly reduces the decoding latency without loss of performance compared with the SO-SCL decoder.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

This paper investigates joint device identification, channel estimation, and symbol detection for cooperative multi-satellite-enhanced random access, where orthogonal time-frequency space modulation with the large antenna array is utilized to combat the dynamics of the terrestrial-satellite links (TSLs). We introduce the generalized complex exponential basis expansion model to parameterize TSLs, thereby reducing the pilot overhead. By exploiting the block sparsity of the TSLs in the angular domain, a message passing algorithm is designed for initial channel estimation. Subsequently, we examine two cooperative modes to leverage the spatial diversity within satellite constellations: the centralized mode, where computations are performed at a high-power central server, and the distributed mode, where computations are offloaded to edge satellites with minimal signaling overhead. Specifically, in the centralized mode, device identification is achieved by aggregating backhaul information from edge satellites, and channel estimation and symbol detection are jointly enhanced through a structured approximate expectation propagation (AEP) algorithm. In the distributed mode, edge satellites share channel information and exchange soft information about data symbols, leading to a distributed version of AEP. The introduced basis expansion model for TSLs enables the efficient implementation of both centralized and distributed algorithms via fast Fourier transform. Simulation results demonstrate that proposed schemes significantly outperform conventional algorithms in terms of the activity error rate, the normalized mean squared error, and the symbol error rate. Notably, the distributed mode achieves performance comparable to the centralized mode with only two exchanges of soft information about data symbols within the constellation.

A wiretap channel is served as the fundamental model of physical layer security techniques, where the secrecy capacity of the Gaussian wiretap channel is proven to be achieved by Gaussian input. However, there remains a gap between the Gaussian secrecy capacity and the secrecy rate with conventional uniformly distributed discrete constellation input, e.g. amplitude shift keying (ASK) and quadrature amplitude modulation (QAM). In this paper, we propose a probabilistic shaped multilevel polar coding scheme to bridge the gap. Specifically, the input distribution optimization problem for maximizing the secrecy rate with ASK/QAM input is solved. Numerical results show that the resulting sub-optimal solution can still approach the Gaussian secrecy capacity. Then, we investigate the polarization of multilevel polar codes for the asymmetric discrete memoryless wiretap channel, and thus propose a multilevel polar coding scheme integration with probabilistic shaping. It is proved that the scheme can achieve the secrecy capacity of the Gaussian wiretap channel with discrete constellation input, and satisfies the reliability condition and weak security condition. A security-oriented polar code construction method to natively satisfies the leakage-based security condition is also investigated. Simulation results show that the proposed scheme achieves more efficient and secure transmission than the uniform constellation input case over both the Gaussian wiretap channel and the Rayleigh fading wiretap channel.

In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated source is only available at the receiver. Unlike other learnable frameworks, our approach demonstrates robustness to non-stationary source correlation, where the overlapping information between image pairs varies. Specifically, we first model the affine relationship between correlated images and leverage this model for learnable mask generation and rate-adaptive joint source-channel coding. Moreover, we also provide a warping-prediction network to remove the distortion from channel interference and affine transform. Intuitively, the observed performance improvement is largely due to focusing on the simple geometric relationship, rather than the complex joint distribution between the sources. Numerical results show that our framework achieves a 1.5 dB gain in PSNR and a 0.2 improvement in MS-SSIM, along with a significant superiority in perceptual metric, compared to state-of-the-art methods when applied to real-world samples with non-stationary correlations.

Joint source-channel coding (JSCC) is a promising paradigm for next-generation communication systems, particularly in challenging transmission environments. In this paper, we propose a novel standard-compatible JSCC framework for the transmission of images over multiple-input multiple-output (MIMO) channels. Different from the existing end-to-end AI-based DeepJSCC schemes, our framework consists of learnable modules that enable communication using conventional separate source and channel codes (SSCC), which makes it amenable for easy deployment on legacy systems. Specifically, the learnable modules involve a preprocessing-empowered network (PPEN) for preserving essential semantic information, and a precoder & combiner-enhanced network (PCEN) for efficient transmission over a resource-constrained MIMO channel. We treat existing compression and channel coding modules as non-trainable blocks. Since the parameters of these modules are non-differentiable, we employ a proxy network that mimics their operations when training the learnable modules. Numerical results demonstrate that our scheme can save more than 29% of the channel bandwidth, and requires lower complexity compared to the constrained baselines. We also show its generalization capability to unseen datasets and tasks through extensive experiments.

The multiple-input and single-output (MISO)-orthogonal frequency-division multiplexing (OFDM) systems afford low latency and high reliability for virtual reality (VR) 360 video in multi-user scenarios. Motivated by the goal of maintaining time-smoothness while holding acceptably low complexity, a crucial factor in VR video transmission, we conduct a comprehensive study that integrates the characteristics of VR video with the strategies for subcarrier assignment and power allocation. By analyzing the pre-transmitted tile-segments, the missing tile-segments, and the video frame structure, we propose two probabilistic slicing schemes (PSPs) to minimize the size of required tile-segments of VR video scenes. In time-smoothness maximization, the desired discrete encoding rate set, discrete subcarrier assignment, continuous power allocation, and fixed total power constraint make it a challenging mixed-integer nonlinear programming (MINLP) problem. Unlike the straightforward relaxation-recovery method, we firstly prove that a near-optimal recovered encoding rate is the discrete value closest to the optimal relaxed-continuous encoding rate. We then propose a Two-step Encoding Rate Maximization (TERM) method, including the relaxed-continuous sum-rate maximization and the discrete encoding rate recovery, to achieve the near-optimal subcarrier assignment and the power allocation with low complexity. Simulation results on real-world VR video dataset validate that the two PSPs can effectively minimize the number of transmitted tile-segments. The proposed TERM with PSPs can maintain time-smoothness of VR 360 video with an acceptably low level of complexity in MISO-OFDM systems.

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is investigated. To this end, an adaptive modulus (AM) encryption algorithm is first proposed. To further enhance security without requiring additional storage for new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Building upon these three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the strengths of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed specifically for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the proposed AM-SUEO-DBLTKM-RFID protocol achieves both low computational complexity and low storage overhead, making it well-suited for deployment in resource-constrained, low-cost RFID tags.

This paper investigates asynchronous multiple-input multiple-output (MIMO) massive unsourced random access (URA) in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framework segregates the data into two components, namely, preamble and coding parts, with the former being tree-coded and the latter LDPC-coded. By leveraging the dual sparsity of the equivalent channel across both codeword and delay domains (CD and DD), we develop a message-passing-based sparse Bayesian learning algorithm, combined with belief propagation and mean field, to iteratively estimate DD channel responses, TO, and delay profiles. Furthermore, by jointly leveraging the observations among multiple slots, we establish a novel graph-based algorithm to iteratively separate the superimposed channels and compensate for the phase rotations. Additionally, the proposed algorithm is applied to the flat fading scenario to estimate both TO and CFO, where the channel and offset estimation is enhanced by leveraging the geometric characteristics of the signal constellation. Extensive simulations reveal that the proposed algorithm achieves superior performance and substantial complexity reduction in both channel and offset estimation compared to the codebook enlarging-based counterparts, and enhanced data recovery performances compared to state-of-the-art URA schemes.

This paper investigates joint device identification, channel estimation, and symbol detection for LEO satellite-enabled grant-free random access systems, specifically targeting scenarios where remote Internet-of-Things (IoT) devices operate without global navigation satellite system (GNSS) assistance. Considering the constrained power consumption of these devices, the large differential delay and Doppler shift are handled at the satellite receiver. We firstly propose a spreading-based multi-frame transmission scheme with orthogonal time-frequency space (OTFS) modulation to mitigate the doubly dispersive effect in time and frequency, and then analyze the input-output relationship of the system. Next, we propose a receiver structure based on three modules: a linear module for identifying active devices that leverages the generalized approximate message passing algorithm to eliminate inter-user and inter-carrier interference; a non-linear module that employs the message passing algorithm to jointly estimate the channel and detect the transmitted symbols; and a third module that aims to exploit the three dimensional block channel sparsity in the delay-Doppler-angle domain. Soft information is exchanged among the three modules by careful message scheduling. Furthermore, the expectation-maximization algorithm is integrated to adjust phase rotation caused by the fractional Doppler and to learn the hyperparameters in the priors. Finally, the convolutional neural network is incorporated to enhance the symbol detection. Simulation results demonstrate that the proposed transmission scheme boosts the system performance, and the designed algorithms outperform the conventional methods significantly in terms of the device identification, channel estimation, and symbol detection.

In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly, the deep learning (DL)-based joint source-channel coding (JSCC) is integrated into the classical separate source-channel coding (SSCC) to transmit the images via the combination of semantic stream and image stream from DL networks and SSCC respectively, which we name as parallel-stream transmission. The positive coding gain stems from the sophisticated design of the JSCC encoder, which leverages the residual information neglected by the SSCC to enhance the learnable image features. Furthermore, a conditional rate adaptation mechanism is introduced to adjust the transmission rate of semantic stream according to residual, rendering the framework more flexible and efficient to bandwidth allocation. We also design a dynamic stream aggregation strategy at the receiver, which provides the composite framework with more robustness to signal-to-noise ratio (SNR) fluctuations in wireless systems compared to a single conventional codec. Finally, the proposed framework is verified to surpass the performance of both traditional and DL-based competitors in a large range of scenarios and meanwhile, maintains lightweight in terms of the transmission and computational complexity of semantic stream, which exhibits the potential to be applied in real systems.

