Driven by the demand for high transmission efficiency in 6G networks, semantic communication has attracted significant interest recently. While most existing works focus on optimizing semantic communication system design to enhance end-to-end transmission performance, they often overlook the integration of quality of service (QoS) requirements. To address this gap, we employ the Alpha-Beta-Gamma (ABG) formula to empirically approximate the relationship between end-to-end transmission quality and signal-to-noise ratio (SNR). Based on this model, we first design an optimal beamforming scheme that minimizes transmission power while ensuring real-time QoS guarantees. Furthermore, to account for inevitable channel state information (CSI) estimation errors in practical scenarios, we propose robust beamforming design schemes under QoS constraints for both bounded and unbounded CSI estimation errors. These optimization problems are efficiently solved using semidefinite relaxation (SDR),S-lemma, and Bernstein-type inequalities. Finally, experimental results demonstrate that our proposed optimal beamforming design scheme outperforms conventional beamforming methods, while the robust beamforming schemes achieve superior performance in handling CSI estimation errors compared to existing computational approaches.

Integrated sensing and communication (ISAC) has emerged as a key technology for future communication systems. In this paper, we provide a general framework to reveal the fundamental trade-off between sensing and communication in OFDM systems, where a unified ISAC signal is exploited to perform both tasks. To evaluate the sensing performance, we introduce two representative performance metrics: The Cramér-Rao Bound (CRB) and the Barakin Bound (BRB). For the asymptotic case when the number of subcarriers is large, we show that the asymptotically optimal input distribution that achieves the Pareto boundary point of the Capacity-CRB\BRB region is Gaussian and the entire Pareto boundary can be obtained by solving a power allocation problem. We prove that the power allocation problem for obtaining the Capacity-CRB region can be calculated by solving a convex optimization problem. However, the Capacity-BRB region is more difficult to be characterized due to the non-convexity of the optimization problem. Therefore, we propose an iterative algorithm to obtain an inner bound of the Capacity-BRB region. Moreover, we derive the sufficient conditions under which the Gaussian distribution remains asymptotically optimal when the number of subcarriers approaches infinity and the delay gap between two targets approaches zero simultaneously. For the non-asymptotic case, an optimization problem is formulated for sensing-optimal input distribution and the capacity-achieving distribution is derived under the sensing-optimal constraints. Finally, numerical simulations are conducted to verify the theoretical analysis and provide useful insights.

Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including troubleshooting, standards interpretation, and network optimization. However, their deployment in practice must balance inference cost, latency, and reliability. In this work, we study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline. In it, an efficient edge model handles routine queries, a more capable cloud model addresses complex cases, and human experts are involved only when necessary. We define a misalignment-cost constrained optimization problem, aiming to minimize average processing cost, while guaranteeing alignment of automated answers with expert judgments. We propose a statistically rigorous threshold selection method based on multiple hypothesis testing (MHT) for a query processing mechanism based on knowledge and confidence tests. The approach provides finite-sample guarantees on misalignment risk. Experiments on the TeleQnA dataset –a telecom-specific benchmark – demonstrate that the proposed method achieves superior cost-efficiency compared to conventional cascaded baselines, while ensuring reliability at prescribed confidence levels.

Extremely large-scale antenna arrays (XL-arrays) and ultra-high frequencies are two fundamental technologies for future sixth-generation (6G) wireless networks, providing enhanced system capacity and substantial bandwidth expansion. To fully leverage these technological advancements, conventional far-field models must be replaced by more accurate near-field spherical-wave propagation models. This paper investigates a near-field communication system comprising a hybrid analog-digital beamforming base station (BS) and multiple mobile users, aiming to maximize system sum-rate through optimized codebook design, beam selection, and digital precoding. To accommodate dynamic user distributions, we propose two model-agnostic meta-learning (MAML)-based frameworks that enable prompt adaptation by learning well-initialized models for fine tuning. The first framework integrates the MAML method with a deep neural network (DNN) to design near-field codebooks tailored to the user distributions, addressing the limitations of conventional uniform codebooks. The second framework employs a joint neural network (NN) for beam selection and digital precoding, combining deep reinforcement learning (DRL) and deep unfolding. The DRL NN formulates beam selection as a Markov Decision Process, while the deep-unfolding NN approximates optimal digital precoding through a lightweight iterative algorithm without matrix inversion. Simulation results show that the proposed frameworks significantly outperform conventional methods, achieving superior generalization and overall performance in dynamic near-field scenarios.

In 6G mobile communications, acquiring accurate and timely channel state information (CSI) becomes increasingly challenging due to the growing antenna array size and bandwidth. To alleviate the CSI feedback burden, the channel knowledge map (CKM) has emerged as a promising approach by leveraging environment-aware techniques to predict CSI based solely on user locations. However, how to effectively construct a CKM remains an open issue. In this paper, we propose F4-CKM, a novel CKM construction framework characterized by four distinctive features: radiance Field rendering, spatial-Frequency-awareness, location-Free usage, and Fast learning. Central to our design is the adaptation of radiance field rendering techniques from computer vision to the radio frequency (RF) domain, enabled by a novel Wireless Radiator Representation (WiRARE) network that captures the spatial-frequency characteristics of wireless channels. Additionally, a novel shaping filter module and an angular sampling strategy are introduced to facilitate CKM construction. Extensive experiments demonstrate that F4-CKM significantly outperforms existing baselines in terms of wireless channel prediction accuracy and efficiency.

Developing channel-adaptive deep joint source-channel coding (JSCC) systems is a critical challenge in wireless image transmission. While recent advancements have been made, most existing approaches are designed for static channel environments, limiting their ability to capture the dynamics of channel environments. As a result, their performance may degrade significantly in practical systems. In this paper, we consider time-varying block fading channels, where the transmission of a single image can experience multiple fading events. We propose a novel coarse-to-fine channel-adaptive JSCC framework (CFA-JSCC) that is designed to handle both significant fluctuations and rapid changes in wireless channels. Specifically, in the coarse-grained phase, CFA-JSCC utilizes the average signal-to-noise ratio (SNR) to adjust the encoding strategy, providing a preliminary adaptation to the prevailing channel conditions. Subsequently, in the fine-grained phase, CFA-JSCC leverages instantaneous SNR to dynamically refine the encoding strategy. This refinement is achieved by re-encoding the remaining channel symbols whenever the channel conditions change. Additionally, to reduce the overhead for SNR feedback, we utilize a limited set of channel quality indicators (CQIs) to represent the channel SNR and further propose a reinforcement learning (RL)-based CQI selection strategy to learn this mapping. This strategy incorporates a novel reward shaping scheme that provides intermediate rewards to facilitate the training process. Experimental results demonstrate that our CFA-JSCC provides enhanced flexibility in capturing channel variations and improved robustness in time-varying channel environments.

In this study, we investigate a downlink multiuser multiple-input multiple-output (MU-MIMO) system employing a two-dimensional (2D) movable antenna (MA) array. We propose a two-timescale optimization framework to jointly optimize antenna position vector (APV) and beamforming for sum-rate maximization, addressing hardware limitations that restrict real-time antenna adjustments. Specifically, APV is updated based on long-term channel statistics at the start of each coherence block, while beamforming is optimized per time slot using short-term information. An efficient stochastic successive convex approximation (SSCA)-based algorithm is developed for joint optimization. To enhance performance and reduce complexity, we propose a deep-unfolding neural network (DUNN) integrating non-linear activations and first-order Taylor approximations for matrix inversions. Furthermore, we introduce a meta-learning approach for improved initialization and rapid convergence, along with an online adaptation mechanism for continuous adjustment to changing channel conditions. Simulation results show that our proposed approach improves sum-rate compared to conventional MIMO systems with uniform arrays, and the DUNN outperforms the SSCA-based algorithm. Meanwhile, meta-learning and online adaptation framework enables network to adapt to channel changes better and converge faster.

Recently, semantic communication (SC) has garnered increasing attention for its efficiency, yet it remains vulnerable to semantic jamming attacks. These attacks entail introducing crafted perturbation signals to legitimate signals over the wireless channel, thereby misleading the receivers’ semantic interpretation. This paper investigates the above issue from a practical perspective. Contrasting with previous studies focusing on power-fixed attacks, we extensively consider a more challenging scenario of power-variable attacks by devising an innovative attack model named Adjustable Perturbation Generator (APG), which is capable of generating semantic jamming signals of various power levels. To combat semantic jamming attacks, we propose a novel framework called Robust Model Ensembling (ROME) for secure semantic communication. Specifically, ROME can detect the presence of semantic jamming attacks and their power levels. When high-power jamming attacks are detected, ROME adapts to raise its robustness at the cost of generalization ability, and thus effectively accommodating the attacks. Furthermore, we theoretically analyze the robustness of the system, demonstrating its superiority in combating semantic jamming attacks via adaptive robustness. Simulation results show that the proposed ROME approach exhibits significant adaptability and delivers graceful robustness and generalization ability under power-variable semantic jamming attacks.

In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical representations of the original image. These representations are then directly mapped to channel symbols for transmission by the JSCC encoder. We extend this framework to scenarios with a feedback link, modeling transmission over a noisy channel as a probabilistic sampling process and deriving a novel generative formulation for JSCC with feedback. Compared with existing approaches, our proposed HJSCC provides enhanced adaptability by dynamically adjusting transmission bandwidth, encoding these representations into varying amounts of channel symbols. Additionally, we introduce a rate attention module to guide the JSCC encoder in optimizing its encoding strategy based on prior information. Extensive experiments on images of varying resolutions demonstrate that our proposed model outperforms existing baselines in rate-distortion performance and maintains robustness against channel noise.

In this study, we explore a downlink multiuser multiple-input multiple-output (MU-MIMO) system with movable antennas (MA). Our objective is to jointly optimize the MA positions and beamforming to maximize system sum-rate. Due to hardware limitations that make real-time MA position adjustments impractical, we propose a two-timescale optimization scheme. In this scheme, the antenna position vector (APV) is update based on long-term channel statistics at the beginning of each coherence time block, while the beamforming matrix is optimized based on short-term information in each subsequent time slot within the same block. We develop an efficient stochastic successive convex approximation (SSCA)-based algorithm for joint APV and beamforming design. Additionally, in order to improve performance and reduce computational complexity, we propose a deep-unfolding neural network (NN) that preserves the structure of SSCA-based algorithm while incorporating a nonlinear activation function and trainable parameters based on first-order Taylor approximations for matrix inversion. Meanwhile, projection operators are designed to ensure compliance with the design constraints. Simulation results show that the twotimescale algorithm improves sum-rate compared to conventional MIMO systems with uniform linear arrays (ULA), and the deepunfolding NN outperforms the SSCA-based algorithm.

The physical cell identity (PCI) is a critical parameter in wireless networks, enabling user equipment (UE) to uniquely identify cells and mitigate inter-cell interference during communication. However, the proliferation of base stations in modern networks has complicated the proper assignment of PCIs, as it requires avoiding multiple types of PCI conflicts, such as PCI collision, mod-3 collision, and confusion. Existing methods generally focus on addressing single PCI conflicts and involve reassigning PCIs for all cells, which can lead to significant data exchange overhead in large-scale networks. To address these challenges, this paper tackles the PCI planning problem by introducing a PCI reassignment budget as a constraint to minimize overall network interference. A penalty-based double-loop PCI planning algorithm is proposed, where the outer loop optimizes penalty parameters derived from the constraints, and the inner loop combines a block coordinate descent (BCD) approach with a proposed concave-convex procedure to optimize PCI assignments. Simulation results demonstrate the effectiveness of the proposed method in significantly reducing network interference compared to existing approaches.

With their high mobility and ease of deployment, unmanned aerial vehicle (UAV)-assisted communication systems have emerged as a prominent area of academic research and a cornerstone technology for Sixth-Generation (6G) mobile communication networks. This paper investigates a multi-UAV downlink wireless communication system in which users exhibit random movement on the ground. To maximize the sum-rate of all users over the observation period, we propose a joint optimization framework that integrates user association, UAV 3D trajectory design, and power allocation, while addressing channel estimation across different timescales. In the long timescale, we model the UAV-user connections as a graph and utilize a graph neural network to jointly optimize user association and UAV trajectories. In the short timescale, we deploy a deep unfolding network for efficient channel estimation and power allocation. Simulation results validate the effectiveness of the proposed approach, showcasing significant performance improvements.

Integrated Sensing and Communication (ISAC) technology facilitates simultaneous reliable communication and high-precision sensing performance in vehicular networks. Many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Aiming to minimize the Cramér-Rao bound (CRB) for estimating vehicle's angle, distance and velocity while meeting communication rate requirements, we propose a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the superiority of the proposed algorithm and scheme.