This paper explores the fundamental limits of unsourced random access (URA) with a random and unknown number$\mathrm{K}_{a}$of active users in MIMO quasi-static Rayleigh fading channels. First, we derive an upper bound on the probability of incorrectly estimating the number of active users. We prove that it exponentially decays with the number of receive antennas and eventually vanishes, whereas reaches a plateau as the power and blocklength increase. Then, we derive non-asymptotic achievability and converse bounds on the minimum energy-per-bit required by each active user to reliably transmit$J$bits with blocklength$n$. Numerical results verify the tightness of our bounds, suggesting that they provide benchmarks to evaluate existing schemes. The extra required energy-per-bit due to the uncertainty of the number of active users decreases as$\mathbb{E}[\mathrm{K}_{a}]$increases. Compared to random access with individual codebooks, the URA paradigm achieves higher spectral and energy efficiency. Moreover, using codewords distributed on a sphere is shown to outperform the Gaussian random coding scheme in the non-asymptotic regime.

This article proposes a novel media access control (MAC) mechanism, called the robust preamble-based MAC mechanism (R-PMAC), which can be applied to power line communication (PLC) networks in the context of the Industrial Internet of Things (IIoT). Compared with other MAC mechanisms, such as P-MAC and the MAC layer of IEEE1901.1, R-PMAC has higher networking speed. Besides, it supports whitelist authentication and functions properly in the presence of data frame loss. First, we outline three basic mechanisms of R-PMAC, containing precise time difference calculation, preambles generation, and short ID allocation. Second, we elaborate its networking process of single layer and multiple layers. Third, we illustrate its robust mechanisms, including collision handling and data retransmission. Moreover, a low-cost hardware platform is established to measure the time of connecting hundreds of PLC nodes for the R-PMAC, P-MAC, and IEEE1901.1 mechanisms in a real power line environment. The experiment results show that R-PMAC outperforms the other mechanisms by achieving a 50% reduction in networking time. These findings indicate that the R-PMAC mechanism holds great potential for quickly and effectively building a PLC network in actual industrial scenarios.

Integrated satellite-aerial-terrestrial relay networks (ISATRNs) have been considered as a promising architecture for next-generation networks, where high altitude platform (HAP) is pivotal in these integrated networks. In this paper, we introduce a novel model for HAP-based ISATRNs with mixed FSO/RF transmission mode, which incorporates unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) to dynamically reconfigure the propagation environment and fulfill the massive access requirements of ground users. Our aim is to maximize the system ergodic rate by joint optimizing the UAV trajectory, RIS phase shift, and active transmit beamforming matrix under the constraint of UAV energy consumption. To solve this intractable problem, a deep reinforcement learning (DRL)-based energy efficient optimization scheme by utilizing an improved long short-term memory (LSTM)-double deep Q-network (DDQN) framework is proposed. Numerical results demonstrate the superiority of our proposed algorithm over the traditional DDQN algorithm, on single-step exploration average reward values and other evaluation metrics.

This paper investigates the unsourced random access (URA) problem with a massive multiple-input multiple-output receiver that serves wireless devices in the near-field of radiation. We employ an uncoupled transmission protocol without appending redundancies to the slot-wise encoded messages. To exploit the channel sparsity for block length reduction while facing the collapsed sparse structure in the angular domain of near-field channels, we propose a sparse channel sampling method that divides the angle-distance (polar) domain based on the maximum permissible coherence. Decoding starts with retrieving active codewords and channels from each slot. We address the issue by leveraging the structured channel sparsity in the spatial and polar domains and propose a novel turbo-based recovery algorithm. Furthermore, we investigate an off-grid compressed sensing method to refine discretely estimated channel parameters over the continuum that improves the detection performance. Afterward, without the assistance of redundancies, we recouple the separated messages according to the similarity of the users’ channel information and propose a modifiedK-medoids method to handle the constraints and collisions involved in channel clustering. Simulations reveal that via exploiting the channel sparsity, the proposed URA scheme achieves high spectral efficiency and surpasses existing multi-slot-based schemes. Moreover, with more measurements provided by the overcomplete channel sampling, the near-field-suited scheme outperforms its counterpart of the far-field.

Reconfigurable intelligent surface (RIS) has been recently regarded as a disruptive candidate technology for enabling next generation wireless communication. It can establish favorable propagation environment to facilitate low-power and spectrally efficient data transmission, possessing attractive potential to support massive access. However, the required activity detection and channel estimation for RIS-assisted massive access is quite challenging due to the passive nature of the conventional reflecting elements. To this end, this paper considers massive access for RIS-assisted communication systems with semi-passive elements, which can operate in sensing mode for receiving signals. Then, by exploiting the sparsity of the RIS-BS channel in the virtual angular domain as well as the sporadic transmission of massive connectivity, we formulate the joint activity detection and channel estimation as a special bilinear recovery problem, which is a combination of sparse matrix factorization, compressed sensing (CS)-based generalized multiple measurement vector (GMMV) problem and matrix completion. Furthermore, we propose a novel hierarchical message passing-based algorithm to address the problem, in which approximate message passing (AMP)-based approximations are adopted to reduce the computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm and its superior performance compared with state-of-the-art baseline schemes.

The reconfigurable intelligent surface (RIS) has drawn considerable attention for its ability to enhance the performance of not only the wireless communication but also the indoor localization with low-cost. This paper investigates the performance limits of the RIS-based near-field localization in the asynchronous scenario, and analyzes the impact of each part of the cascaded channel on the localization performance. The Fisher information matrix (FIM) and the position error bound (PEB) are derived. Besides, we also derive the equivalent Fisher information (EFI) for the position-related intermediate parameters. Enabled by the derived EFI, we verify that both the ranging and bearing information of the user can be obtained when the near-field model is considered for the RIS-User equipment (UE) part of the channel, while only the direction of the UE can be inferred in the far-field scenario. This result is well known in the scenario that the curvature of arrival (COA) is directly sensed by the traditional active large-scale array, and we prove that it still holds when the COA is sensed passively by the large RIS. For the base station (BS)-RIS part of the channel, we reveal that this part of the channel determines the type of the gain provided by the BS antenna array. Besides, in the single-carrier, single snapshot case, it requires both the BS-RIS and the RIS-UE part of the channel works in the near-field scenario to localize the UE. We also show that the well-known focusing control scheme for RIS, which maximizes the received SNR, is not always a good choice and may degrade the localization performance in the asynchronous scenario. The simulation results validate the analytic work. The impact of the focusing control scheme on the PEB performances under synchronous and asynchronous conditions is also investigated.

The robustness of filter bank multi-carrier (FBMC) against delays is appealing for asynchronous grant-free massive connectivity systems. In this paper, we study the joint activity detection, delay and channel estimation (JADDCE) problem for filter bank multi-carrier (FBMC)-based uplink massive connectivity with asynchronous transmission and frequency-selective fading (FSF) channels. We formulate JADDCE as a compressed sensing (CS) problem to fully exploit the sparsity structure and propose an efficient algorithm based on generalized approximate message passing (GAMP) to solve it. Besides, since parameters such as noise variance and activity probability may not be perfectly known by the receiver, we introduce the expectation maximization (EM) method into the algorithm and derive the updating rules of the unknown parameters. We also utilize the analysis framework based on average mutual information (AMI) to find theoretical upper-bound of the channel estimation performance. Simulation results show satisfying detection and estimation performance of the proposed algorithm under FSF channels. Besides, the channel estimation performance can approach the theoretical upper-bound.

Though achieving marvelous progress in various scenarios, existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading channels, neglecting the widely-used multiple-input multiple-output (MIMO) channels, which hinders the application into practical systems. One common solution to combat MIMO fading is to utilize feedback MIMO channel state information (CSI). In this paper, we incorporate MIMO CSI into system designs from a new perspective and propose the learnable CSI fusion semantic communication (LCFSC) framework, where CSI is treated as side information by the semantic extractor to enhance the semantic coding. To avoid feature fusion due to abrupt combination of CSI with features, we present a non-invasive CSI fusion multi-head attention module inside the Swin Transformer. With the learned attention masking map determined by both source and channel states, more robust attention distribution could be generated. Furthermore, the percentage of mask elements could be flexibly adjusted by the learnable mask ratio, which is produced based on the conditional variational interference in an unsupervised manner. In this way, CSI-aware semantic coding is achieved through learnable CSI fusion masking. Experiment results testify the superiority of LCFSC over traditional schemes and state-of-the-art Swin Transformer-based semantic communication frameworks in MIMO fading channels.

In this paper, a novel random scattering transceiver design for unsourced random access (URA) is investigated. Distinctive from the state-of-the-art such as coded compressed sensing (CCS) kind and interleaving division multiple access (IDMA) kind, the data is split into two portions and the back-and-forth interleaving/de-interleaving for soft information update is dismantled. A portion of the data is encoded by compressed sensing (CS) and the rest is encoded by convolutional code LDPC (CC-LDPC). Based on the principle of probabilistic data association (PDA), a receiver with a turbo PDA multi-user detector (MUD) is designed by the random scattering transmission. Meanwhile, the proposed turbo PDA detector can also cooperate with the CC-LDPC decoder. A sliding window decoding structure is embedded in the proposed receiver. Moreover, given that permeable collision in URA can undermine the system performance, this paper treats collision as interference and elaborates different slot-wise MUD signal models combining the feature of random scattering, based on which a collision resolution procedure is proposed. Empirically, the proposed scheme shows faster system performance convergence and more accurate MUD performance than the IDMA kind. Numerical results with fair comparisons validate the viability of the proposed scheme.

This paper studies a novel movable antenna (MA)-enhanced multiple-input multiple-output (MIMO) system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of wireless communications. We aim to maximize the achievable rate by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To solve the resulting design problem, we develop a constrained stochastic successive convex approximation (CSSCA) algorithm applicable for the general movement mode. Furthermore, we propose two simplified antenna movement modes, namely the linear movement mode and the planar movement mode, to facilitate efficient antenna movement and reduce the computational complexity of the CSSCA algorithm. Numerical results show that the considered MA-enhanced system can significantly improve the achievable rate compared to conventional MIMO systems employing uniform planar arrays (UPAs) and that the proposed planar movement mode performs closely to the performance upper bound achieved by the general movement mode.

This paper investigates the multiple-input-multiple-output (MIMO) massive unsourced random access in an asynchronous orthogonal frequency division multiplexing (OFDM) system, with both timing and frequency offsets (TFO) and non-negligible user collisions. The proposed coding framework splits the data into two parts encoded by sparse regression code (SPARC) and low-density parity check (LDPC) code. Multistage orthogonal pilots are transmitted in the first part to reduce collision density. Unlike existing schemes requiring a quantization codebook with a large size for estimating TFO, we establish a graph-based channel reconstruction and collision resolution (GB-CR2) algorithm to iteratively reconstruct channels, resolve collisions, and compensate for TFO rotations on the formulated graph jointly among multiple stages. We further propose to leverage the geometric characteristics of signal constellations to correct TFO estimations. Exhaustive simulations demonstrate remarkable performance superiority in channel estimation and data recovery with substantial complexity reduction compared to state-of-the-art schemes.

This paper investigates joint device identification, channel estimation, and signal detection for LEO satellite-enabled grant-free random access, where a multiple-input multiple-output (MIMO) system with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link (TSL). We divide the receiver structure into three modules: first, a linear module for identifying active devices, which leverages the generalized approximate message passing (GAMP) algorithm to eliminate inter-user interference in the delay-Doppler domain; second, a non-linear module adopting the message passing algorithm to jointly estimate channel and detect transmit signals; the third aided by Markov random field (MRF) aims to explore the three dimensional block sparsity of channel in the delay-Doppler-angle domain. The soft information is exchanged iteratively between these three modules by careful scheduling. Furthermore, the expectation-maximization algorithm is embedded to learn the hyperparameters in prior distributions. Simulation results demonstrate that the proposed scheme outperforms the conventional methods significantly in terms of activity error rate, channel estimation accuracy, and symbol error rate.

Multinode communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet of Things (IoT) scenarios. However, its huge amounts of data flows and inflexibility for task extension have triggered the urgent requirement of communication-efficient distributed data transmission frameworks. In this article, inspired by the great superiorities on bandwidth reduction and task adaptation of semantic communications, we propose a federated learning (FL)-based semantic communication (FLSC) framework for multitask distributed image transmission with IoT devices. FL enables the design of independent semantic communication link of each user while further improves the semantic extraction and task performance through global aggregation. Each link in FLSC is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator for coarse-to-fine semantic extraction and meaning translation according to specific tasks. In order to extend the FLSC into more realistic conditions, we design a channel state information-based multiple-input–multiple-output transmission module to combat channel fading and noise. Simulation results show that the coarse semantic information can deal with a range of image-level tasks. Moreover, especially in low signal-to-noise ratio (SNR) and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional scheme, e.g., about 10 peak SNR gain in the 3-dB channel condition.

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

This paper considers the massive random access problem in multiple-input multiple-output (MIMO) quasi-static Rayleigh fading channels. Specifically, we derive achievability and converse bounds on the minimum energy-per-bit required for each active user to transmit$J$bits with blocklength$n$, power$P$, and$L$receive antennas under a per-user probability of error (PUPE) constraint, in the cases with and without a priori channel state information at the receiver (CSIR and no-CSI). In the case of no-CSI, we consider both the settings with and without the knowledge of the number$K_{a}$of active users at the receiver. Numerical evaluation shows that the gap between achievability and converse bounds is less than 2.5 dB for the CSIR case and less than 4 dB for the no-CSI case in most considered regimes. Under the condition that the distribution of$K_{a}$is known in advance, the uncertainty of the exact value of$K_{a}$entails only a small penalty in terms of energy efficiency. Our results show the significance of MIMO for the massive random access problem. As an example, we show that the spectral efficiency grows approximately linearly with the number of receive antennas in the case of CSIR, whereas the growth rate decreases in the case of no-CSI. Moreover, in the case of no-CSI, we demonstrate the suboptimality of the pilot-assisted scheme, especially when the number of active users is large. Building on non-asymptotic results, assuming all users are active and$J=\Theta (1)$, we obtain scaling laws of the number of supported users as follows: when$L = \Theta \left ({n^{2}}\right)$and$P=\Theta \left ({\frac {1}{n^{2}}}\right)$, one can reliably serve$K = \mathcal {O}(n^{2})$users in the case of no-CSI; under mild conditions in the case of CSIR, the PUPE requirement is satisfied if and only if$\frac {nL\ln KP}{K}=\Omega \left ({1}\right)$.

In this paper, the analysis of excess distortion exponent for joint source-channel coding (JSCC) in semantic-aware communication systems is presented. By introducing an unobservable semantic source, we extend the classical results by Csiszar to semantic-aware communication systems. Both upper and lower bounds of the exponent for the discrete memoryless source-channel pair are established. Moreover, an extended achievable bound of the excess distortion exponent for MIMO systems is derived. Further analysis explores how the block fading and numbers of antennas influence the exponent of semantic-aware MIMO systems. Our results offer some theoretical bounds of error decay performance and can be used to guide future semantic communications with joint source-channel coding scheme.

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

This paper investigates a novel method for designing linear precoders with finite alphabet inputs based on autoencoders (AE) without the knowledge of the channel model. By model-free training of the autoencoder in a multiple-input multiple-output (MIMO) system, the proposed method can effectively solve the optimization problem to design the precoders that maximize the mutual information between the channel inputs and outputs, when only the input-output information of the channel can be observed. Specifically, the proposed method regards the receiver and the precoder as two independent parameterized functions in the AE and alternately trains them using the exact and approximated gradient, respectively. Compared with previous precoders design methods, it alleviates the limitation of requiring the explicit channel model to be known. Simulation results show that the proposed method works as well as those methods under known channel models in terms of maximizing the mutual information and reducing the bit error rate.

This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppler shift. In addition, the multiple-input multiple-output (MIMO) with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link. To simplify the computation process, we estimate the channel tensor in parallel along the delay dimension. Then, the deep learning and expectation-maximization approach are integrated into the generalized approximate message passing with cross-correlation-based Gaussian prior to capture the channel sparsity in the delay-Doppler-angle domain and learn the hyperparameters. Finally, active devices are detected by computing energy of the estimated channel. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

In this paper, we propose an enhanced preamble based media access control mechanism (E-PMAC), which can be applied in power line communication (PLC) network for Industrial Internet of Things (IIoT). We introduce detailed technologies used in E-PMAC, including delay calibration mechanism, preamble design, and slot allocation algorithm. With these technologies, E-PMAC is more robust than existing preamble based MAC mechanism (P-MAC). Besides, we analyze the disadvantage of P-MAC in multi-layer networking and design the networking process of E-PMAC to accelerate networking process. We analyze the complexity of networking process in P-MAC and E-PMAC and prove that E-PMAC has lower complexity than P-MAC. Finally, we simulate the single-layer networking and multi-layer networking of E-PMAC, P-MAC, and existing PLC protocol, i.e., IEEE1901.1. The simulation results indicate that E-PMAC spends much less time in networking than IEEE1901.1 and P-MAC. Finally, with our work, a PLC network based on E-PMAC mechanism can be realized.

In this paper, we investigate a joint device activity detection (DAD), channel estimation (CE), and data decoding (DD) algorithm for multiple-input multiple-output (MIMO) massive unsourced random access (URA). Different from the state-of-the-art slotted transmission scheme, the data in the proposed framework is split into only two parts. A portion of the data is coded by compressed sensing (CS) and the rest is low-density-parity-check (LDPC) coded. In addition to being part of the data, information bits in the CS phase also undertake the task of interleaving pattern design and CE. The principle of interleave-division multiple access (IDMA) is exploited to reduce the interference among devices in the LDPC phase. Based on the belief propagation (BP) algorithm, a low-complexity iterative message passing (MP) algorithm is utilized to decode the data embedded in these two phases separately. Moreover, combined with successive interference cancellation (SIC), the proposed joint DAD-CE-DD algorithm is performed to further improve performance by utilizing the belief of each other. Additionally, based on the energy detection (ED) and sliding window protocol (SWP), we develop a collision resolution protocol to handle the codeword collision, a common issue in the URA system. In addition to the complexity reduction, the proposed algorithm exhibits a substantial performance enhancement compared to the state-of-the-art in terms of efficiency and accuracy.

This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-FM) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive wireless communication technique that is capable of reconfiguring the wireless propagation environment. In this paper, we study a free-space RIS-assisted multiple-input single-output (MISO) communication system in far-field operation. To maximize the received power from the physical and electromagnetic nature point of view, a comprehensive optimization, including beamforming of the transmitter, phase shifts of the RIS, orientation and position of the RIS is formulated and addressed. After exploiting the property of line-of-sight (LoS) links, we derive closed-form solutions of beamforming and phase shifts. For the non-trivial RIS position optimization problem in arbitrary three-dimensional space, a dimensional-reducing theory is proved. The simulation results show that the proposed closed-form beamforming and phase shifts approach the upper bound of the received power. The robustness of our proposed solutions in terms of the perturbation is also verified. Moreover, the RIS significantly enhances the performance of the mmWave/THz communication system.

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced$ K $-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes.

This paper studies a convincingly secure transmission framework under an allowable outage probability for practical finite-alphabet inputs, where a full-duplex receiver (Bob) is taken into account to emit the artificial noise for deteriorating the eavesdropper’s decoding performance. We develop a secure-reliable mechanism to take the place of prior secrecy rate (SR) maximization strategy, where a closed form expression is invoked for substituting the non-closed SR expression upon exploiting the multi-exponential decay fitting approach. Hence, the intractable expectation operation over a large number of noise samples is circumvented. Moreover, a pair of critical probabilities of the reliable transmission and secure outage are first analyzed, and then a low-complexity optimization scheme is formulated. Apart from designing the transmission scheme for classically secure networks consisting of a transmitter, Bob and an eavesdropper, we further carry out an investigation on conceiving a secure-reliable strategy against multiple Eves for improving the system’s extensibility. To this end, analytical expressions of both the reliability outage and secrecy outage probabilities for multiple-Eve scenarios are also derived. Furthermore, a pragmatic iterative solution is conceived for addressing the corresponding max-min optimization problem. Finally, the simulation results validate the significance of our considered secure-reliable transmission in terms of the average SR performance attained.