Deep learning-based joint source-channel coding (JSCC) is emerging as a promising technology for effective image transmission. However, most existing approaches focus on transmitting clear images, overlooking real-world challenges such as motion blur caused by camera shaking or fast-moving objects. Motion blur often degrades image quality, making transmission and reconstruction more challenging. Event cameras, which asynchronously record pixel intensity changes with extremely low latency, have shown great potential for motion deblurring tasks. However, the efficient transmission of the abundant data generated by event cameras remains a significant challenge. In this work, we propose a novel JSCC framework for the joint transmission of blurry images and events, aimed at achieving high-quality reconstructions under limited channel bandwidth. This approach is designed as a deblurring task-oriented JSCC system. Since RGB cameras and event cameras capture the same scene through different modalities, their outputs contain both shared and domain-specific information. To avoid repeatedly transmitting the shared information, we extract and transmit their shared information and domain-specific information, respectively. At the receiver, the received signals are processed by a deblurring decoder to generate clear images. Additionally, we introduce a multi-stage training strategy to train the proposed model. Simulation results demonstrate that our method significantly outperforms existing JSCC-based image transmission schemes, addressing motion blur effectively.

Despite the remarkable success of semantic image transmission, existing approaches face the challenge of distortion accumulation. Specifically, as a received image is further forwarded to other devices, reconstruction distortion will accumulate, leading to decreased system stability. In this paper, we propose an idempotent semantic communication system for image transmission to enhance stability. We systematically analyze the factors contributing to this accumulation effect and propose several strategies to mitigate it. First, we design the system using a right-invertible neural network to achieve idempotence, ensuring the decoder functions as the right inverse of the encoder. Second, we introduce a feature discretization mechanism to further reduce distortion accumulation, leveraging the benefits of digitalization over analog transmission. Finally, we employ a recursive training strategy, which incorporates the reconstructed images into the training process to significantly improve overall stability. Empirical results demonstrate that our proposed strategies effectively enhance system stability, minimizing quality degradation and enhancing output consistency across multiple transmissions.

Recent advancements in integrated sensing and communication (ISAC) technology offer significant potential for high-resolution localization and high-throughput communication in vehicular networks. However, many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. Additionally, the limited transmit power of roadside units (RSUs) and the small radar cross section (RCS) of vehicles can result in weak echo signals, hindering effective vehicle detection and tracking. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Our approach optimizes both RSU beamforming and IOS configuration (including refraction and reflection amplitudes and phase shifts) to minimize the Cramér-Rao bound (CRB) while meeting communication rate requirements. Solving this optimization problem is challenging due to the complex variable-coupling and the implicit CRB expression. To overcome these difficulties, we present a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the effectiveness of the proposed ZO-IPDD algorithm and the superior performance of the tracking scheme compared to existing methods.

International audience

International audience

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research.

Motivated by progress in data-driven supervised learning, semantic communication has witnessed remarkable advancements in improving the efficiency of data transmission under various channel conditions. These advancements typically require a substantial amount of training data for offline training, which is challenging in practical systems. Therefore, in this work, we propose O2SC, a one-shot online-learning framework for semantic communication to achieve adaptive transmission under different channel conditions. Since semantic communication relies on acquired channel state information (CSI), we jointly design the channel estimation and semantic communication processes. Specifically, we introduce a denoising module based on one-shot self-supervised learning, allowing semantic communication systems to adapt to new channel conditions without the need to collect extensive training data. The denoising module is utilized to eliminate noise in the received data samples, using only the data samples themselves. Following this, we further exploit meta-learning to allow the system to quickly adapt to diverse channel conditions, by finding an appropriate initialization for each data sample in a timely way. Simulation results demonstrate that the proposed method achieves performance close to that of supervised learning-based approaches while also providing improved generalizability across different channel conditions.

Recent studies in joint source-channel coding (JSCC) have fostered a fresh paradigm in end-to-end semantic communication. Despite notable performance achievements, present initiatives in building semantic communication systems primarily hinge on the transmission of continuous channel symbols, thus presenting challenges in compatibility with established digital systems. In this paper, we introduce a novel approach to address this challenge by developing a multi-order digital joint coding-modulation (MDJCM) scheme for semantic communications. Initially, we construct a digital semantic communication system by integrating a multi-order modulation/demodulation module into a nonlinear transform source-channel coding (NTSCC) framework. Recognizing the non-differentiable nature of modulation/demodulation, we propose a novel substitution training strategy. Herein, we treat modulation/demodulation as a constrained quantization process and introduce scaling operations alongside manually crafted noise to approximate this process. As a result, employing this approximation in training semantic communication systems can be deployed in practical modulation/demodulation scenarios with superior performance. Additionally, we demonstrate the equivalence by analyzing the involved probability distribution. Moreover, to further upgrade the performance, we develop a hierarchical dimension-reduction strategy to provide a gradual information extraction process. Extensive experimental evaluations demonstrate the superiority of our proposed method over existing digital and non-digital JSCC techniques.

With the explosive growth of maritime activities, it is expected to provide seamless communications with quality of service (QoS) guarantee over broad sea area. In the context, this paper proposes a space-air-ground-sea integrated maritime communication architecture combining satellite, unmanned aerial vehicle (UAV), terrestrial base station (TBS) and unmanned surface vessel (USV). Firstly, according to the distance away from the shore, the whole marine space is divided to coastal area, offshore area, middle-sea area and open-sea area, the maritime users in which are served by TBS, USV, UAV and satellite, respectively. Then, by exploiting the potential of integrated maritime communication system, a joint beamforming and trajectory optimization algorithm is designed to maximize the minimum transmission rate of maritime users. Finally, theoretical analysis and simulation results validate the effectiveness of the proposed algorithm.

In modern wireless network architectures, such as O-RAN, artificial intelligence (AI)-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The AI “apps” are selected on the basis of contextual information such as network conditions, topology, traffic statistics, and design goals. The mapping between context and AI model parameters is ideally done in a zero-shot fashion via an automatic model selection (AMS) mapping that leverages only contextual information without requiring any current data. This paper introduces a general methodology for the online optimization of AMS mappings. Optimizing an AMS mapping is challenging, as it requires exposure to data collected from many different contexts. Therefore, if carried out online, this initial optimization phase would be extremely time consuming. A possible solution is to leverage a digital twin of the physical system to generate synthetic data from multiple simulated contexts. However, given that the simulator at the digital twin is imperfect, a direct use of simulated data for the optimization of the AMS mapping would yield poor performance when tested in the real system. This paper proposes a novel method for the online optimization of AMS mapping that corrects for the bias of the simulator by means of limited real data collected from the physical system. Experimental results for a graph neural network-based power control app demonstrate the significant advantages of the proposed approach.

In the realm of semantic communication, the significance of encoded features can vary, while wireless channels are known to exhibit fluctuations across multiple subchannels in different domains. Consequently, critical features may traverse subchannels with poor states, resulting in performance degradation. To tackle this challenge, we introduce a framework called Feature Allocation for Semantic Transmission (FAST), which offers adaptability to channel fluctuations across both spatial and temporal domains. In particular, an importance evaluator is first developed to assess the importance of various features. In the temporal domain, channel prediction is utilized to estimate future channel state information (CSI). Subsequently, feature allocation is implemented by assigning suitable transmission time slots to different features. Furthermore, we extend FAST to the space-time domain, considering two common scenarios: precoding-free and precoding-based multiple-input multiple-output (MIMO) systems. An important attribute of FAST is its versatility, requiring no intricate fine-tuning. Simulation results demonstrate that this approach significantly enhances the performance of semantic communication systems in image transmission. It retains its superiority even when faced with substantial changes in system configuration.

Being able to provide excellent error-correction performance for polar codes with short-to-moderate code length, successive-cancellation list (SCL) is regarded as one of the most promising decoding algorithms. However, the application of SCL decoding in low-latency communication scenarios is limited due to its sequential nature. Recently, fast list decoding algorithms are proposed by considering special nodes with low code rates. Aiming at achieving further speedup for SCL decoding, this paper presents fast list decoding algorithms for two types of high-rate special nodes, namely single-parity-check (SPC) and sequence rate-1(SRI) nodes, based on the minimum-combinations set (MCS) which is able to significantly narrow the search space of candidate paths. Typically, SPC nodes can be directly decoded within one round of path splitting procedure, whereas SR1 nodes, as a group of parallel SPC nodes, can also be decode efficiently. Simulation results show that the proposed fast SCL decoder is able to reduce the decoding latency by 68.4% as compared to the state of the art, without any error-correction performance degradation.

Unmanned Aerial Vehicle (UAV) technologies have garnered significant attention, particularly in the context of UAV-assisted wireless networks, which are seen as a pivotal component in the development of Sixth-Generation (6G) mobile communication systems. In this research, we delve into the realm of UAV-assisted wireless communication networks, where a single UAV efficiently caters to numerous random mobile users on the ground. Our focus lies in optimizing user movement tracking, beamforming, and UAV trajectory to maximize the data transmission rates for users within a specified time frame, all while adhering to stringent power constraints and the UAV's limited flight range. We harness the power of deep reinforcement learning (DRL) to monitor mobile users and predict the ever-changing channel state information. As beamforming and UAV trajectory adjustments operate on different timescales, we introduce a dual-layer deep unfolding network to fine-tune the transmit beamformer and UAV trajectory simultaneously. The outcomes of our simulations demonstrate the effectiveness and commendable performance of the proposed scheme.

Deep learning-based joint source-channel coding (JSCC) is emerging as a potential technology to meet the demand for effective data transmission, particularly for image transmission. Nevertheless, most existing advancements only consider analog transmission, where the channel symbols are continuous, making them incompatible with practical digital communication systems. In this work, we address this by involving the modulation process and consider mapping the continuous channel symbols into discrete space. Recognizing the non-uniform distribution of the output channel symbols in existing methods, we propose two effective methods to improve the performance. Firstly, we introduce a uniform modulation scheme, where the distance between two constellations is adjustable to match the non-uniform nature of the distribution. In addition, we further design a non-uniform modulation scheme according to the output distribution. To this end, we first generate the constellations by performing feature clustering on an analog image transmission system, then the generated constellations are employed to modulate the continuous channel symbols. For both schemes, we fine-tune the digital system to alleviate the performance loss caused by modulation. Here, the straight-through estimator (STE) is considered to overcome the non-differentiable nature. Our experimental results demonstrate that the proposed schemes significantly outperform existing digital image transmission systems.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can enable full spatial coverage. In this paper, we investigate an artificial intelligence (AI)-empowered STAR-RIS-assisted multi-user communication system. We aim to maximize the system sum-rate by jointly optimizing the mode selection matrix of the STAR-RIS elements, the passive beamforming matrix of the STAR-RIS, and the beamforming matrix of the base station (BS). Due to the mixed-timescale structure, we propose a joint neural network (NN) design consisting of an advantage pointer-critic (APC) NN to optimize the discrete variables in the mode selection matrix, a fully connected NN, and a deep-unfolding NN to optimize the beamforming matrices. Specifically, the element mode selection problem is formulated as a Markov decision process (MDP), and an APC NN is carefully designed to solve it. The passive beamforming matrix of the STAR-RIS is optimized by employing a fully connected NN. Then, we apply an iterative weighted minimum mean-square error (WMMSE)-based deep-unfolding NN for the BS beamforming design. Simulation results verify that our jointly trained NN can outperform the conventional algorithms with reduced overhead.

Recently, semantic communication has received increasing attention for its potential to enhance efficiency, yet research on semantic security is still in its infancy. Due to the open nature of wireless channels, semantic communication systems are susceptible to wireless adversarial attacks. These attacks entail introducing deliberately crafted perturbation signals to legitimate signals over the wireless channel, which misleads the semantic interpretation at the receiver. This paper explores defense approaches from a practical perspective. To better characterize real-world wireless adversarial attacks, we first introduce an effective attack model named Adjustable Perturbation Generator (APG), designed to generate perturbation signals of various power levels. To combat these attacks, we propose a novel framework called Robust Model Ensembling for Semantic Communication (ROME-SC). Specifically, a Multi-level Perturbation Detector (MPD) is developed to detect the presence of attacks and measure their power levels. Then, the robust model ensembling approach is proposed to handle wireless adversarial attacks adaptively with the assistance of the MPD. Simulation results show that the proposed ROME-SC significantly enhances the overall performance of semantic communication systems under wireless adversarial attacks.