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

Massive multiple-input and multiple-output (MIMO) enables ultra-high throughput and low latency for tile-based adaptive virtual reality (VR) 360° video transmission in wireless network. In this paper, we consider a massive MIMO system where multiple users in a single-cell theater watch an identical VR 360° video. Based on tile prediction, base station (BS) deliveries the tiles in predicted field of view (FoV) to users. By introducing practical supplementary transmission for missing tiles and unacceptable VR sickness, we propose the first stable transmission scheme for VR video. we formulate an integer non-linear programming (INLP) problem to maximize users’ average quality of experience (QoE) score. Moreover, we derive the achievable spectral efficiency (SE) expression of predictive tile groups and the approximately achievable SE expression of missing tile groups, respectively. Analytically, the overall throughput is related to the number of tile groups and the length of pilot sequences. By exploiting the relationship between the structure of viewport tiles and SE expression, we propose a multi-lattice multi-stream grouping method aimed at improving the overall throughput for VR video transmission. Moreover, we analyze the relationship between QoE objective and number of predictive tile. We transform the original INLP problem into an integer linear programming problem by setting the predictive tiles groups as some constants. With variable relaxation and recovery, we obtain the optimal average QoE. Extensive simulation results validate that the proposed algorithm effectively improves QoE.

Because of discrepant deteriorations of the intended receiver and the unintended receiver, artificial noise (AN) can be invoked in conjunction with the precoder to wireless transmissions for enhancing the secrecy rate (SR) performance as long as we elaborately frame them. This paper studies a secure transmission strategy by jointly designing the precoder and AN beamformer at the relay network, where a passive eavesdropper and finite-alphabet inputs are taken into account. We propose a pair of solutions for low-order modulation and high-order modulation, respectively. To solve the first optimization problem, we propose a low-complexity algorithm with the aid of the invoked cut-off rate. Interestingly, we find that the phase of an optimum precoder for maximizing the SR has a correlation to both the channels spanning from the transmitter to the relays and spanning from the relays to the legitimate receiver. Furthermore, we reveal that the AN beamformer vector has at most one non-zero component, which only locates at the position corresponding to the minimum element of the channel between the relays and the intended receiver. According to these findings, the SR maximization problem over the two vectors is simplified as one only related to a pair of scalars. As for the high-order modulation, a new solution is further proposed for circumventing the predicament that the computational complexity exponentially increases as the size of the adopted modulation increases, where we not only eliminate two-layer summation over the legitimate symbols but also conceive a concave maximization SR problem. Finally, simulation results demonstrate the efficiency of the proposed algorithms in terms of the SR performance.

This article studies massive access in cell-free massive multi-input multi-output (MIMO)-based Internet of Things and solves the challenging active user detection (AUD) and channel estimation (CE) problems. For the uplink transmission, we propose an advanced frame structure design to reduce the access latency. Moreover, by considering the cooperation of all access points (APs), we investigate two processing paradigms at the receiver for massive access: cloud computing and edge computing. For cloud computing, all APs are connected to a centralized processing unit (CPU), and the signals received at all APs are centrally processed at the CPU. While for edge computing, the central processing is offloaded to part of APs equipped with distributed processing units, so that the AUD and CE can be performed in a distributed processing strategy. Furthermore, by leveraging the structured sparsity of the channel matrix, we develop a structured sparsity-based generalized approximated message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the quantization accuracy of the processed signals is taken into account. Based on the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE scheme is further developed under two paradigms for reduced access latency. Simulation results validate the superiority of the proposed approach over the state-of-the-art baseline schemes. Besides, the results reveal that the edge computing can achieve the similar massive access performance as the cloud computing, and the edge computing is capable of alleviating the burden on CPU, having a faster access response, and supporting more flexible AP cooperation.

Filter bank multiple access (FBMA) without subbands orthogonality has been proposed as a new candidate waveform to better meet the requirements of future wireless communication systems and scenarios. It has the ability to process directly the complex symbols without any fancy preprocessing. Along with the usage of cyclic prefix (CP) and wide-banded subband design, CP-FBMA can further improve the peak-to-average power ratio and bit error rate performance while reducing the length of filters. However, the potential gain of removing the orthogonality constraint on the subband filters in the system has not been fully exploited from the perspective of waveform design, which inspires us to optimize the subband filters for CP-FBMA system to maximizing the achievable rate. Besides, we propose a joint optimization algorithm to optimize both the waveform and the covariance matrices iteratively. Furthermore, the joint optimization algorithm can meet the requirements of filter design in practical applications in which the available spectrum consists of several isolated bandwidth parts. Both general framework and detailed derivation of the algorithms are presented. Simulation results show that the algorithms converge after only a few iterations and can improve the sum rate dramatically while reducing the transmission delay of information symbols.

Intelligent reflecting surface (IRS) is of low-cost and energy-efficiency and will be a promising technology for the future wireless communications like sixth generation. To address the problem of conventional directional modulation (DM) that Alice only transmits single confidential bit stream (CBS) to Bob with multiple antennas in a line-of-sight channel, IRS is proposed to create friendly multipaths for DM such that two CBSs can be transmitted from Alice to Bob. This will significantly enhance the secrecy rate (SR) of DM. To maximize the SR (Max-SR), a general non-convex optimization problem is formulated with the unit-modulus constraint of IRS phase-shift matrix (PSM), and the general alternating iterative (GAI) algorithm is proposed to jointly obtain the transmit beamforming vectors (TBVs) and PSM by alternately optimizing one and fixing another. To reduce its high complexity, a low-complexity iterative algorithm for Max-SR is proposed by placing the constraint of null-space (NS) on the TBVs, called NS projection (NSP). Here, each CBS is transmitted separately in the NSs of other CBS and AN channels. Simulation results show that the SRs of the proposed GAI and NSP can approximately double that of IRS-based DM with single CBS for massive IRS in the high signal-to-noise ratio region.

Wireless caching technologies have been proposed to relieve the transmission pressures, especially, the transmission redundancy on back-haul channels. In this paper, we consider a two-layer caching network, consisting of traditional macro-cell base station (MBS) aided back-haul channels and small-cell base stations (SBSs) aided local links. The network service provider (NSP), who is in charge of the two layers, leases its resources of the secondary layer, i.e., coverage of the SBSs, to content providers (CPs) for making extra profits and releasing pressures on the back-haul channels. At the same time, CPs will evaluate whether they are provided with proper incentives to pre-cache their files in the SBSs. Considering different quality of services (QoS) provided by the two layers as well as the economical impact of the traditional layer on the secondary layer, the NSP designs the optimal incentive mechanisms within the framework of contract theory for maximizing its own profits. First, we formulate the utility of the NSP and CPs. Then, the minimum transmission requirement, reserve price and limited resources are considered as constraints in designing the optimal contract. Also, some important properties of these constraints are analyzed to facilitate the optimal contract determination process. At last, an optimal contract determination scheme is proposed, based on which the optimal coverage set is determined first, and then the corresponding optimal prices are derived with the aid of equal cost line. Numerical results are provided to demonstrate the effectiveness of the proposed optimal contract in increasing the NSP's profits and incentivizing CPs to transmit on the secondary layer.

The massive machine-type communication has been one of the most representative services for future wireless networks. It aims to support massive connectivity of user equipments (UEs) which sporadically transmit packets with small size. In this work, we assume the number of UEs grows linearly and unboundedly with blocklength and each UE has an individual codebook. Among all UEs, an unknown subset of UEs are active and transmit a fixed number of data bits to a base station over a shared -spectrum radio link. Under these settings, we derive the achievability and converse bounds on the minimum energy-per-bit for reliable random access over quasi-static fading channels with and without channel state information (CSI) at the receiver. These bounds provide energy-efficiency guidance for new schemes suited for massive random access. Simulation results indicate that the orthogonalization scheme TDMA is energy-inefficient for large values of UE density μ. Besides, the multi-user interference can be perfectly cancelled when μ is below a critical threshold. In the case of no-CSI, the energy-per-bit for random access is only a bit more than that with the knowledge UE activity.

This paper investigates the massive random access for a huge amount of user devices served by a base station (BS) equipped with a massive number of antennas. We consider a grant-free unsourced random access (U-RA) scheme where all users possess the same codebook and the BS aims at declaring a list of transmitted codewords and recovering the messages sent by active users. Most of the existing works concentrate on applying U-RA in the oversimplified independent and identically distributed (i.i.d.) channels. In this paper, we consider a fairly general joint-correlated MIMO channel model with line-of-sight components for the realistic outdoor wireless propagation environments. We conduct the activity detection for the emitted codewords by performing an improved coordinate descent approach with Bayesian learning automaton to solve a covariance-based maximum likelihood estimation problem. The proposed algorithm exhibits a faster convergence rate than traditional descent approaches. We further employ a coupled coding scheme to resolve the issue that the dimensions of the common codebook expand exponentially with user payload size in the practical massive machine-type communications scenario. Our simulations reveal that to achieve an error probability of 0.05 for reliable communications in correlated channels, one must pay a 0.9 to 1.3 dB penalty comparing to the minimum signal to noise ratio needed in i.i.d. channels on condition that a sufficient number of receiving antennas is equipped at the BS.

This paper considers the support of grant-free massive access and solves the challenge of active user detection and channel estimation in the case of a massive number of users. By exploiting the sparsity of user activities, the concerned problems are formulated as a compressive sensing problem, whose solution is acquired by approximate message passing (AMP) algorithm. Considering the cooperation of multiple access points, for the deployment of AMP algorithm, we compare two processing paradigms, cloud computing and fog computing, in terms of their effectiveness in guaranteeing ultra reliable low-latency access. For cloud computing, the access points are connected in a cloud radio access network (C-RAN) manner, and the signals received at all access points are concentrated and jointly processed in the cloud baseband unit. While for fog computing, based on fog radio access network (F-RAN), the estimation of user activity and corresponding channels for the whole network is split, and the related processing tasks are performed at the access points and fog processing units in proximity to users. Compared to the cloud computing paradigm based on traditional C-RAN, simulation results demonstrate the superiority of the proposed fog computing deployment based on F-RAN.

In this paper, we propose a new polar coding scheme for the unsourced, uncoordinated Gaussian random access channel. Our scheme is based on sparse spreading, treat interference as noise and successive interference cancellation (SIC). On the transmitters side, each user randomly picks a code-length and a transmit power from multiple choices according to some probability distribution to encode its message, and an interleaver to spread its encoded codeword bits across the entire transmission block. The encoding configuration of each user is transmitted by compressive sensing, similar to some previous works. On the receiver side, after recovering the encoding configurations of all users, it applies single-user polar decoding and SIC to recover the message list. Numerical results show that our scheme outperforms all previous schemes for active user number Ka≥ 250, and provides competitive performance for Ka≤ 225. Moreover, our scheme has much lower complexity compared to other schemes as we only use single-user polar coding.

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users' confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption.

Random access is necessary in crowded scenarios due to the limitation of pilot sequences and the intermittent pattern of device activity. Nowadays, most of the related works are based on independent and identically distributed (i.i.d.) channels. However, massive multiple-input multiple-output (MIMO) channels are not always i.i.d. in realistic outdoor wireless propagation environments. In this paper, a device grouping and pilot set allocation algorithm is proposed for the uplink massive MIMO systems over spatially correlated Rayleigh fading channels. Firstly, devices are divided into multiple groups, and the channel covariance matrixes of devices within the same group are approximately orthogonal. In each group, a dedicated pilot set is assigned. Then active devices perform random pilot and data access process. The mean square error of channel estimation (MSE-CE) and the spectral efficiency of this scheme are derived, and the MSE-CE can be minimized when collision devices have non- overlapping angle of arrival (AoA) intervals. Simulation results indicate that the MSE-CE and spectral efficiency of this protocol are improved compared with the traditional scheme. The MSE-CE of the proposed scheme is close to the theoretical lower bound over a wide signal-to-noise ratio (SNR) region especially for long pilot sequence. Furthermore, the MSE-CE performance gains are significant in high SNR and strongly correlated scenarios.

High-speed trains are being widely deployed around the world. To meet the high data rate transmission requirements, millimeter wave high-speed train communication systems with large antenna arrays have drawn increasingly attentions. Since channel conditions vary rapidly in high-speed train communication scenarios, frequent channel estimation is required. Moreover, due to the limit period of each transmission time interval, the key challenge in channel estimation is to design an efficient beam searching scheme to allow more time for data transmission. This paper formulates the beam searching problem into a multi-armed bandit problem, and proposes a bandit inspired beam searching scheme to reduce the number of measurements. The performance of the proposed scheme is evaluated in terms of regret, and simulation results show that the proposed scheme can approach the theocratical limit quickly.

In this paper, we study multiple-antenna wireless communication networks, where a large number of devices simultaneously communicate with an access point. The capacity region of multiple-input multiple-output massive multiple access channels (MIMO mMAC) is investigated. While joint typicality decoding is utilized to establish the achievability of capacity region for conventional MAC with fixed number of the users, the technique is not directly applicable for the MIMO mMAC. Instead, an information-theoretic approach based on Gallager's error exponent analysis is exploited to characterize the finite dimension region of the MIMO mMAC. Theoretical results reveal that the region is dominated by the sum rate constraint only, and the individual user rates are dominated by specific factors that correspond to the allocation of the sum rate. The rate in conventional MAC is not achievable when the number of users is comparable with codelength, which is due to the fact that successive interference cancellation cannot guarantee an arbitrary small error decoding probability for MIMO mMAC. The results further imply that, asymptotically, the individual user rate is independent of the number of transmit antennas, and channel hardening makes the individual user rate close to that when only statistic knowledge of channel is available at transmitter. The finite dimension region of MIMO mMAC is a generalization of the symmetric rate in Chen et al. (2017).

Energy-efficient computation is of crucial importance in mobile edge computing (MEC) networks. However, few investigations have studied resource allocation strategies for maximizing the computation efficiency. A computation efficiency maximization framework is established in wireless-powered MEC networks relying on non-orthogonal multiple access (NOMA) under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. The energy harvesting time, the local computing frequency, the operation mode selection, the offloading time and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. An iterative algorithm and an alternative optimization algorithm are proposed to solve the formulated challenging non-convex problems. Simulation results show that our proposed resource allocation schemes outperform the benchmark schemes in terms of computation efficiency. Moreover, a tradeoff is elucidated between the computation efficiency and the computation throughput.

In this paper, we consider the design of joint resource blocks (RBs) and power allocation for dual-mode base stations operating over millimeter wave (mmW) band and microwave (μW) band. The resource allocation design aims to minimize the system energy consumption while taking into account the channel state information, maximum delay, load, and different types of user applications (UAs). To facilitate the design, we first propose a group-based algorithm to assign UAs to multiple groups. Within each group, low-power UAs, which often appear in short distance and experience less obstacles, are inclined to be served over mmW band. The allocation problem over mmW band can be solved by a greedy algorithm. Over μW band, we propose an estimation-optimal-descent algorithm. The rate of each UA at all RBs is estimated to initialize the allocation. Then, we keep altering RB's ownership until any altering makes power increases. Simulation results show that our proposed algorithm offers an excellent tradeoff between low energy consumption and fair transmission.

Secure wireless information and power transfer based on directional modulation is conceived for amplify-and-forward relaying networks. Explicitly, we first formulate a secrecy rate maximization (SRM) problem, which can be decomposed into a twin-level optimization problem and solved by a one-dimensional (1D) search and semidefinite relaxation (SDR) technique. Subsequently, in order to reduce the search complexity, we formulate an optimization problem based on maximizing the signal-to-leakage-AN-noise-ratio (Max-SLANR) criterion, and transform it into a SDR problem. In addition, the relaxation is proved to be tight according to the classic Karush-Kuhn-Tucker (KKT) conditions. Finally, to reduce the computational complexity, a successive convex approximation (SCA) scheme is proposed to find a near-optimal solution. The complexity of the SCA scheme is much lower than that of the SRM and the Max-SLANR schemes. Simulation results demonstrate that the performance of the SCA scheme is very close to that of the SRM scheme in terms of its secrecy rate and bit error rate, but much better than that of the zero forcing scheme.

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems with finite-alphabet inputs. The mmWave MIMO system employs partially-connected hybrid precoding architecture with dynamic subarrays, where each radio frequency (RF) chain is connected to a dynamic subset of antennas. We consider the design of analog and digital precoders utilizing statistical and/or mixed channel state information (CSI), which involve solving an extremely difficult problem in theory: First, designing the optimal partition of antennas over RF chains is a combinatorial optimization problem, whose optimal solution requires an exhaustive search over all antenna partitioning solutions; Second, the average mutual information under mmWave MIMO channels lacks closed-form expression and involves prohibitive computational burden; and Third, the hybrid precoding problem with given partition of antennas is nonconvex with respect to the analog and digital precoders. To address these issues, this paper first presents a simple criterion and the corresponding low complexity algorithm to design the optimal partition of antennas using statistical CSI. Then, it derives the lower bound and its approximation for the average mutual information, in which the computational complexity is greatly reduced compared to calculating the average mutual information directly. In addition, it also shows that the lower bound with a constant shift offers a very accurate approximation to the average mutual information. This paper further proposes utilizing the lower bound approximation as a low-complexity and accurate alternative for developing a manifold-based gradient ascent algorithm to find near-optimal analog and digital precoders. Several numerical results are provided to show that our proposed algorithm outperforms the existing hybrid precoding algorithms.

In this paper, we study the design of secure communication for time-division duplex multi-cell multi-user massive multiple-input-multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission of the users. We exploit both the received pilot's and the received data signals for uplink channel estimation. We show analytically that when both the number of transmit antennas and the length of the data vector tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station, provided their signal powers are different. This finding reveals that decreasing (instead of increasing) the desired user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this observation, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. For the special case of a single-cell single-user system with independent and identically distributed fading, the obtained expression reveals that the secrecy rate scales logarithmically with the number of transmit antennas. This is the same scaling law as for the achievable rate of a single-user massive MIMO system in the absence of eavesdroppers. The numerical results indicate that the proposed scheme achieves significant secrecy rate gains compared with alternative approaches based on matched filter precoding with artificial noise generation and null space transmission.

In this paper, we study the design of a hybrid precoder, consisting of an analog and a digital precoder, for the delivery phase of downlink cache-enabled millimeter-wave (mm-wave) radio access networks (CeMm-RANs). In CeMm-RANs, enhanced remote radio heads (eRRHs), which are equipped with local cache and baseband signal processing capabilities in addition to the basic functionalities of conventional RRHs, are connected to the baseband processing unit via fronthaul links. Two different fronthaul information transfer strategies are considered, namely, hard fronthaul information transfer, where hard information of uncached requested files is transmitted via the fronthaul links to a subset of eRRHs, and soft fronthaul information transfer, where the fronthaul links are used to transmit quantized baseband signals of uncached requested files. The hybrid precoder is optimized for maximization of the minimum user rate under a fronthaul capacity constraint, an eRRH transmit power constraint, and a constant-modulus constraint on the analog precoder. The resulting optimization problem is non-convex, and hence, the global optimal solution is difficult to obtain. Therefore, convex approximation methods are employed to tackle the non-convexity of the achievable user rate, the fronthaul capacity constraint, and the constant modulus constraint on the analog precoder. Then, an effective algorithm with provable convergence is developed to solve the approximated optimization problem. The simulation results are provided to evaluate the performance of the proposed algorithms, where fully digital precoding is used as the benchmark. The results reveal that except for the case of a large fronthaul link capacity, soft fronthaul information transfer is preferable for CeMm-RANs. Furthermore, surprisingly, hybrid precoding outperforms fully digital precoding with soft fronthaul information transfer for medium-to-large file sizes and fronthaul capacity limited mm-wave cloud RANs.