Semantic communication is a promising framework for the next generation communication systems, which generally adopts deep learning based joint source and channel coding and has been verified to offer superior efficacy. A key ingredient in augmenting the reliability of this framework is the incorporation of hybrid automatic repeat request (HARQ) techniques. However, existing semantic HARQ architectures, such as fixed-length HARQ or chase combining HARQ (CC-HARQ), utilize predefined retransmission code lengths, lacking the flexibility to adjust to different channel signal-to-noise ratio (SNR) conditions. To address this issue, this paper develops an adaptive HARQ scheme by leveraging the double deep Q-network (DDQN) to determine the retransmission code lengths. Specifically, we first propose a basic model which consists of an image reconstruction module and a performance estimation module. The performance estimation module replaces the conventional error detection method like cyclic redundancy check (CRC) to estimate the structural similarity index measure (SSIM) of the reconstructed image at the receiver. Building on this basic model, our proposed HARQ scheme works by feeding back an NACK signal and an appropriate code length determined by the proposed DDQN algorithm to the semantic transmitter for the next transmission, if the estimated SSIM performance of the previous transmission does not exceed a predefined threshold. Experimental results demonstrate that our HARQ scheme is able to achieve the same SSIM performance as the existing semantic HARQ schemes, but with significantly reduced communication cost.

For reliable communication in high mobility scenarios, we need to estimate the channel in orthogonal time frequency space (OTFS) systems, which can be considered as a sparse signal recovery problem with an uncertain sensing matrix and solved by compressed sensing (CS) algorithms. However, conventional expectation maximization (EM)-based CS algorithms only output the point estimation of grid parameters to approximate the sensing matrix, which leads to an unavoidable approximation error. To address this problem, we present a turbo inverse-free successive linear approximation variational Bayesian inference (Turbo-IFSLA-VBI) algorithm, which provides the Bayesian estimation of both channel and grid parameters, thus the approximation error can be eliminated by iteratively approximating the sensing matrix with updated grid parameters. Besides, the proposed method employs a majorization-minimization (MM) framework to simplify the matrix inverse operations, achieving a lower computational complexity. Finally, simulation results are presented to verify the superiority of the proposed scheme over the state-of-the-art schemes.

Intelligent reflecting surface (IRS) is a promising technology to reconfigure the wireless channel cost-effectively, thereby improving transmission reliability in mobile ad hoc networks (MANETs). Prior works related to IRS primarily rely on channel estimation for configuring IRS, which, however, will introduce additional overhead and impact the efficiency of IRS-assisted MANETs, leading to increased delay and energy consumption during data transmission. To overcome this difficulty, we propose a multi-IRS-assisted double deep Q-Network (MIRS-DDQN) routing algorithm to find paths with higher end-to-end data rate and lower energy consumption. Routing packets are designed to collect experience tuples for DDQN to optimize the joint routing and transmit power selection policy. Moreover, these packets are also used to execute a blind beamforming strategy to configure IRS without incurring additional communication overhead. In particular, the IRSs can effectively enhance the links related to the IRS-assisted nodes and thus provide better solutions for DDQN to find an energy-efficient path with higher end-to-end data rate. Simulation results are presented to demonstrate the advantages of the proposed algorithm as compared to benchmark schemes in terms of end-to-end delay, energy consumption and end-to-end data rate.

In recent developments, deep learning (DL)-based joint source-channel coding (JSCC) for wireless image transmission has made significant strides in performance enhancement. Nonetheless, the majority of existing DL-based JSCC methods are tailored for scenarios featuring stable channel conditions, notably a fixed signal-to-noise ratio (SNR). This specialization poses a limitation, as their performance tends to wane in practical scenarios marked by highly dynamic channels, given that a fixed SNR inadequately represents the dynamic nature of such channels. In response to this challenge, we introduce a novel solution, namely deep refinement-based JSCC (DRJSCC). This innovative method is designed to seamlessly adapt to channels ex-hibiting temporal variations. By leveraging instantaneous channel state information (CSI), we dynamically optimize the encoding strategy through re-encoding the channel symbols. This dynamic adjustment ensures that the encoding strategy consistently aligns with the varying channel conditions during the transmission process. Specifically, our approach begins with the division of encoded symbols into multiple blocks, which are transmitted progressively to the receiver. In the event of changing channel conditions, we propose a mechanism to re-encode the remaining blocks, allowing them to adapt to the current channel conditions. Experimental results show that the DRJSCC scheme achieves comparable performance to the other mainstream DL-based JSCC models in stable channel conditions, and also exhibits great robustness against time-varying channels.

5G-WiFi heterogeneous network (HetNet) is considered to be an effective solution to the edge computing capability crisis, the introduction of WiFi networks can well share the burden of the 5G network. In this paper, based on 5G-WiFi HetNet, we formulate the optimization problem of joint task offloading and resource allocation to maximize the utility of uplink transmission rate and business cost while ensuring high reliability. In order to address the high nonlinearity caused by the binary variables and the fractional form. We utilize a fractional transform method and propose a two-layer iterative algorithm based on the penalty dual decomposition (PDD) approach to effectively solve this mixed-integer nonlinear programming (MINLP) problem. Simulation results verify the convergence of the proposed algorithm and show that it improves the utility of the system while ensuring high reliability.

Although existing semantic communication systems have achieved great success, they have not considered that the channel is time-varying wherein deep fading occurs occasionally. Moreover, the importance of each semantic feature differs from each other. Consequently, the important features may be affected by channel fading and corrupted, resulting in performance degradation. Therefore, higher performance can be achieved by avoiding the transmission of important features when the channel state is poor. In this paper, we propose a scheme of Feature Arrangement for Semantic Transmission (FAST). In particular, we aim to schedule the transmission order of features and transmit important features when the channel state is good. To this end, we first propose a novel metric termed feature priority, which takes into consideration both feature importance and feature robustness. Then, we perform channel prediction at the transmitter side to obtain the future channel state information (CSI). Furthermore, the feature arrangement module is developed based on the proposed feature priority and the predicted CSI by transmitting the prior features under better CSI. Simulation results show that the proposed scheme significantly improves the performance of image transmission compared to existing semantic communication systems without feature arrangement.

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) holds significant promise, providing a robust solution to address the limitations imposed by the computing and energy resources in wireless devices (WDs) operating within various low-power networks. In this context, intelligent reflecting surface (IRS) technology emerges as a noteworthy communication innovation that not only conserves energy but also optimizes spectrum resources. With the aid of IRS, wireless-powered MEC systems can considerably enhance the efficiency of radio frequency (RF) energy transmission while simultaneously improving wireless information transmission performance. This article delves into the long-term energy efficiency of IRS-aided multiuser wireless-powered MEC systems. Recognizing the stochastic nature of task arrivals, we introduce a stochastic optimization problem aimed at addressing the energy efficiency challenge. This problem’s objective is to optimize the system’s long-term energy efficiency while adhering to constraints related to network stability, energy stability, task offloading policies, IRS phase shift vectors, device central processing unit (CPU) frequencies, transmission power, and temporal causality. Subsequently, we transform this long-term optimization problem into a short-term deterministic problem using Lyapunov optimization theory. However, the transformed problem remains highly coupled and involves discrete variables. Consequently, we have developed an algorithm based on the penalty dual decomposition (PDD) method to effectively address this challenge. Simulation results prove conclusively that with the assistance of IRS, system energy efficiency is effectively improved.

Intelligent reflecting surface (IRS)-aided wireless relaying technology has aroused great interest recently as a promising new solution to enhance the system performance. However, most existing works only consider the decode-and-forward (DF) relay and ignore the base station (BS) to user direct link. In this paper, we focus on an IRS-aided full-duplex (FD) amplify-and-forward (AF) relay system and study the deployment design and beamforming optimization problem. Specifically, we first analyze the asymptotic rates achieved by three IRS deployment strategies (i.e., deploying the IRS near the BS, relay and user) when the number of reflecting elements becomes sufficiently large to obtain useful insights. Then, for the practical case with finite number of reflecting elements, we aim to maximize the transmission rate under different IRS deployment strategies by jointly optimizing the IRS reflection coefficients and transmit powers at the BS and relay. For the case of deploying the IRS near the user, a block coordinate decent (BCD)-based algorithm is proposed. For the cases of deploying the IRS near the relay and BS, we propose a virtual stochastic successive convex approximation (VSSCA) algorithm to solve our considered deterministic optimization problems efficiently. Finally, numerical results are provided to demonstrate the asymptotic performance analysis as well as the effectiveness of our proposed algorithms as compared to various benchmark schemes.

Recently, the ever-increasing demand for bandwidth in multi-modal communication systems requires a paradigm shift. Powered by deep learning, semantic communications are applied to multi-modal scenarios to boost communication efficiency and save communication resources. However, the existing end-to-end neural network (NN) based framework without the channel encoder/decoder is incompatible with modern digital communication systems. Moreover, most end-to-end designs are task-specific and require re- design and re- training for new tasks, which limits their applications. In this paper, we propose a distributed multi-modal semantic communication framework incorporating the conventional channel encoder/decoder. We adopt NN-based semantic encoder and decoder to extract correlated semantic information contained in different modalities, including speech, text, and image. Based on the proposed framework, we further establish a general rate-adaptive coding mechanism for various types of multi-modal semantic tasks. In particular, we utilize unequal error protection based on semantic importance, which is derived by evaluating the distortion bound of each modality. We further formulate and solve an optimization problem that aims at minimizing inference delay while maintaining inference accuracy for semantic tasks. Numerical results show that the proposed mechanism fares better than both conventional communication and existing semantic communication systems in terms of task performance, inference delay, and deployment complexity.

Task-oriented semantic communications have achieved significant performance gains. However, the employed deep neural networks in semantic communications have to be updated when the task is changed or multiple models need to be stored for performing different tasks. To address this issue, we develop a unified deep learning-enabled semantic communication system (U-DeepSC), where a unified end-to-end framework can serve many different tasks with multiple modalities of data. As the number of required features varies from task to task, we propose a vector-wise dynamic scheme that can adjust the number of transmitted symbols for different tasks. Moreover, our dynamic scheme can also adaptively adjust the number of transmitted features under different channel conditions to optimize the transmission efficiency. Particularly, we devise a lightweight feature selection module (FSM) to evaluate the importance of feature vectors, which can hierarchically drop redundant feature vectors and significantly accelerate the inference. To reduce the transmission overhead, we then design a unified codebook for feature representation to serve multiple tasks, where only the indices of these task-specific features in the codebook are transmitted. According to the simulation results, the proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task but with significant reduction in both transmission overhead and model size.

Wireless information surveillance has received widespread attention due to the urgency of monitoring growing suspicious communications. This paper considers a challenging surveillance scenario, where the monitor (E) intends to eavesdrop the suspicious multihop communications from a long distance to ensure concealment, leading to the eavesdropping condition undesirable. To tackle this challenging, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted surveillance strategy, where the STAR-RIS, acts as a “bridge”, is deliberately deployed between the suspicious system and E, to adaptively transmit and reflect the suspicious signal and E's jamming signal, and then facilitate E's eavesdropping. Specifically, we consider the adaptive rate transmission and the delay-limited transmission for the suspicious system, and accordingly maximize E's instantaneous and average eavesdropping rate, by jointly optimizing the passive transmission- and reflection-coefficient matrices at the STAR-RIS, the jamming set and jamming power allocations of E (across all hops). The optimization problems in both transmission modes include numerous integer and continuous variables and thus are highly non-convex. Nevertheless, we show by detailed analysis that the original problem in each mode can be solved by only considering two possible cases, where E and the STAR-RIS intend to enhance and reduce the suspicious transmission rate, respectively. More importantly, in each case, many of necessary prerequisites for achieving the optimal solution are first determined analytically. Armed with these, the optimization problem then can be solved by leveraging the successive convex approximation technique and the simple search. As demonstrated by simulation results, since our proposed strategy is adaptive in term of varying the suspicious transmission rate, it will achieve significant eavesdropping performance gain as compared to other competitive benchmarks.

Movable antenna (MA) technology is highly promising for improving communication performance, due to its advantage of flexibly adjusting positions of antennas to reconfigure channel conditions. In this paper, we investigate MAs-assisted secure transmission under a legitimate transmitter Alice, a legitimate receiver Bob and multiple eavesdroppers. Specifically, we consider a practical scenario where Alice has no any knowledge about the instantaneous non-line-of-sight component of the wiretap channel. Under this setup, we evaluate the secrecy performance by adopting the secrecy outage probability metric, the tight approximation of which is first derived by interpreting the Rician fading as a special case of Nakagami fading and concurrently exploiting the Laguerre series approximation. Then, we minimize the secrecy outage probability by jointly optimizing the transmit beamforming and positions of antennas at Alice. However, the problem is highly non-convex because the objective includes the complex incomplete gamma function. To tackle this challenge, we, for the first time, effectively approximate the inverse of the incomplete gamma function as a simple linear model. Based on this approximation, we arrive at a simplified problem with a clear structure, which can be solved via the developed alternating projected gradient ascent (APGA) algorithm. Considering the high complexity of the APGA, we further design another scheme where the zero-forcing based beamforming is adopted by Alice, and then we transform the problem into minimizing a simple function which is only related to positions of antennas at Alice. Such problem is well-solved via another projected gradient descent algorithm developed with a lower complexity. As demonstrated by simulations, our proposed schemes achieve significant performance gains compared to conventional schemes based on fixed-position antennas.

Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

In recent years, the application of neural networks (NNs) in wireless communication has garnered widespread attention and proven successful. However, conventional learning-based NNs often suffer from poor calibration, meaning that they struggle to reliably quantify prediction confidence and lack proper uncertainty estimation. This limitation becomes especially critical for next generation communication systems, particularly in complex industrial scenarios with stringent reliability requirements. Previous efforts to enhance model calibration have primarily centered on modifying NNs’ training processes. However, these methods often demand significant computing resources, making them impractical for resource-constrained scenarios. In this paper, we investigate a distributed wireless communication system involving multiple users and propose a novel approach to improve model calibration. Our method focuses on enhancing calibration during the inference stage of NNs by introducing a power control mechanism. Notably, existing research indicates that many NNs exhibit overconfidence, i.e., the NN’s confidence exceeds its actual accuracy. Leveraging this insight, we exploit the inherent noise and fading in wireless systems to naturally reduce the NN’s confidence while preserving accuracy. To achieve this, we employ linear relaxation-based perturbation analysis (LiRPA) to approximate the relationship between the perturbed output and the input perturbation of the NN. Subsequently, we devise an optimization problem by leveraging the analyzed relationship and the definition of perfect calibration. It is aimed at finding the input perturbation that maximizes the probability of the model achieving perfect calibration. Finally, considering different channel conditions and a given specific modulation method, we derive the optimal transmission power based on bit error rate (BER) formula. Simulation results demonstrate that our proposed power control method exhibits significant advantages in model calibration compared to several traditional approaches.

In integrated sensing, computation, and communication (ISCC) systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module that can detect whether the sensing target is static. Subsequently, we analyze the sensing performance of the proposed framework and formulate a sensing accuracy maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy and derive a threshold selection policy that demonstrates the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis, and extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

Recently, deep learning-enabled joint-source channel coding (JSCC) has received increasing attention due to its great success in image transmission. However, most existing JSCC studies only focus on single-input single-output (SISO) channels. In this paper, we first propose a JSCC system for wireless image transmission over multiple-input multiple-output (MIMO) channels. As the complexity of an image determines its reconstruction difficulty, the JSCC achieves quite different reconstruction performances on different images. Moreover, we observe that the images with higher reconstruction qualities are generally more robust to the noise, and can be allocated with less communication resources than the images with lower reconstruction qualities. Based on this observation, we propose an adaptive channel state information (CSI) feedback scheme for precoding, which improves the effectiveness by adjusting the feedback overhead. In particular, we develop a performance evaluator to predict the reconstruction quality of each image, so that the proposed scheme can adaptively decrease the CSI feedback overhead for the transmitted images with high predicted reconstruction qualities in the JSCC system. We perform experiments to demonstrate that the proposed scheme can significantly improve the image transmission performance with much-reduced feedback overhead.

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets.

In conventional supervised deep learning based channel estimation algorithms, a large number of training samples are required for offline training. However, in practical communication systems, it is difficult to obtain channel samples for every signal-to-noise ratio (SNR). Furthermore, the generalization ability of these deep neural networks (DNN) is typically poor. In this work, we propose a one-shot self-supervised learning framework for channel estimation in multi-input multi-output (MIMO) systems. The required number of samples for offline training is small and our approach can be directly deployed to adapt to variable channels. Our framework consists of a traditional channel estimation module and a denoising module. The denoising module is designed based on the one-shot learning method Self2Self and employs Bernoulli sampling to generate training labels. Besides,we further utilize a blind spot strategy and dropout technique to avoid overfitting. Simulation results show that the performance of the proposed one-shot self-supervised learning method is very close to the supervised learning approach while obtaining improved generalization ability for different channel environments.

With the great success of deep learning (DL) in image classification, speech recognition, and other fields, more and more studies have applied various neural networks (NNs) to wireless resource allocation. Generally speaking, these artificial intelligent (AI) models are trained under some special learning hypotheses, especially that the statistics of the training data are static during the training stage. However, the distribution of channel state information (CSI) is constantly changing in the real-world wireless communication environment. Therefore, it is essential to study effective dynamic DL technologies to solve wireless resource allocation problems. In this paper, we propose a novel framework, named meta-gating, for solving resource allocation problems in an episodically dynamic wireless environment, where the CSI distribution changes over periods and remains constant within each period. The proposed framework, consisting of an inner network and an outer network, aims to adapt to the dynamic wireless environment by achieving three important goals, i.e., seamlessness, quickness and continuity. Specifically, for the former two goals, we propose a training method by combining a model-agnostic meta-learning (MAML) algorithm with an unsupervised learning mechanism. With this training method, the inner network is able to fast adapt to different channel distributions because of the good initialization. As for the goal of ‘continuity’, the outer network can learn to evaluate the importance of inner network’s parameters under different CSI distributions, and then decide which subset of the inner network should be activated through the gating operation. Additionally, we theoretically analyze the performance of the proposed meta-gating framework. Simulation results demonstrate that the proposed meta-gating framework can well achieve the three important goals compared with existing state-of-the-art algorithms.

Unmanned aerial vehicle (UAV) plays an important role in wireless communication systems, due to the additional degree of freedom realized from its flexible deployment. Driven by this advantage and considering the security issue, this paper aims to investigate UAV-enabled proactive eavesdropping over distributed transmit beamforming-based suspicious communications. Specifically, for the suspicious system, there are multiple suspicious clusters aiming to communicate with the suspicious destination (D) using mutually orthogonal frequency bands, and distributed transmit beamforming is exploited by each cluster to strengthen the signal receiving quality at D. For the legitimate party, the full-duplex UAV exploits one antenna to jam D and uses the other antenna to overhear the signals of the suspicious clusters concurrently. By resorting to the Laguerre series approximation and the central limit theorem, we first characterize, in closed form, the approximated distributions of the receiving signal-to-interference-noise ratio (SINR) at D and the UAV, which are shown to be very tight. Based on this analysis and considering that the suspicious system works in the delay-limited transmission mode or the delay-sensitive transmission mode, we aim to maximize the minimum eavesdropping success probability of the UAV for those suspicious communications links, by jointly adjusting the UAV’s deployment and jamming power allocations over different frequency bands. The problem is highly non-convex. To tackle this, we develop an alternative optimization framework and further a novel and low-complexity solution in the high SNR regime to the optimization problem. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This paper studies information surveillance over wireless-powered suspicious multiuser communications, where multiple suspicious transmitters (STs) first harvest wireless energy from the suspicious power beacon (PB) in phase I and then communicate with the suspicious destination (SD) in phase II over mutually orthogonal channels, and there is a legitimate monitor (M) aiming to overhear the suspicious signals of the STs based on wireless-powered cooperative jamming. Specifically, the jammers first harvest energy from M in phase I and then interfere with the SD in phase II. Considering the fairness issue, M aims to maximize the minimum eavesdropping success probability of these suspicious signals, by jointly optimizing its transmit power in phase I and the jammers’ power allocations in phase II. To solve the problem, first we strictly prove that M should exhaust its maximum power for the energy transfer, even the additional energy can be harvested by the STs to enhance their transmit power and rate. Then, the general successive convex approximation (SCA) technique and one low-complexity solution are respectively proposed to optimize the jammers’ power allocations. Further, the closed-form jamming power allocations are derived in the high signal-to-noise ratio range to reveal some interesting insights. The joint deployments of M and the jammers are also investigated to enhance the eavesdropping performance. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

In this paper, we investigate the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multi-user multiple-input multiple-output (MIMO) system. The joint optimization framework for user clustering, downlink beamforming and power allocation scheme is formulated as a novel mixed-integer non-linear program (MINLP), where the aim is to minimize the total transmission power while satisfying quality-of-service (QoS), user clustering and power constraints. Owing to the non-convexity and combinatorial nature of the problem, obtaining an optimal solution is challenging. To tackle this issue, we first develop an algorithm based on branch-and-bound (BB), whereby the feasible space is successively partitioned and searched by means of lower and upper bounds on the objective function. While this algorithm is shown to return an$\epsilon $-optimal solution within a finite number of iterations, it entails high computational complexity. Considering this limitation, we then reformulate the original problem into a more tractable form and conceive a low-complexity algorithm for its solution based on the penalty dual-decomposition technique. The proposed joint design algorithms for MIMO NOMA are evaluated by means of simulations over mmWave channels. Results show significant improvements in terms of total transmit power and spectral efficiency compared to benchmark approaches.

The performance of over-the-air computation (AirComp) systems degrades due to the hostile channel conditions of wireless devices (WDs), which can be significantly improved by the employment of reconfigurable intelligent surfaces (RISs). However, the conventional RISs require that the WDs have to be located in the half-plane of the reflection space, which restricts their potential benefits. To address this issue, the novel family of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is considered in AirComp systems to improve the computation accuracy across a wide coverage area. To minimize the computation mean-squared-error (MSE) in STAR-RIS assisted AirComp systems, we propose a joint beamforming design for optimizing both the transmit power at the WDs, as well as the passive reflect and transmit beamforming matrices at the STAR-RIS, and the receive beamforming vector at the fusion center (FC). Specifically, in the updates of the passive reflect and transmit beamforming matrices, closed-form solutions are derived by introducing an auxiliary variable and exploiting the coupled binary phase-shift conditions. Moreover, by assuming that the number of antennas at the FC and that of elements at the STAR-RIS/RIS are sufficiently high, we theoretically prove that the STAR-RIS assisted AirComp systems provide higher computation accuracy than the conventional RIS assisted systems. Our numerical results show that the proposed beamforming design outperforms the benchmark schemes relying on random phase-shift constraints and the deployment of conventional RIS. Moreover, its performance is close to the lower bound achieved by the beamforming design based on the STAR-RIS dispensing with coupled phase-shift constraints.

Integrated sensing and communication (ISAC) has recently been considered as a promising approach to save spectrum resources and reduce hardware cost. Meanwhile, as information security becomes increasingly more critical issue, government agencies urgently need to legitimately monitor suspicious communications via proactive eavesdropping. Thus, in this paper, we investigate a wireless legitimate surveillance system with radar function. We seek to jointly optimize the receive and transmit beamforming vectors to maximize the eavesdropping success probability which is transformed into the difference of signal-to-interference-plus-noise ratios (SINRs) subject to the performance requirements of radar and surveillance. The formulated problem is challenging to solve. By employing the Rayleigh quotient and fully exploiting the structure of the problem, we apply the divide-and-conquer principle to divide the formulated problem into two subproblems for two different cases. For the first case, we aim at minimizing the total transmit power, and for the second case we focus on maximizing the jamming power. For both subproblems, with the aid of orthogonal decomposition, we obtain the optimal solution of the receive and transmit beamforming vectors in closed-form. Performance analysis and discussion of some insightful results are also carried out. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithm in terms of eavesdropping success probability.

As an important part of the fifth generation (5G) mobile networks, unmanned aerial vehicles (UAVs) have been applied in various communication scenarios due to their high operability and low cost. In this paper, we investigate a multi-UAV communication system with moving users and consider the co-channel interference caused by the transmissions of all other UAVs. To ensure the fairness, we maximize the minimum average user rate during the observed time by jointly optimizing UAVs’ trajectories, transmission power, and user association. Considering that UAVs can cover a large area for communications, UAVs do not need to move as soon as the users move. Therefore, a two-timescale structure is proposed for the considered scenario, where the UAVs’ trajectories are optimized based on the channel state information (CSI) in a long timescale, while the transmission power and the user association are optimized based on the instantaneous CSI in a short timescale. To effectively tackle this challenging non-convex problem with both discrete and continuous variables, we propose a joint neural network (NN) design, where a deep reinforcement learning based Pointer Network named advantage pointer-critic (APC) is applied to optimize discrete variables and a deep-unfolding NN is used to optimize the continuous variables. Specifically, we first formulate a Markov decision process to model the user association, and then employ the APC network trained by the advantage actor-critic algorithm to address it. The APC network consists of a Pointer Network and a Multilayer Perceptron. As for the deep-unfolding NN, we first develop a block coordinate descent based algorithm to optimize the UAVs’ trajectories and transmission power, and then unfold the algorithm into a layer-wise NN with introduced trainable parameters. These two networks are jointly trained in an unsupervised fashion. Simulation results validate that the proposed joint NN significantly outperforms the optimization algorithm with much lower complexity, and achieves good performances on scalability and generalization ability.

Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus causes the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.

The time of arrival (TOA)-based localization techniques, which need to estimate the delay of the line-of-sight (LoS) path, have been widely employed in location-aware networks. To achieve a high-accuracy delay estimation, a number of multiband-based algorithms have been proposed recently, which exploit the channel state information (CSI) measurements over multiple non-contiguous frequency bands. However, to the best of our knowledge, there still lacks an efficient scheme that fully exploits the multiband gains when the phase distortion factors caused by hardware imperfections are considered, due to that the associated multi-parameter estimation problem contains many local optimums and the existing algorithms can easily get stuck in a “bad” local optimum. To address these issues, we propose a novel two-stage global estimation (TSGE) scheme for multiband delay estimation. In the coarse stage, we exploit the group sparsity structure of the multiband channel and propose a Turbo Bayesian inference (Turbo-BI) algorithm to achieve a good initial delay estimation based on a coarse signal model, which is transformed from the original multiband signal model by absorbing the carrier frequency terms. The estimation problem derived from the coarse signal model contains fewer local optimums and thus a more stable estimation can be achieved than directly using the original signal model. Then in the refined stage, with the help of coarse estimation results to narrow down the search range, we perform a global delay estimation using a particle swarm optimization-least square (PSO-LS) algorithm based on a refined multiband signal model to exploit the multiband gains to further improve the estimation accuracy. Simulation results show that the proposed TSGE significantly outperforms the benchmarks with comparative computational complexity.

This paper investigates a new concept of employing intelligent reflecting surface (IRS) to enhance the monitoring performance of wireless information surveillance system, where a full-duplex legitimate monitor is employed to eavesdrop the suspicious transmission from a transmitter to a receiver with the help of an IRS. Under this setup, we consider three IRS deployment strategies, where the IRS is placed near the suspicious transmitter, the suspicious receiver and the legitimate monitor, respectively. First, the monitoring rate achievable by the IRS-aided surveillance system under each deployment strategy is analyzed, which reveals that deploying the IRS near the suspicions transmitter achieves the maximum rate with an asymptotically large number of IRS reflecting elements. Next, efficient algorithms are proposed to maximize the monitoring rate by jointly optimizing the receive and jamming beamforming vectors at the legitimate monitor and the reflection coefficients at the IRS. In particular, a performance upper bound is obtained via properly characterizing the upper and lower bounds of the jamming signal power and using semidefinite relaxation (SDR), while low-complexity algorithms based on the penalty dual decomposition (PDD) framework are also presented to achieve near-optimal performance. Finally, numerical results are presented to validate our analysis as well as the effectiveness of the proposed algorithms, and useful insights are drawn.

Reconfigurable intelligent surfaces (RISs) achieve high passive beamforming gains for signal enhancement or interference nulling by dynamically adjusting their reflection coefficients. Their employment is particularly appealing for improving both the wireless security and the efficiency of radio frequency (RF)-based wireless power transfer. Motivated by this, we conceive and investigate a RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system designed for information and power transfer from a base station (BS) to an information user (IU) and to multiple energy users (EUs), respectively. Moreover, the EUs are also potential eavesdroppers that may overhear the communication between the BS and IU. We adopttwo-timescaletransmission for reducing the signal processing complexity as well as channel training overhead, and aim for maximizing the average worst-case secrecy rate achieved by the IU. This is achieved by jointly optimizing theshort-termtransmit beamforming vectors at the BS (including information and energy beams) as well as thelong-termphase shifts at the RIS, under the energy harvesting constraints considered at the EUs and the power constraint at the BS. The stochastic optimization problem formulated is non-convex with intricately coupled variables, and is non-smooth due to the existence of multiple EUs/eavesdroppers. No standard optimization approach is available for this challenging scenario. To tackle this challenge, we propose a smooth approximation aided stochastic successive convex approximation (SA-SSCA) algorithm. Furthermore, a low-complexity heuristic algorithm is proposed for reducing the computational complexity without unduly eroding the performance. Simulation results show the efficiency of the RIS in securing SWIPT systems. The significant performance gains achieved by our proposed algorithms over the relevant benchmark schemes are also demonstrated.

In this paper, we investigate a multi-UAV communication system with moving users and consider the co-channel interference caused by the transmissions of all other UAVs. To ensure the fairness of moving users, we maximize the minimum average user rate during the observed time by jointly optimizing UAVs' trajectories, transmission power, and user association. To effectively tackle this non-convex problem with both discrete and continuous variables, we propose a joint neural network (NN) design, where a network named advantage pointer-critic (APC) is applied to optimize discrete variables and a deep-unfolding NN is used to optimize continuous variables. Specifically, we first elaborately formulate a Markov decision process to model the user association, and then use the APC network trained by the advantage actor-critic algorithm to address it. As for the deep-unfolding NN, we first develop a block coordinate descent based algorithm to optimize UAVs' trajectories and transmission power, and then unfold this algorithm into a layer-wise NN with introduced trainable parameters. These two networks are jointly trained in an unsupervised fashion. Simulation results validate that the proposed joint NN significantly outperforms the mathematical optimization algorithm with much lower complexity.

In location-aware networks, the time of arrival (TOA)-based localization techniques have been widely employed. To achieve high-accuracy delay estimation, a number of multiband-based algorithms have been proposed recently, which exploit the channel state information (CSI) measurements over multiple non-contiguous frequency bands. However, to our best knowledge, there still lacks an efficient scheme that fully exploits the multiband gains when phase distortion factors are considered, due to that the associated multi-parameter estimation problem contains many local optimums and the existing algorithms can easily get stuck in a “bad” local optimum. To address these issues, we propose a novel two-stage global estimation (TSGE) scheme for multiband delay estimation. In the coarse stage, we propose a weighted multiple signal classification (MUSIC) algorithm to achieve an initial delay estimation based on a coarse signal model. The estimation problem derived from the coarse signal model contains less local optimums and thus a more stable estimation can be achieved than directly using the original signal model. Then in the refined stage, with the help of coarse estimation results to narrow down the search range, we perform a global delay estimation using a particle swarm optimization (PSO) algorithm based on a refined multiband signal model to exploit the multiband gains to further improve the estimation accuracy. Simulation results show that the proposed TSGE significantly outperforms the benchmarks.

The task-oriented semantic communication sys-tems have achieved significant performance gain, however, the paradigm that employs a model for a specific task might be limited, since the system has to be updated once the task is changed or multiple models are stored for serving various tasks. To address this issue, we firstly propose a unified deep learning enabled semantic communication system (U-DeepSC), where a unified model is developed to serve various transmission tasks. To jointly serve these tasks in one model with fixed parameters, we employ domain adaptation in the training procedure to specify the task-specific features for each task. Thus, the system only needs to transmit the task-specific features, rather than all the features, to reduce the transmission overhead. Moreover, since each task is of different difficulty and requires different number of layers to achieve satisfactory performance, we develop the multi-exit architecture to provide early-exit results for relatively simple tasks. In the experiments, we employ a proposed U-DeepSC to serve five tasks with multi-modalities. Simulation re-sults demonstrate that our proposed U-DeepSC achieves compa-rable performance to the task-oriented semantic communication system designed for a specific task with significant transmission overhead reduction and much less number of model parameters.

Deep-unfolding has received great attention since it achieves satisfactory performance with relatively low complexity. Typically, the deep-unfolding networks are restricted to a fixed-depth for all inputs. However, the optimal number of layers required for convergence changes with different inputs. In this paper, we first develop a framework of deep deterministic policy gradient (DDPG)-driven deep-unfolding with adaptive depth for different inputs. Specifically, the optimization variables, trainable parameters, and the deep-unfolding architecture are designed as the state, action, and state transition of DDPG, respectively. Then, this framework is employed to deal with the channel estimation problem in massive multiple-input multiple-output systems. Specifically, first of all we formulate the channel estimation problem with off-grid basis and develop a sparse Bayesian learning (SBL)-based algorithm to solve it. Secondly, the SBL-based algorithm is unfolded into a layer-wise structure with a set of introduced trainable parameters. Thirdly, the proposed DDPG-driven deep-unfolding framework is employed to solve this channel estimation problem based on the unfolded structure of the SBL-based algorithm. Simulation results show that the proposed algorithm outperforms the conventional algorithms with much reduced number of layers.

Although the semantic communications have exhibited satisfactory performance in a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise is a particular kind of noise in semantic communication systems, which refers to the misleading between the intended semantic symbols and received ones. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. Particularly, we analyze the causes of semantic noise and propose a practical method to generate it. To remove the effect of semantic noise, adversarial training is proposed to incorporate the samples with semantic noise in the training dataset. Then, the masked autoencoder (MAE) is designed as the architecture of a robust semantic communication system, where a portion of the input is masked. To further improve the robustness of semantic communication systems, we firstly employ the vector quantization-variational autoencoder (VQ-VAE) to design a discrete codebook shared by the transmitter and the receiver for encoded feature representation. Thus, the transmitter simply needs to transmit the indices of these features in the codebook. Simulation results show that our proposed method significantly improves the robustness of semantic communication systems against semantic noise with significant reduction on the transmission overhead.

Over-the-air computation (AirComp) has been recognized as a promising technique of enabling the fusion center (FC) to aggregate the data gleaned from massive distributed wireless devices (WDs). Nevertheless, the computational performance of AirComp is significantly affected by the potentially poor channel conditions between the WDs and FC due to physical obstacles. For mitigating this limitation, we employ reconfigurable intelligent surfaces (RISs) for enhancing the reception quality and, thus, improve the computational performance of AirComp. Moreover, the previous studies of RIS-assisted AirComp tend to rely on the real-time channel state information (CSI), leading to excessive overhead since the number of RIS elements is large. To mitigate the above issue, a mixed-timescale penalty-dual-decomposition (MTPDD) algorithm is proposed, in which the transmit power of each WD, the receive beamforming vector at the FC, and the passive beamforming matrix of the RIS are jointly optimized. We aim to minimize the average computation mean-squared error (MSE) over time with reduced signaling overhead. Specifically, at each time slot, we optimize the short-term transmit power and receive the beamforming vector based on the real-time low-dimensional CSI vectors. In contrast, in each frame, we update the long-term passive RIS beamforming matrix based on the channel statistics. Besides, we analyzed both the convergence and the computational complexity of the proposed algorithms. Simulation results verify the benefits of our proposed MTPDD beamforming algorithm. It is also shown that the performance of the MTPDD algorithm approaches that achieved by the scheme using real-time perfect CSI with reduced signal overhead.

Integrated sensing and communication (ISAC) has been regarded as one of the most promising technologies for future wireless communications. However, the mutual interference in the communication radar coexistence system cannot be ignored. Inspired by the studies of reconfigurable intelligent surface (RIS), we propose a double-RIS-assisted coexistence system where two RISs are deployed for enhancing communication signals and suppressing mutual interference. We aim to jointly optimize the beamforming of RISs and radar to maximize communication performance while maintaining radar detection performance. The investigated problem is challenging, and thus we transform it into an equivalent but more tractable form by introducing auxiliary variables. Then, we propose a penalty dual decomposition (PDD)-based algorithm to solve the resultant problem. Moreover, we consider two special cases: the large radar transmit power scenario and the low radar transmit power scenario. For the former, we prove that the beamforming design is only determined by the communication channel and the corresponding optimal joint beamforming strategy can be obtained in closed-form. For the latter, we minimize the mutual interference via the block coordinate descent (BCD) method. By combining the solutions of these two cases, a low-complexity algorithm is also developed. Finally, simulation results show that both the PDD-based and low-complexity algorithms outperform benchmark algorithms.

In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which consists of deep neural network (DNN)-aided pilot training, channel feedback, and hybrid analog-digital (HAD) precoding. Specifically, we develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter. To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed. The analog precoders are designed by the long-term DNN based on the CSI statistics and updated once in a frame consisting of a number of time slots. In contrast, the digital precoders are optimized by the short-term DNN at each time slot based on the estimated low-dimensional equivalent CSI matrices. A two-timescale training method is also developed for the proposed DNN with a binary layer. We then analyze the generalization ability and signaling overhead for the proposed DNN based algorithm. Simulation results show that our proposed technique significantly outperforms conventional schemes in terms of bit-error rate performance with reduced signaling overhead and shorter pilot sequences.