This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the base station with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max-min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. The simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.

The conventional grant-based network relies on the handshaking between the base station and active devices to achieve dynamic multi-user scheduling, which may result in large signaling overheads as well as system latency. To address those problems, a grant-free receiver design is considered in this paper based on sparse code multiple access (SCMA), one of the promising air interface technologies for 5G wireless networks. With the presence of unknown multipath fading, the proposed receiver performs joint channel estimation and data decoding without knowing the user activity in the network. Formulating a factor graph representation for the problem, we devise a message-passing receiver for the uplink SCMA that performs joint estimation iteratively. Motivated by the idea of approximate inference, we use expectation propagation to project the intractable distributions into Gaussian families such that a linear complexity decoder is obtained. The simulation results show that the proposed receiver can detect active devices in the network with a high accuracy and can achieve an improved bit-error-rate performance compared with existing methods.

Hybrid precoding provides a tradeoff between spectral efficiency and power consumption in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we investigate the partially-connected hybrid precoding design for mmWave MIMO broadcast channels with finite alphabet inputs. To enhance the spectral efficiency, a new algorithm is proposed to dynamically optimize the mapping strategy from radio frequency (RF) chains to transmit antennas such that the weighted sum of channel gains is maximized. Then we adopt the inexact alternating minimization method to design hybrid precoding matrices with given optimal mapping strategy and finite-alphabet inputs. Simulation results demonstrate the good performance of our proposed algorithm.

In this paper, we study the design of secure communication for time division duplexing multi-cell multi-user massive multiple-input multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission phase of the desired users. We exploit both the received pilots and data signals for uplink channel estimation. We show analytically that when the number of transmit antennas and the length of the data vector both tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station if their signal powers are different. This finding reveals that decreasing (instead of increasing) the desire user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this result, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. Numerical results indicate that under strong active attacks, the proposed design achieves significant secrecy rate gains compared to the conventional design employing matched filter precoding and artificial noise generation.

With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality. The problem is non-convex and challenging to tackle. An alternative optimization algorithm is proposed based on sequential convex optimization. Simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence.

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution toward 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, and so on. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

Provides an overview of the technical articles and features presented in this issue. Our regular papers this month focus on 5G related topics such as multipleinput– multipleoutput transmission using finite input signals, and achieving ultrareliable and low-latency wireless communication.

Multiple antennas have played an essential role in spatial multiplexing and diversity transmission for a wide range of communication applications. Most advances in the design of high-speed wireless multiple-input-multiple-output (MIMO) systems have been based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. However, although the Gaussian signal is capacity-achieving, practical systems transmit signals belonging to finite and discrete constellations. Therefore, capacity-achieving transceiver processing based on a Gaussian input signal can be quite suboptimal for practical MIMO systems with discrete constellation input signals. To address this shortcoming, this paper aims to provide a comprehensive overview of MIMO transmission design with finite input signals. It first summarizes existing fundamental results for MIMO systems with finite input signals. Next, focusing on basic point-to-point MIMO systems, it examines transmission schemes based on the three most important criteria for communication systems: mutual-information-driven designs, mean-square-error-driven designs, and diversity-driven designs. In particular, a unified framework is developed for the design of low-complexity transmission schemes applicable to massive MIMO systems in forthcoming 5G wireless networks for the first time. Furthermore, adaptive transmission designs are proposed that switch among these criteria based on channel conditions to formulate the best transmission strategy. A survey is then given of transmission designs with finite input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, transmission designs with finite input signals are discussed for other multi-antenna systems. Finally, a number of technical challenges that remain unresolved at the time of writing are highlighted, and future trends in transmission design with finite input signals are discussed.

This paper investigates the physical-layer security for an indoor visible light communication network consisting of a transmitter, a legitimate receiver, and an eavesdropper. Both the main channel and the wiretapping channel have non-negative inputs, which are corrupted by additive white Gaussian noises. Considering the illumination requirement and the physical characteristics of lighting source, the input is also constrained in both its average and peak optical intensities. Two scenarios are investigated: one is only with an average optical intensity constraint and the other is with both average and peak optical intensity constraints. Based on the information theory, closed-form expressions of the upper and lower bounds on secrecy capacity for the two scenarios are derived. Numerical results show that the upper and lower bounds on secrecy capacity are tight, which validates the derived closed-form expressions. Moreover, the asymptotic behaviors in the high signal-to-noise ratio (SNR) regime are analyzed from the theoretical aspects. At high SNR, when only considering the average optical intensity constraint, a small performance gap exists between the asymptotic upper and lower bounds on secrecy capacity. When considering both average and peak optical intensity constraints, the asymptotic upper and lower bounds on secrecy capacity coincide with each other. These conclusions are also confirmed by numerical results.

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush-Kuhn-Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared with the conventional cache strategy without considering cache and buffer tradeoff in terms of delay.

This paper considers secure communication for amplify-and-forward (AF) relay networks with finite alphabet input. The joint optimization of power selection and beamforming design for improving the physical layer security of AF relay networks with single and multiple eavesdroppers is investigated. For the case with one eavesdropper, we transform the problem of multi-variable beamforming design into a single-variable optimization problem through semi-definite programming and solve it with one-dimensional optimization techniques. Moreover, the corresponding source power is obtained by utilizing the relation between the mutual information and minimum mean square error. Then, an iterative two-step algorithm is proposed to maximize the achievable secrecy rate. In the presence of multiple eavesdroppers, a zero-forcing beamforming scheme, where the confidential signal is nulled out in the direction of all eavesdroppers, is proposed to enhance the physical layer security. We decouple the source power and the beamforming vector by transforming the achievable secrecy rate into a single-variable function of the source power. Then, the suboptimal source power and the corresponding beamforming vector with low-complexity are derived. Numerical examples show that the proposed schemes significantly enhance the secrecy performance of the AF relay networks.

The implementation of automatic image registration is still difficult in various applications. In this paper, an automatic image registration approach through line-support region segmentation and geometrical outlier removal is proposed. This new approach is designed to address the problems associated with the registration of images with affine deformations and inconsistent content, such as remote sensing images with different spectral content or noise interference, or map images with inconsistent annotations. To begin with, line-support regions, namely a straight region whose points share roughly the same image gradient angle, are extracted to address the issues of inconsistent content existing in images. To alleviate the incompleteness of line segments, an iterative strategy with multi-resolution is employed to preserve global structures that are masked at full resolution by image details or noise. Then, geometrical outlier removal is developed to provide reliable feature point matching, which is based on affine-invariant geometrical classifications for corresponding matches initialized by scale invariant feature transform. The candidate outliers are selected by comparing the disparity of accumulated classifications among all matches, instead of conventional methods which only rely on local geometrical relations. Various image sets have been considered in this paper for the evaluation of the proposed approach, including aerial images with simulated affine deformations, remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multisensor, and multitemporal), and map images with inconsistent annotations. Experimental results demonstrate the superior performance of the proposed method over the existing approaches for the whole data set.

In this paper, we investigate the design of a pilot spoofing attack (PSA) carried out by multiple single-antenna eavesdroppers (Eves) in a downlink time-division duplex system, where a multiple antenna base station (BS) transmits confidential information to a single-antenna legitimate user. During the uplink channel training phase, multiple Eves collaboratively impair the channel acquisition of the legitimate link, aimed at maximizing the wiretapping signal-to-noise ratio (SNR) in the subsequent downlink data transmission phase. Two different scenarios are investigated: 1) the BS is unaware of the PSA and 2) the BS attempts to detect the presence of the PSA. For both scenarios, we formulate wiretapping SNR maximization problems. For the second scenario, we also investigate the probability of successful detection and constrain it to remain below a pre-designed threshold. The two resulting optimization problems can be unified into a more general non-convex optimization problem, and we propose an efficient algorithm based on the minorization-maximization (MM) method and the alternating direction method of multipliers (ADMM) to solve it. The proposed MM-ADMM algorithm is shown to converge to a stationary point of the general problem. In addition, we propose a semi-definite relaxation (SDR) method as a benchmark to evaluate the efficiency of the MM-ADMM algorithm. Numerical results show that the MM-ADMM algorithm achieves near-optimal performance and is computationally more efficient than the SDR-based method.

Recently, optical spatial modulation (OSM) has been proposed for indoor optical wireless communications (OWC), which is considered as a feasible complementary solution for high data rate transmission. This paper investigates the constellation optimization problem for OSM based OWC. An OWC system with multiple transmitters and multiple receivers is considered. By using OSM, only a single transmitter is active at each time instance. Considering the non-negativity and the peak optical intensity constraints, the constellation space for OSM based OWC system is established. By exploiting the channel state information at the transmitter, this paper designs the multi-dimensional constellations by maximizing the minimum Euclidean distance between the received constellation points. Two feasible algorithms are proposed to solve the optimization problem. To evaluate the performance of the proposed algorithms, the symbol error rate is also derived. Simulation results show that the derived constellations using the proposed two algorithms outperform the existing uniformly distributed constellations.

In this paper, we investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we show analytically that a generalized singular value decomposition (GSVD) based design, which is optimal for Gaussian inputs, may exhibit a severe performance loss for finite alphabet inputs in the high signal-to-noise ratio (SNR) regime. In light of this, we propose a novel Per-Group-GSVD (PG-GSVD) design which can effectively compensate the performance loss caused by the GSVD design. More importantly, the computational complexity of the PG-GSVD design is by orders of magnitude lower than that of the existing design for finite alphabet inputs in \cite{Wu2012TVT} while the resulting performance loss is minimal. Numerical results indicate that the proposed PG-GSVD design can be efficiently implemented in large-scale multiple-antenna systems and achieves significant performance gains compared to the GSVD design.

In this paper, an indoor visible light communication (VLC) system with the input-dependent noise is considered. In the system, the main noise is caused by Gaussian noise, however, with a noise variance depending on the current input signal strength. In the presence of the input-dependent noise, the theoretical expression of the bit error rate (BER) for the VLC using on-off keying is derived. Based on the derived BER, an optimization problem is formulated to improve the BER performance by tilting the receiver plane. The proposed optimization problem is proven to be a convex optimization problem, which can be efficiently solved by using the specialized solver such as the CVX toolbox for MATLAB. To verify the accuracy of the derived expression of the BER, all theoretical results are thoroughly confirmed by using the Monte-Carlo simulations. Moreover, simulation results show that the larger the variance of the input-dependent noise is, the worse the BER performance becomes. Additionally, the BER performance can be dramatically improved by tilting the receiver plane properly.

With the development of vehicle-to-vehicle (V2V) communications, the interference among different V2V communication groups will become the limitation for high-rate transmissions. Two intergroup interference coordination schemes are proposed for the V2V communication. The first proposed scheme is a Doppler-shift-based frequency-domain interference alignment scheme. Through pre- and postprocessing, we guarantee that one V2V group is free from interference, and the other V2V group is partially interfered. The second proposed scheme is a frequency-domain precoding scheme. Through frequency-domain precoding, the intergroup interference between the two V2V groups can be mitigated completely.

Filter bank multi-carrier (FBMC) entitles many advantages over orthogonal frequency division multiplexing (OFDM) and is considered to be a more competitive waveform in the future generation cellular communications. In current FBMC, the prototype filter is deliberately designed to meet the perfect reconstruction (PR) constraint to establish subband orthogonality in real domain, which may not be optimal from communication perspective. In this paper, we challenge the necessity of PR constraint by proposing a new FBMC framework, which directly accepts non-orthogonal transmission. The resulting imperfect reconstruction FBMC (iPR-FBMC) has several advantages over its PR FBMC counterpart: 1) the constraint on the prototype filter is relaxed; 2) more importantly, the prototype filter can now be optimized with new goal of improving the detection performance rather than having to meet the PR condition; and 3) it allows for more flexible subband management in multi-user scenario. We will show how those advantages can be exploited. Simulations show that with moderate increase in computational complexity, the proposed iPR-FBMC with optimized prototype filter has superior bit error rate (BER) performance to existing FBMC with PR constraint and even outperforms OFDM, especially in highly frequency selective channels. The findings may shed light into potential research on non-orthogonal FBMC without PR constraint.

In this paper, we investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we investigate the case where instantaneous channel state information (CSI) of the eavesdropper is known at the transmitter. We show analytically that a generalized singular value decomposition (GSVD)-based design, which is optimal for Gaussian inputs, may exhibit a severe performance loss for finite alphabet inputs in the high signal-to-noise ratio regime. In light of this, we propose a novel Per-Group-GSVD (PG-GSVD) design, which can effectively compensate the performance loss caused by the GSVD design. More importantly, the computational complexity of the PG-GSVD design is by orders of magnitude lower than that of the existing design for finite alphabet inputs while the resulting performance loss is minimal. Then, we extend the PG-GSVD design to the case where only statistical CSI of the eavesdropper is available at the transmitter. Numerical results indicate that the proposed PG-GSVD design can be efficiently implemented in large-scale multiple-antenna systems and achieves significant performance gains compared with the GSVD design.

Reliable feature point matching is a vital yet challenging process in feature-based image registration. In this paper, a robust feature point matching algorithm, which is called recovery and filtering vertex trichotomy matching, is proposed to remove outliers and retain sufficient inliers for remote sensing images. A novel affine-invariant descriptor, which is called the vertex trichotomy descriptor, is proposed on the basis of that geometrical relations between any of vertices and lines are preserved after affine transformations, which is constructed by mapping each vertex into trichotomy sets. The outlier removals in vertex trichotomy matching (VTM) are implemented by iteratively comparing the disparity of the corresponding vertex trichotomy descriptors. Some inliers mistakenly validated by a large number of outliers are removed in VTM iterations, and several residual outliers that are close to the correct locations cannot be excluded with the same graph structures. Therefore, a recovery and filtering strategy is designed to recover some inliers based on identical vertex trichotomy descriptors and restricted transformation errors. Assisted with the additional recovered inliers, residual outliers can be also filtered out during the process of reaching identical graphs for the expanded vertex sets. Experimental results demonstrate the superior performance on precision and stability of this algorithm under various conditions, such as remote sensing images with large transformations, duplicated patterns, or inconsistent spectral content.

This paper investigates the design of precoders for single-user multiple-input multiple-output (MIMO) channels, and, in particular, for finite-alphabet signals. Based on an asymptotic expression for the mutual information of channels exhibiting line-of-sight components and rather general antenna correlations, precoding structures that decompose the general channel into a set of parallel subchannel pairs are proposed. Then, a low-complexity iterative algorithm is devised to maximize the sum mutual information of all pairs. The proposed algorithm significantly reduces the computational load of existing approaches with only minimal loss in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unmanageable with existing solutions. Most importantly, the proposed solution does not require instantaneous channel state information (CSI) at the transmitter, but only statistical CSI.

In this paper, we investigate the design of multiple-input multiple-output single-user precoders for finite-alphabet signals under the premise of statistical channel-state information at the transmitter. Based on an asymptotic expression for the mutual information of channels exhibiting antenna correlations, we propose a low-complexity iterative algorithm that radically reduces the computational load of existing approaches by orders of magnitude with only minimal losses in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unwieldy in existing solutions.

In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multiple-input multiple-output systems with a multi-antenna active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform toward the eavesdropper, thus increasing its received signal power. Assuming matched filter precoding and artificial noise (AN) generation at the transmitter, we derive an asymptotic achievable secrecy rate when the number of transmit antennas approaches infinity. For the case of a single-antenna active eavesdropper, we obtain a closed-form expression for the optimal power allocation policy for the transmit signal and the AN, and find the minimum transmit power required to ensure reliable secure communication. Furthermore, we show that the transmit antenna correlation diversity of the intended users and the eavesdropper can be exploited in order to improve the secrecy rate. In fact, under certain orthogonality conditions of the channel covariance matrices, the secrecy rate loss introduced by the eavesdropper can be completely mitigated.

In this paper, we study the resource allocation algorithm design for distributed antenna multiuser networks with full-duplex (FD) radio base stations (BSs), which enable simultaneous uplink and downlink communications. The considered resource allocation algorithm design is formulated as an optimization problem taking into account the antenna circuit power consumption of the BSs and the quality of service (QoS) requirements of both uplink and downlink users. We minimize the total network power consumption by jointly optimizing the downlink beamformer, the uplink transmit power, and the antenna selection. To overcome the intractability of the resulting problem, we reformulate it as an optimization problem with decoupled binary selection variables and nonconvex constraints. The reformulated problem facilitates the design of an iterative resource allocation algorithm, which obtains an optimal solution based on the generalized Bender's decomposition (GBD). For this algorithm, we also propose a simple technique to improve the speed of convergence. Furthermore, to strike a balance between computational complexity and system performance, a suboptimal resource allocation algorithm with polynomial time complexity is proposed. Simulation results illustrate that the proposed GBD-based iterative algorithm converges to the globally optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. Our results also demonstrate the tradeoff between power efficiency and the number of active transmit antennas when the circuit power consumption is taken into account. In particular, activating an exceedingly large number of antennas may not be an efficient approach for reducing the total system power consumption. In addition, our results reveal that FD systems facilitate significant power savings compared to traditional half-duplex systems, despite the nonnegligible self-interference.

This paper gives a replica analysis for the minimum mean square error (MSE) of a massive multiple-input multipleoutput (MIMO) system by using Bayesian inference. The Bayesoptimal estimator is adopted to estimate the data symbols and the channels from a block of received signals in the spatial-temporal domain. We show that using the Bayes-optimal estimator, the interfering signals from adjacent cells can be separated from the received signals without pilot information of the interfering signals. In addition, the MSEs with respect to the data symbols and the channels of the desired users decrease with the number of receive antennas and the number of data symbols, respectively. There are no residual interference terms that remain bounded away from zero as the numbers of receive antennas and data symbols approach infinity.

In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multipleinput multiple-output (MIMO) systems in the presence of an active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform towards the eavesdropper, thus increasing its received signal power. We derive an asymptotic achievable secrecy rate for matched filter precoding and artificial noise (AN) generation at the transmitter when the number of transmit antennas goes to infinity. For the achievability scheme at hand, we obtain the optimal power allocation policy for the transmit signal and the AN in closed form. For the case of correlated fading channels, we show that the impact of the active eavesdropper can be completely removed if the transmit correlation matrices of the users and the eavesdropper are orthogonal. Inspired by this result, we propose a precoder null space design exploiting the low rank property of the transmit correlation matrices of massive MIMO channels, which can significantly degrade the eavesdropping capabilities of the active eavesdropper.

A novel dual-graph-based matching method is proposed in this letter particularly for the multispectral/multidate images with low overlapping areas, similar patterns, or large transformations. First, scale invariant feature transform based matching is improved by normalizing gradient orientations and maximizing the scale ratio similarity of all corresponding points. Next, Delaunay graphs are generated for outlier removal, and the candidate outliers are selected by comparing the distinction of Delaunay graph structures. In order to bring back the inliers removed in Delaunay triangulation matching iterations and to exclude the remaining outliers, the recovery strategy equipped with the dual graph of Delaunay is explored. Inliers located in the corresponding Voronoi cells are recovered to the residual sets. The experimental results demonstrate the accuracy and robustness of the proposed algorithm for various representative remote sensing images.