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to the large number of antennas, the conventional channel estimation and hybrid beamforming algorithms generally require high computational complexity and signaling overhead. In this work, we propose an end-to-end deep-unfolding neural network (NN) joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the system sum rate in time-division duplex (TDD) massive MIMO. Specifically, the recursive least-squares (RLS) algorithm and stochastic successive convex approximation (SSCA) algorithm are unfolded for channel estimation and hybrid beamforming, respectively. In order to reduce the signaling overhead, we consider a mixed-timescale hybrid beamforming scheme, where the analog beamforming matrices are optimized based on the channel state information (CSI) statistics offline, while the digital beamforming matrices are designed at each time slot based on the estimated low-dimensional equivalent CSI matrices. We jointly train the analog beamformers together with the trainable parameters of the RLS and SSCA induced deep-unfolding NNs based on the CSI statistics offline. During data transmission, we estimate the low-dimensional equivalent CSI by the RLS induced deep-unfolding NN and update the digital beamformers. In addition, we propose a mixed-timescale deep-unfolding NN where the analog beamformers are optimized online, and extend the framework to frequency-division duplex (FDD) systems where channel feedback is considered. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms with reduced computational complexity and signaling overhead.

We investigate proactive eavesdropping with one half-duplex legitimate monitor (E) in unmanned aerial vehicle (UAV)-enabled suspicious relaying systems, which consist of a suspicious transmitter (ST), a suspicious UAV-based relay (SU) and a suspicious destination (SD). Under this setup, we propose two jamming-assisted eavesdropping strategies, namely, “Eavesdrop-Then-Jam” (ETJ) and “Jam-Then-Eavesdrop” (JTE), to maximize the eavesdropping throughput of E. Specifically, for ETJ, E first eavesdrops the suspicious signal of the ST in the ST-SU phase and then jams the SD in the SU-SD phase; for JTE, E first jams the SU in the ST-SU phase and then eavesdrops the suspicious signal of the SU in the SU-SD phase.However, in both strategies, as the jamming power of E (independent variable) changes, the SU can also adaptively adjust its deployment (dependent variable) to maximize the benefit of the suspicious system. Facing this challenge, we can only employ the undesirable exhaustive search over both the independent and dependent variables to achieve the optimal solution. To decrease the complexity, we further derive a tight approximation of the Lambert function and then develop easy-to-implement algorithms to find the sub-optimal solutions. Numerical results demonstrate the effectiveness of our proposed strategies compared to conventional passive eavesdropping.

This letter studies jamming-assisted proactive eavesdropping over multiple orthogonal suspicious links with a wireless-powered monitor. Considering the shortage of energy, the monitor adopts the power splitting technique to divide each of the received suspicious signals into two parts for information decoding and energy harvesting, and then exploits the accumulated energy to jam over those suspicious links, to reduce the corresponding suspicious communication rate and facilitate the eavesdropping of the monitor itself. Our objective is to maximize the minimum eavesdropping success probability of the monitor for all received suspicious signals, by jointly optimizing its power splitting ratios and jamming power allocations. The formulated problem involves the complex Lambert function and is highly non-convex. To tackle this challenge, first we derive a very tight approximation of the Lambert function based on its key property. Then, we employ the general successive convex approximation (SCA)-based technique to iteratively find a locally optimal solution. Moreover, we propose a parallel coordinate descent (PCD)-based low-complexity algorithm to obtain a sub-optimal solution. Numerical results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This letter studies proactive eavesdropping with one legitimate monitor (E) in the classical single-hop suspicious communication. Specifically, unlike all previous works that ignored the suspicious channel training phase and just considered the jamming power optimization of E in the suspicious data transmission phase to facilitate eavesdropping, this letter advances the research by comprehensively investigating the jamming power allocation of E in both phases, with the purpose of maximizing its eavesdropping success probability. Under this setup, we first derive an exact expression of the objective and reveal the existence of a fundamental trade-off in deciding the jamming power allocation, for which a simple one-dimensional search is employed to find the optimal solution. To simplify the analysis, we further derive a tight approximation of the objective and then develop a very fast alternating optimization algorithm to find the sub-optimal jamming power allocation. Moreover, we extend our analysis to the case where the channel state information is available at the suspicious transmitter via channel feedback. Simulation results demonstrate the effectiveness of our proposed scheme compared to competitive benchmarks.

Driven by the demand for massive and accurate sensing data to achieve wireless network intelligence under a limited available spectrum, the coexistence between radar and communication systems has attracted public attention. In this paper, we investigate a novel dual-functional full-duplex relay aided radar-communication system where the phased-array radar is employed at the amplify-and-forward (AF) relay. A joint transceiver design is proposed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all detection directions at the radar receiver under communication quality-of-service and total energy constraints. The formulated optimization problem is particularly challenging due to the highly nonconvex objective function and constraints. Based on the problem structure, we equivalently decompose it into the radar-energy and relay-energy minimization problems under SINR requirements. To solve the radar-energy minimization problem, we propose a low-complexity algorithm based on the alternating direction method of multipliers to optimize the radar transmit power and receiver. The relay-energy minimization problem can be simplified into an equivalent quadratic programming problem by introducing an insightful unitary matrix. Then, the closed-form expression for the AF relay beamforming matrix can be derived, which is jointly determined by the channel condition of relay communication and the detection direction of the radar. After that, we introduce the overall transceiver design algorithm to the original problem and discuss its optimality and computational complexity. Simulation results verify that the proposed algorithm significantly outperforms other benchmark algorithms.

Achieving high channel estimation accuracy and reducing hardware cost as well as power dissipation constitute substantial challenges in the design of massive multiple-input multiple-output (MIMO) systems. To resolve these difficulties, sophisticated pilot designs have been conceived for the family of energy-efficient hybrid analog-digital (HAD) beamforming architecture relying on adaptive-resolution analog-to-digital converters (RADCs). In this paper, we jointly optimize the pilot sequences, the number of RADC quantization bits and the hybrid receiver combiner in the uplink of multiuser massive MIMO systems. We solve the associated mean square error (MSE) minimization problem of channel estimation in the context of correlated Rayleigh fading channels subject to practical constraints. The associated mixed-integer problem is quite challenging due to the nonconvex nature of the objective function and of the constraints. By relying on advanced fractional programming (FP) techniques, we first recast the original problem into a more tractable yet equivalent form, which allows the decoupling of the fractional objective function. We then conceive a pair of novel algorithms for solving the resultant problems for codebook-based and codebook-free pilot schemes, respectively. To reduce the design complexity, we also propose a simplified algorithm for the codebook-based pilot scheme. Our simulation results confirm the superiority of the proposed algorithms over the relevant state-of-the-art benchmark schemes.

This paper investigates the passive beamforming and deployment design for an intelligent reflecting surface (IRS) aided full-duplex (FD) wireless system, where an FD access point (AP) communicates with an uplink (UL) user and a downlink (DL) user simultaneously over the same time-frequency dimension with the help of IRS. Under this setup, we consider three deployment cases: 1) two distributed IRSs placed near the UL user and DL user, respectively; 2) one centralized IRS placed near the DL user; 3) one centralized IRS placed near the UL user. In each case, we aim to minimize the weighted sum transmit power consumption of the AP and UL user by jointly optimizing their transmit power and the passive reflection coefficients at the IRS (or IRSs), subject to the UL and DL users’ rate constraints and the uni-modulus constraints on the IRS reflection coefficients. First, we analyze the minimum transmit power required in the IRS-aided FD system under each deployment scheme, and compare it with that of the corresponding half-duplex (HD) system. We show that the FD system outperforms its HD counterpart for all IRS deployment schemes, while the distributed deployment further outperforms the other two centralized deployment schemes. Next, we transform the challenging power minimization problem into an equivalent but more tractable form and propose an efficient algorithm to solve it based on the block coordinate descent (BCD) method. Finally, numerical results are presented to validate our analysis as well as the efficacy of the proposed passive beamforming design.

Radar sensing will be integrated into the 6G communication system to support various applications. In this integrated sensing and communication system, a radar target may also be a communication channel scatterer. In this case, the radar and communication channels exhibit certain joint burst sparsity. We propose a two-stage joint pilot optimization, target detection and channel estimation scheme to exploit such joint burst sparsity and pilot beamforming gain to enhance detection/estimation performance. In Stage 1, the base station (BS) sends downlink pilots (DP) for initial target search, and the user sends uplink pilots (UP) for channel estimation. Then the BS performs joint target detection and channel estimation. In Stage 2, the BS exploits the prior information obtained in Stage 1 to optimize the DP signal to further refine the performance. A Turbo Sparse Bayesian inference algorithm is proposed for joint target detection and channel estimation in both stages. The pilot optimization problem in Stage 2 is a semi-definite programming with rank-1 constraints. By replacing the rank-1 constraint with a tight and smooth approximation, we propose an efficient pilot optimization algorithm based on the majorization-minimization (MM) method. Simulations verify the advantages of the proposed scheme.

Distributed machine learning has been extensively employed in wireless systems, which can leverage abundant data distributed over massive devices to collaboratively train a high-quality global model. The research efforts of recent works have focused on improving performance (e.g., communication efficiency, energy efficiency, and scalability) of centralized architectures, which include a number of distributed devices and a server. However, centralized architectures may cause congestion at the central node, which is not applicable under some circumstances. To tackle this issue, we introduce a decentralized edge learning framework over wireless networks via unreliable device-to-device (D2D) links and improve its learning performance. The unreliable transmission caused by the channel uncertainty has a negative effect on model convergence. To enhance the performance, we formulate an optimization problem to minimize the overall model deviation under a given latency requirement by jointly optimizing the broadcast data rate and bandwidth allocation. Then, the optimal solution of broadcast data rate is derived and an algorithm for obtaining the optimal bandwidth allocation is developed. Besides, we also propose a decentralized edge learning protocol without a central server and provide the convergence analysis. Finally, extensive simulations are conducted to demonstrate the performance advantages of our proposed algorithm compared against the baseline algorithm.

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD) system. We jointly optimize the active beamforming matrices at the access point (AP) and uplink users, and the passive beamforming matrix at the IRS to maximize the weighted sum-rate of the system. Since it is practically difficult to acquire the channel state information (CSI) for IRS-related links due to its passive operation and large number of elements, we conceive a mixed-timescale beamforming scheme. Specifically, the high-dimensional passive beamforming matrix at the IRS is updated based on the channel statistics while the active beamforming matrices are optimized relied on the low-dimensional real-time effective CSI at each time slot. We propose an efficient stochastic successive convex approximation (SSCA)-based algorithm for jointly designing the active and passive beamforming matrices. Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue. The proposed deep-unfolding NN maintains the structure of the SSCA-based algorithm but introduces a novel non-linear activation function and some learnable parameters induced by the first-order Taylor expansion to approximate the matrix inversion. In addition, we develop a black-box NN as a benchmark. Simulation results show that the proposed mixed-timescale algorithm outperforms the existing single-timescale algorithm and the proposed deep-unfolding NN approaches the performance of the SSCA-based algorithm with much reduced computational complexity when deployed online.

Intelligent reflecting surface (IRS) has emerged as a promising paradigm to improve the capacity and reliability of a wireless communication system by smartly reconfiguring the wireless propagation environment. To achieve the promising gains of IRS, the acquisition of the channel state information (CSI) is essential, which however is practically difficult since the IRS does not employ any transmit/receive radio frequency (RF) chains in general and it has limited signal processing capability. In this paper, we study the uplink channel estimation problem for an IRS-aided multiuser single-input multi-output (SIMO) system. The existing channel estimation approach for IRS-aided multiuser systems mainly consists of three phases, where the direct channels from the base station (BS) to all the users, the reflected channel from the BS to a typical user via the IRS, and the other reflected channels are estimated sequentially based on the estimation results of the previous phases. However, this approach will lead to a serious error propagation issue, i.e., the channel estimation errors in the first and second phases will deteriorate the estimation performance in the second and third phases. To resolve this difficulty, we propose a novel two-phase channel estimation (2PCE) strategy which is able to alleviate the negative effects caused by error propagation and enhance the channel estimation performance with the same amount of channel training overhead as in the existing approach. Specifically, in the first phase, the direct and reflected channels associated with a typical user are estimated simultaneously by varying the reflection patterns at the IRS, such that the estimation errors of the direct channel associated with this typical user will not affect the estimation of the corresponding reflected channel. In the second phase, we estimate the CSI associated with the other users and demonstrate that by properly designing the pilot symbols of the users and the reflection patterns at the IRS, the direct and reflected channels associated with each user can also be estimated simultaneously, which helps to reduce the error propagation. Moreover, the asymptotic mean squared error (MSE) of the proposed 2PCE strategy is analyzed when the least-square (LS) channel estimation method is employed, and we show that the 2PCE strategy can outperform the existing approach. Finally, extensive simulation results are presented to validate the effectiveness of our proposed channel estimation strategy.