In this paper, we investigate the design of linear precoders for the multiple-input-multiple-output (MIMO) multiple access channel (MAC). We assume that statistical channel state information (CSI) is available at the transmitters and consider the problem under the practical finite alphabet input assumption. First, we derive an asymptotic (in the large system limit) expression for the weighted sum rate (WSR) of the MIMO MAC with finite alphabet inputs and Weichselberger's MIMO channel model. Subsequently, we obtain the optimal structures of the linear precoders of the users maximizing the asymptotic WSR and an iterative algorithm for determining the precoders. We show that the complexity of the proposed precoder design is significantly lower than that of MIMO MAC precoders designed for finite alphabet inputs and instantaneous CSI. Simulation results for finite alphabet signaling indicate that the proposed precoder achieves significant performance gains over existing precoder designs.

In this paper, we investigate the design of linear precoders for multiple-input multiple-output (MIMO) multiple access channels (MAC). We assume that statistical channel state information (CSI) is available at the transmitters and consider the problem under the practical finite alphabet input assumption. First, we derive an asymptotic (in the large-system limit) weighted sum rate (WSR) expression for the MIMO MAC with finite alphabet inputs and general jointly-correlated fading. Subsequently, we obtain necessary conditions for linear precoders maximizing the asymptotic WSR and propose an iterative algorithm for determining the precoders of all users. In the proposed algorithm, the search space of each user for designing the precoding matrices is its own modulation set. This significantly reduces the dimension of the search space for finding the precoding matrices of all users compared to the conventional precoding design for the MIMO MAC with finite alphabet inputs, where the search space is the combination of the modulation sets of all users. As a result, the proposed algorithm decreases the computational complexity for MIMO MAC precoding design with finite alphabet inputs by several orders of magnitude. Simulation results for finite alphabet signalling indicate that the proposed iterative algorithm achieves significant performance gains over existing precoder designs, including the precoder design based on the Gaussian input assumption, in terms of both the sum rate and the coded bit error rate.

This paper investigates the linear precoder design for multiple-input multiple-output (MIMO) K-user interference channels with finite alphabet inputs. We first obtain the general explicit expressions of the achievable rate of each user in MIMO interference channel systems. We study optimal transmission strategies in both high signal-to-noise ratio (SNR) and low SNR regions. We show that given finite alphabet inputs, a simple power allocation design can achieve optimal performance. In contrast, the well-known interference alignment technique for Gaussian input scenarios, only utilizes a partial interference-free signal space for transmission and leads to a constant performance loss when it is applied to finite-alphabet input scenarios. We determine this constant rate loss at high SNR. Moreover, we establish necessary conditions for the linear precoder design of the weighted sum-rate maximization. We also develop an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results show that for the practical digital modulated signals from discrete constellations, the proposed iterative algorithm achieves considerably higher sum-rate than the existing methods.

In this letter, Bilateral K Nearest Neighbors Spatial Orders around Geometric Centers (Bi-SOGC) is presented to match feature points for remote sensing images with large affine transformation, similar patterns or multispectral images. In Bi-SOGC, both the bilateral adjacent relations and the spatial angular orders are considered. Bilateral K Nearest Neighbors (BiKNN) descriptors are proposed to describe the adjacent information. The vertices with maximum BiKNN difference are deemed as candidate outliers. The invariant spatial angular orders for affine transformation are used to deal with outliers in pseudo isomorphic structures, geometric centers are taken as the reference points. To increase the correct matching points and eliminate stubborn outliers, a recovery strategy utilizes the addition of fresh inliers to break down the stabilized pseudo graphs of the residual sets. Experimental results demonstrate the superior performance of this algorithm under various conditions for remote sensing images.

This paper investigates the linear precoder design for K-user interference channels of multiple-input multiple-output (MIMO) transceivers under finite alphabet inputs. We first obtain general explicit expressions of the achievable rate for users in the MIMO interference channel systems. We study optimal transmission strategies in both low and high signal-to-noise ratio (SNR) regions. Given finite alphabet inputs, we show that a simple power allocation design achieves optimal performance at high SNR whereas the well-known interference alignment technique for Gaussian inputs only utilizes a partial interference-free signal space for transmission and leads to a constant rate loss when applied naively to finite-alphabet inputs. Moreover, we establish necessary conditions for the linear precoder design to achieve weighted sum-rate maximization. We also present an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results demonstrate that the proposed iterative algorithm achieves considerably higher sum-rate under practical QAM inputs than other known methods.

We investigate the design of linear transmit precoding for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna configurations. For the case where all the users employ the same modulation scheme, we further present a weighted sum rate upper-bound of the MIMO BC with identical transmit precoding matrices. The resulting bound demonstrates a serious performance loss due to multi-user interference for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region, which motivates the use of simple precoding to combat this multiuser interference. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Numerical results illustrate that our proposed algorithm provides significant gains over other conventional precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input assumption.

In this paper, we investigate the secrecy rate of finite alphabet communications over multiple-input, multiple-output, multiple-antenna eavesdropper (MIMOME) systems. Traditional precoder designs at the transmitter for achieving secrecy capacity (maximum secrecy rate) for MIMOME systems are developed according to the assumption of Gaussian input signals. Such designs may risk substantial secrecy rate loss when Gaussian inputs are replaced by practical finite alphabet inputs. To address this issue, we propose a linear precoding design to directly maximize the secrecy rate for MIMOME systems under the constraint of finite alphabet input. Exploiting convex optimization and matrix calculus, we present necessary conditions required of the optimal precoding design and develop an iterative algorithm for finding an efficient precoder. With finite alphabet input signals, maximum transmission power no longer corresponds to maximum secrecy rate as in the case of Gaussian input. We further derive closed-form results on the optimal transmission design for maximizing secrecy rate in low signal-to-noise ratio (SNR) region and near-optimal transmission power in a high SNR region.

This paper investigates communication over multiple-input multiple-output Rayleigh-product channels in the presence of both co-channel interference and thermal noise. We first present exact expressions for the marginal ordered eigenvalue distributions of the channel Gram matrix when multichannel beamforming is employed, from which we obtain exact results on the outage probability on each eigenmode. Our results are applicable to an extensive class of multichannel systems with generic system configurations. For particular scenarios of single-input multiple-output, keyhole, and multiple-input single-output channels, simplified closed-form expressions for the asymptotic distributions of the non-zero eigenvalue of the channel Gram matrix are derived. These results indicate that the diversity order is only determined by the second largest value among the number of transmit antennas, scatterers, and receive antennas. For these scenarios, we also derive concise closed-form expressions for the moments of the output signal-to-interference-plus-noise ratio, from which an explicit relationship to the moments in Rayleigh fading channels is revealed. These results are also used to investigate the impact of the number of channel scatterers on the bandwidth requirements for a given transmission rate and power, at low signal-to-noise ratios.

We investigate the design of linear transmit precoder for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We first derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna number. We further present a weighted sum rate upper bound of the MIMO BC with identical transmit precoding matrices. The resulting bound exhibits a serious performance loss because of the non-uniquely decodable transmit signals for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region. This performance loss motivates the use of a simple precoding to combat the non-unique decodability. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Our { simulation} results under the practical transmit symbols of discrete constellations demonstrate significant gains by the proposed algorithm over other precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input signals. For the low-density parity-check coded systems, our precoder provides considerably coded BER improvement through iterative decoding and detection.

This paper studies the eigenvalue distributions of multiple-input multiple-output (MIMO) Rayleigh-product channels in the presence of both co-channel interference and thermal noise. We first present exact expressions for the marginal ordered eigenvalue distributions of the channel Gram matrix when multichannel beamforming is employed, from which we obtain exact results of the outage probability on each eigenmode. Our results apply to a wide class of multichannel systems which transmit on the eigenmodes of the MIMO channel, allowing for transmission on any numbers of eigensubchannels, with any numbers of antennas, any numbers of scatterers in the environment, and any numbers of interferers with arbitrary powers. Also, for the particular case of keyhole channels, simplified closed-form expressions for the asymptotic distributions of the non-zero eigenvalue of the channel Gram matrix are derived, which indicate that the diversity order is only determined by the minimum of the number of transmit and receive antennas.

We investigate the multiple-input multiple-output broadcast channel with channel distribution information available at the transmitter. The so-called fading-paper model is considered with Nttransmit antennas, and each user having Nrreceive antennas. Near-optimal designs are proposed for the inflation factor matrix under general fading conditions, based on maximizing the approximation of linear assignment capacity. It is proved that for Nt≤ Nr, this matrix has a similar structure and achieves a similar interference elimination effect as dirty-paper coding. Also, low complexity iterative algorithms are proposed for Nt>; Nrcase, which yield a good choice for the inflation factor matrix numerically. Based on the obtained inflation factor matrix, we provide efficient approaches to evaluate the linear assignment achievable rate region for some popular statistical channel models.

This paper investigates the performance of Rayleigh-product multiple-input multiple-output (MIMO) channels in the presence of both co-channel interference (CCI) and thermal noise. We obtain closed form expressions for the cumulative distribution function and probability density function of the output signal-to interference-plus-noise ratio (SINR) when optimum combining is employed. In contrast to prior results, our expressions apply for arbitrary numbers of interferers with arbitrary powers. Furthermore, the impact of noise is firstly addressed in our expressions. These are made possible based on the recent random matrix theory tools from which the new statistical properties of maximum eigenvalue of the resultant channel matrix can be derived. The new statistical results permit a general analysis for outage probability of the optimum combining system in Rayleigh-product MIMO channels. Simulation results are also provided to validate the analysis and to examine the effect of CCI and thermal noise on performance.