Over-the-air computation (AirComp) has received substantial attention, given its ability to aggregate massive amounts of data from distributed wireless devices (WDs). However, the computation accuracy at the fusion center (FC) may be severely affected by receiving data corrupted by the poor channel conditions. To mitigate this issue, we consider the employment of reconfigurable intelligent surfaces (RISs) in the AirComp system considered for improving the quality of received data, and hence improve the computation accuracy. However, most previous contributions on RIS-assisted AirComp systems only employ a single RIS in the resultant single-RIS-assisted (SRIS-assisted) AirComp systems. We develop this concept further for mitigating the deleterious channel effects by conceiving a double-RIS-assisted (DRIS-assisted) AirComp system, where one of the RISs is located near the WDs and the other in the vicinity of the FC. We theoretically prove that the DRIS-assisted AirComp system outperforms its SRIS-assisted counterpart in terms of the resultant computation mean-squared-error (MSE). Furthermore, we propose a pair of algorithms for jointly optimizing the transmit power at the WDs, the receive beamforming vector at the FC, and the passive beamforming matrices at the RISs for minimizing the computational MSE. Specifically, the transmit power is updated by exploiting the Lagrange duality method, while the receive beamforming vector is optimized by utilizing the first-order optimality condition. Furthermore, a pair of techniques are developed for optimizing the passive beamforming matrices at the RISs based on semidefinite relaxation (SDR) and penalty-duality-decomposition (PDD), respectively. Both the complexity and the convergence of the proposed algorithms are analyzed. Finally, simulation results are provided for quantifying the overall performance of the resultant DRIS-assisted AirComp system.

In this work, we investigate the joint design of beam selection and digital precoding matrices for millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) to maximize the sum-rate. To tackle this challenging problem with discrete variables and coupled constraints, we propose an efficient framework of joint neural network (NN) design. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure. Simulation results show that our proposed jointly trained NN significantly outperforms the existing iterative algorithms.

In this paper, we study the application of spatial user clustering along with downlink beamforming in multiple-input multiple-output sparse code multiple access (MIMO-SCMA) systems. A user clustering algorithm based on a constrained K-means method is proposed to limit the number of users in each cluster. Subsequently, a two-stage beamforming approach is developed in which a cluster beamformer and user-specific beamformer obtained from each stage are combined to form the final beamformer for each user. Specifically, in the first stage, the block diagonalization technique is employed to design cluster beamformers so that the inter-cluster interference is removed. In the second stage, an optimization problem is formulated to determine user-specific beamformers for all the users such that the total transmit power is minimized under signal-to-interference-plus-noise ratio (SINR) constraints. The performance of the proposed user clustering and downlink beamforming approaches in MIMO-SCMA systems is evaluated through simulations. The results provide useful insights into the advantages of the proposed scheme in terms of transmit power, and spectral efficiency over benchmark approaches.

Dual-layer iterative algorithms are generally required when solving resource allocation problems in wireless communication systems. Specifically, the spectrum efficiency maximization problem for hybrid precoding architecture is hard to solve by the single-layer iterative algorithm. The dual-layer penalty dual decomposition (PDD) algorithm has been proposed to address the problem. Although the PDD algorithm achieves significant performance, it requires high computational complexity, which hinders its practical applications in real-time systems. To address this issue, we first propose a novel framework for deep-unfolding, where a dual-layer deep-unfolding neural network (DLDUNN) is formulated. We then apply the proposed frame-work to solve the spectrum efficiency maximization problem for hybrid precoding architecture. An efficient DLDUNN is designed based on unfolding the iterative PDD algorithm into a layer-wise structure. We also introduce some trainable parameters in place of the high-complexity operations. Simulation results show that the DLDUNN presents the performance of the PDD algorithm with remarkably reduced complexity.

The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure. The investigated non-convex problem with discrete variables and coupled constraints is challenging to solve and an efficient framework of joint neural network (NN) design is proposed to tackle it. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. The base station is considered to be an agent, where the state, action, and reward function are carefully designed. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure with introduced trainable parameters. Simulation results verify that this jointly trained NN remarkably outperforms the existing iterative algorithms with reduced complexity and stronger robustness.

An unmanned aerial vehicle (UAV)-aided secure communication system is conceived and investigated, where the UAV transmits legitimate information to a ground user in the presence of an eavesdropper (Eve). To guarantee the security, the UAV employs a power splitting approach, where its transmit power can be divided into two parts for transmitting confidential messages and artificial noise (AN), respectively. We aim to maximize the average secrecy rate by jointly optimizing the UAV's trajectory, the transmit power levels and the corresponding power splitting ratios allocated to different time slots during the whole flight time, subject to both the maximum UAV speed constraint, the total mobility energy constraint, the total transmit power constraint, and other related constraints. To efficiently tackle this non-convex optimization problem, we propose an iterative algorithm by blending the benefits of the block coordinate descent (BCD) method, the concave-convex procedure (CCCP) and the alternating direction method of multipliers (ADMM). Specially, we show that the proposed algorithm exhibits very low computational complexity and each of its updating steps can be formulated in a nearly closed form. Besides, it can be easily extended to the case of three-dimensional (3D) trajectory design. Our simulation results validate the efficiency of the proposed algorithm.

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high-complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.

Although the millimeter wave (mmWave) massive multiple‐input and multiple‐output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping‐based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low‐complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple‐signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed‐form solution for updating the step‐size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

With the combination of deep learning and mobile edge computing, the accuracy of the computer vision task in mobile augmented reality (AR) applications can be significantly improved along with the enhancement on the end-to-end latency and energy efficiency. However, no architecture-based delay model for convolutional neural networks (CNNs) has been proposed in edge computing. In this paper, we first develop a new delay model to characterize the relation between the processing delay and the input image size of general CNN models. Then, we formulate a non-convex optimization problem to maximize the learning accuracy under the communication and computation resource constraints. By problem transformation, the optimal resource allocation policy is derived in closed-form and low-complexity search algorithm is also developed. Finally, test results validate the applicability of the delay model and demonstrate the learning accuracy improvement of the proposed algorithm.

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and/or enhancing its capacity. Hence we investigate a mmWave device-to-device (D2D) MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). In order to minimize the system's delay, the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, over orthogonal time slots. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ A/D hybrid beamforming for their transmission. We develop a novel algorithm for jointly optimizing the offloading ratio and the hybrid beamformers. The simulation results show that the proposed algorithm significantly reduces the system's delay compared to the existing algorithms.

In this work, the authors propose a transceiver design strategy based on switched preprocessing (SP) for interference management in K ‐pair MIMO interference channels. Each transmitter performs SP by using a small number of permutation matrices to allocate the entries of its precoder output vector on different transmit antennas. Each arrangement of permutation matrices among the K transmitters gives rise to a set of K parallel point‐to‐point transceivers, referred to as MIMO latent transceiver set (MLTS). Based on the given channel state information (CSI), the optimum MLTS among the available ones is chosen by minimising the squared Euclidean distance between the pre‐estimated noiseless received vector and the true transmit symbol vector. In addition, they consider two CSI error models, i.e. the stochastic error model and the norm‐bounded error model, and for each type they propose robust algorithms for the design of the MLTS associated to the different choices of permutation matrices, which are based on minimising various types of mean square error criteria. A detailed study of computational complexity for the proposed SP‐based MIMO transceiver design algorithms is carried out. Simulation results verify the effectiveness of the new SP‐based designs for MIMO interference channels.

In this article, relay-assisted computation offloading (RACO) is investigated, where user A wishes to share the results of computational tasks with another user B with the assistance of a mobile-edge relay server (MERS). To enable this computation offloading, we propose a hybrid relaying (HR) approach employing a pair of orthogonal frequency bands, which are, respectively, used for the amplify-forward relaying of computational results and the decode-forward relaying of the unprocessed raw tasks. The motivation here is to adapt the allocation of computing and communication resources both to dynamic user requirements and to diverse computational tasks. Using this framework, we seek to minimize the weighted sum of the execution delays and the energy consumption in the RACO system by jointly optimizing the computation offloading ratio, the bandwidth allocation, the processor speeds, as well as the transmit power levels of both user A and the MERS, under some practical constraints. By adopting a series of transformations, we first recast this problem into a form amenable to optimization and then develop an efficient iterative algorithm for its solution based on the concave-convex procedure (CCCP). By virtue of the particular problem structure in our case, we propose furthermore a simplified algorithm based on the inexact block coordinate descent (IBCD) method, which leads us to much lower computational complexity. Finally, our numerical results demonstrate the advantages of the proposed algorithms over the state-of-the-art benchmark schemes.

Due to the severe pathloss experienced by electromagnetic waves in the millimeter wave (mmWave) band, a substantial challenge in their design is to have an adequate coverage area. With the objective of improving the coverage area and the sum rate attained, we conceive new full-duplex (FD) mmWave multiple-input multiple-output (MIMO) multiple-relay systems. Specifically, we propose a novel two-timescale analog-digital hybrid beamforming scheme for maximizing the sum rate, while reducing the system's complexity and the channel state information (CSI) signalling overhead, as well as mitigating both the effects of self-interference and that of outdated CSIs caused by the associated delays. In the proposed scheme, the long-timescale analog beamforming matrices are designed based on the available channel statistics and updated in a frame-based manner, where a frame contains a fixed number of time slots. By contrast, the short-timescale digital beamforming matrices are optimized more frequently - namely for each time slot - based on the low-dimensional effective CSI matrices available on a real-time basis. We develop both an efficient analog beamforming algorithm based on the cut-set bound as well as on stochastic successive convex approximation (SSCA) and an innovative digital beamforming algorithm that relies on the theory of penalty dual decomposition (PDD), where our design objective is to maximize the system's sum rate. Both the convergence properties and the computational complexity of the proposed algorithms are also examined. Our simulation results show that the proposed two-timescale hybrid beamforming design significantly outperforms the conventional beamformers both in terms of requiring a lower CSI-signalling overhead and a higher sum rate in the face of realistic outdated CSIs.

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.

In this work, we investigate a full-duplex (FD) millimeter wave (mmWave) multicell system, where the BS of each cell receives signals from uplink (UL) users and transmits signals to downlink (DL) users at the same time, over the same frequency band. We maximize the sum rate lower bound of the FD multicell system by jointly optimizing the digital and analog beamforming matrices at the base station (BS) and the transmit power levels of the UL users under total transmit power constraints and unit-modulus constraints (due to the analog beamforming matrices), in the presence of imperfect channel state information (CSI). The problem under study is very challenging due to the highly non-convexity of the objective function and constraints. We transform this problem into an equivalent but more tractable form and propose a novel iterative algorithm based on the penalty dual decomposition (PDD) to solve it. The proposed algorithm is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of the original problem. Moreover, we also extend our proposed algorithm to the structure of subarray. Simulation results validate the effectiveness of the proposed algorithm as compared with conventional nonrobust and half-duplex (HD) algorithms.

It is an essential goal for future wireless networks to provide better artificial intelligent services. In this paper, we investigate the joint communication and computation resource optimization in the mobile edge learning system to support augmented reality applications, where the convolutional neural networks (CNNs) are deployed at the edge server. For such a system, we first develop a delay model to characterize the relation between the computation latency and the input image size of general CNN models. Then, we formulate a mixed integer nonlinear optimization problem to maximize the system computation capacity under the constraints of learning accuracy, end-to-end latency, and energy consumption. To solve this problem, we first investigate maximizing the system learning accuracy under the communication and computation resource constraints. The optimal resource allocation policy can be achieved by a low-complexity search algorithm. We further prove that the original problem is NP-hard and propose an efficient heuristic algorithm with a newly-developed offloading priority function. An upper bound for the proposed algorithm is also derived. Finally, test results validate the applicability of the delay model and demonstrate the performance improvement of the proposed algorithm as compared with the existing algorithms.

Unmanned aerial vehicles (UAVs) have been applied in many different communication scenarios due to their mobility and manipuility. In this paper, we investigate a UAV-aided relay network, where a number of ground users in the urban area with many obstructions need to collect data from a base station (BS), and a UAV could fly around above the users and serve as a decode-and-forward (DF) mobile relay to improve the transmission coverage and performance. In this situation, channel can be represented by the segmented ray-tracing model. To ensure fairness, we aim to maximize the minimum throughput among all the users by jointly optimizing the three-dimensional (3D) UAV trajectory, user scheduling, and bandwidth allocation. To tackle the non-convex objective function and coupling constraints, we first construct surrogate functions, and then approximate the problem into a convex one and develop a constrained successive convex approximation (CSCA) algorithm. In particular, through insightful auxiliary variables and linearly coupled equality (LCE) constraints, we propose a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) to solve the approximated convex problem in the iteration of the proposed CSCA algorithm. Furthermore, we prove the convergence of the proposed algorithm and analyze its complexity. The proposed algorithm can be easily extended to the multi-UAV scenario. Simulation results show that the proposed design significantly outperforms the existing schemes.

Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network's edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.

This work studies the joint problem of content placement, remote radio head (RRH) clustering and beamformer design, in a cache-enabled cloud-radio access network (C-RAN). In the considered system, downlink users are cooperatively served by multiple RRHs, in turn connected to a centralized baseband unit (BBU) pool via fronthaul links. Each RRH is equipped with a local cache from which it can directly acquire the requested user contents, without utilizing the fronthaul links. We aim to jointly optimize the aforementioned three aspects, in order to strike a balance between fronthaul traffic reduction and transmission power minimization. To this end, we propose to employ the ratio between these two important system utilities as the objective function, referred to as caching efficiency. Two joint design algorithms are presented to address the resulting nonconvex optimization problem, which features coupling constraints and mixed-integer variables, namely: the penalty concave-convex procedure (P-CCCP) and penalty dual decomposition (PDD) based algorithms. Furthermore, since content placement is usually updated over a larger timescale, we propose a two-timescale joint design algorithm, where the P-CCCP and PDD-based algorithms can be employed for efficient initialization as well as for establishing performance limits. Simulation results validate the efficiency of the proposed algorithms.

In this paper, we study a UAV-aided relay network, where a number of ground users need to collect data from a base station (BS), and a UAV could fly around above the users and serve as a decode-and-forward (DF) mobile relay to improve the performance. Firstly, we develop a novel UAV-relay model, which maximizes the minimum throughput among users by optimizing the UAV trajectory, the user scheduling and bandwidth allocation. Moreover, we adopt the segmented ray- tracing channel model to characterize the practical urban channel. Then, by constructing surrogate functions of the nonconvex constraints, we approximate the problem into a convex one and develop a block successive convex approximation (BSCA) algorithm to solve it. In particular, through insightful auxiliary variables and linearly coupled equality constraints, we propose a low-complexity algorithm to solve the key subproblem in the iteration of the proposed BSCA algorithm. Finally, simulation results show that the proposed design significantly outperforms the existing algorithms.

The mobile edge computing (MEC) and device-to-device (D2D) communications take advantage of the proximity for supporting high-speed mobile computing and high-rate data communications, respectively. In this paper, we integrate both techniques to further improve the computation capacity of the cellular networks by proposing the D2D-MEC technique. We aim to maximize the number of supported devices and formulate a mixed integer non-linear problem. To solve it, we decouple it into two subproblems and prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. The first one minimizes the required edge computation resource for a given D2D pair while the second one maximizes the number of supported devices via optimal D2D pairing. Then, by solving two subproblems, the optimal algorithm is developed and some insightful results are also highlighted. Finally, numerical results show that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

This work studies the joint problem of optimal content placement, RRH clustering and beamformer design, in a cache-enabled cloud-radio access network (C-RAN). In the considered system, multiple remote radio heads (RRHs) connected to a centralized baseband unit (BBU) pool via fronthaul links, cooperatively serve the downlink users by grouping them into potentially overlapping clusters. Each RRH is equipped with a local cache from which it can directly acquire the requested user contents, without the need to occupy the fronthaul links. We aim to jointly optimize the caching placement, user association and downlink beamforming vector at each RRH, in order to strike a balance between fronthaul traffic reduction and transmission power minimization. To this end, we propose to employ the ratio between these two important system utilities as the objective function, referred to as caching efficiency. A penalty dual decomposition (PDD) based algorithm is presented to address the resulting nonconvex optimization problem, which features coupling constraints and mixed-integer variables. Simulation results validate the efficiency of the proposed algorithm.

In non-orthogonal multiple access (NOMA) systems, the outage is considered to happen when a user cannot correctly decode the messages for the users with higher decoding order and hence the successive interference cancellation (SIC) is failed in traditional definition. However, in this case, the user may still correctly decode its message by treating the uncancelled signal as interference and the outage is avoided. By considering this behavior, a more accurate alternative outage probability can be defined. In this paper, we investigate user scheduling and power allocation for a downlink NOMA system with imperfect SIC by employing the alternative outage probability as then performance metric. The coupling of user scheduling and power allocation makes the problem complicated. Therefore, we propose a two-phase algorithm, in which the user scheduling is first optimized through a matching theory based algorithm, and then power allocation is performed with the aid of the concave-convex procedure (CCCP) method. Simulation results show that the proposed low- complexity algorithm can achieve near-optimal performance and the algorithm based on the alternative outage probability outperforms the traditional one when the decoding is significantly affected by imperfect SIC.

In a mobile edge computing (MEC) system with a large number of low power mobile terminals, proper computation offloading and resource allocation is crucial to achieving desirable system performance. In this paper, we consider the joint computation offloading and resource allocation problem for an uplink MEC system under the min-max fairness criterion. The proposed optimization problem is difficult to solve due mainly to the nonconvex nondifferentialbe objective and the nonlinear coupling of design variables in the constraints. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then develop a novel algorithm based on the concave-convex procedure (CCCP) technique to address the problem. Furthermore, by exploiting the problem structure, an efficient algorithm based on inexact block coordinate descent (IBCD) method is proposed to reduce the computational complexity. Numerical results validate the efficiency of the proposed algorithms.

Cooperative localization can compensate weaknesses of traditional localization techniques which do not operate well in harsh environment. However, cooperative localization signals increase the interference power for communication. In this work, we seek to joint localization and transmission power in order to maximize the transmission rate of secondary user under the power budget and primary users' outage constraints. At the same time, we consider the trade-off between localization error and localization interference when formulating the above problem in cooperative localization. The proposed optimization problem is nonconvex and highly coupled, which is challenging to solve. To simplify the problem, we introduce some auxiliary variables to the original optimal problem and apply a algorithm based on concave-convex procedure (CCCP). The simulation results demonstrate the advantages of location-aware network based on cooperative localization.

In this paper, we investigate the joint design of hybrid beamforming and demanding computation tasks offloading in mmWave-based mobile edge computing (MEC) systems, in order to minimize the maximum latency. The resulting optimization problem is challenging, mainly due to the highly nonlinear objective function and the unit modulus constraints on the analog beamformers. By seeking the special structure of the problem, we divide it into two separate problems. An iterative weighted mean-square error minimization (WMMSE) approach is adopted to address the first optimization problem, and the second problem is solved in closed-form. We also investigate a more practical scheme when only finite resolution phase shifters are implemented. Simulation results are provided to confirm that the proposed strategy achieves significant better performance than recent reported beamforming algorithm with fixed offloading ratio, and it is effective when low-resolution phase shifters are used.

Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified l0-norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.

In this paper, we investigate joint trajectory design and resource allocation algorithms to maximize the minimum average rate among ground users for unmanned aerial vehicle (UAV) communication systems, where both the orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) modes are considered. We first formulate the problems for UAV communications with the OMA and NOMA modes, respectively, which contain binary variables and highly coupled nonconvex objective functions and constraints. In order to handle the challenging problems, we transform the original problems into more tractable forms and then develop novel algorithms based on penalty dual-decomposition technique to solve them. Simulation results show that the proposed algorithms outperform the benchmarks.

The joint design of hybrid beamforming matrices is conceived for multiuser mm-wave full-duplex (FD) multiple-input multiple-output (MIMO) relay-aided systems in the presence of realistic channel state information (CSI) errors. Specifically, considering a probabilistic CSI error model, we maximize the system's worst-case sum rate by jointly optimizing the base station's (BS's) analog and digital beamforming matrices, plus the analog receive and transmit beamforming matrices of the relay station (RS) as well as its digital amplify-and-forward beamforming matrix under practical constraints. Explicitly, the transmit power constraints of the BS and RS, the residual self-interference power constraint of the RS, the per-user quality of service constraints, and the unit-modulus constraints on the analog beamforming matrix elements are all taken into account. Since the resultant optimization problem is very challenging due to its highly nonlinear objective function and nonconvex coupling constraints, we first transform it into a more tractable form. We then develop a novel joint optimization algorithm based on the penalty dual decomposition (PDD) technique to solve the resultant problem. The proposed PDD-based algorithm performs double-loop iterations: the inner loop updates the optimization variables in a block coordinate descent fashion, while the outer loop adjusts the Lagrange multipliers and penalty parameter, hence ensuring convergence to the set of stationary solutions of the original problem. Our simulations show that the mm-wave FD hybrid MIMO relay systems relying on our new algorithm significantly outperform both their non-robust FD and conventional half-duplex counterparts.

The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

In this article, we investigate the application of NOMA in mobile unmanned aerial vehicle (UAV) communication networks and propose the algorithm to jointly optimize the UAV trajectory and power allocation. Specifically, we formulate the optimization problem to maximize the minimum average rate among ground users for NOMA based UAV communication systems, which contains complicated and discrete binary constraints, as well as the highly coupled nonconvex objective function. Then, we transform the challenging original problem into a more tractable form with some equality constraints. Finally, we develop a double-loop algorithm to solve it with the aid of penalty dual-decomposition (PDD) technique. From the simulation results, the proposed algorithm outperforms the benchmarks.

In this article, we address joint optimization of unmanned aerial vehicle (UAV) trajectories and user communication scheduling for a dual-UAV enabled secure communication system, where one UAV moves to serve multiple users on the ground in a time division multiple access (TDMA) mode while the other UAV in the area flies to jam the colluding eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum average secrecy rate among the users within each period by jointly optimizing UAV trajectories and user scheduling variables under the maximum UAV speed constraints, the UAV return constraints, and the discrete binary constraints on user scheduling variables. The resulting optimization problem is very challenging due to its highly nonconvex objective function and constraints. We then develop a novel algorithm based on the penalty concave-convex procedure (CCCP) technique to solve it. Based on our simulation results, the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

This paper investigates a multiuser mobile edge computing system under interference channels, where mobile users can offload their latency-sensitive (computation-intensive) tasks to the mobile edge server via a base station (BS). In this work, we seek to jointly optimize the user selection indicators for offloading and the computation resources, as well as the transmit power level of the offloading users in order to minimize the system energy consumption under latency-sensitive, computation and transmit power budget, transmission quality, and user selection constraints. The proposed optimization problem is nonconvex and highly coupled, which is difficult to solve. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then propose a concave-convex procedure (CCCP) based algorithm to obtain the resulting problem. Furthermore, a simplified algorithm is proposed to reduce the computational complexity. Simulation results are proposed to verify the proposed algorithms.

This paper considers a computational results sharing (CRS) system where user A wants to share its computational results with user B with the aid of a relay equipped with an mobile edge computing (MEC) server. The performance of the CRS systems can be greatly impacted by the relay forward protocol and resources allocation. To realize cooperative computation and communication in a relay aided mobile edge computing system, we develop a hybrid relay forward protocol and properly allocate the system computational and communication resources, where we seek to balance the execution delay and network energy consumption. The problem is formulated as a nondifferentialbe optimization problem which is nonconvex with highly coupled constraints. By exploiting the problem structure, we propose a lightweight algorithm based on inexact block coordinate descent method. Our results show that the proposed algorithm exhibits much faster convergence as compared with the popular concave-convex procedure based algorithm, while achieving good performance.

In this paper, we investigate a novel unmanned aerial vehicle (UAV)-enabled secure communication system. Two UAVs are applied in this system where one UAV moves around to communicate with multiple users on the ground using orthogonal time-division multiple access while the other UAV in the area jams the eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum worst-case secrecy rate among the users within each period by jointly adjusting UAV trajectories and user scheduling under the maximum UAV speed constraints, the UAV return constraints, the UAV collision avoidance constraints, and the discrete binary constraints on user scheduling variables. Since the resulting optimization problem is very difficult to solve due to its highly nonlinear objective function and nonconvex constraints, we first equivalently transform it into a more tractable problem. In particular, the binary constraints are equivalently converted to a number of equality constraints. Then, we develop a novel joint optimization algorithm to handle the converted problem. In order to further improve the secrecy rate performance, we also extend the developed algorithm to the case with multiple jamming UAVs. The simulation results show that the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

This paper addresses the design of joint beamforming and jamming for a millimeter wave (mmWave) information surveillance system where a suspicious transmitter in the network sends messages to a suspicious receiver under the supervision of a surveillant controller (SC), which not only carries out the duty of a base station or other access point, but also legitimately monitor the suspicious link. Specifically, we seek to maximize the effective monitoring rate for information surveillance by jointly optimizing the analog transmit and receive beamforming vectors of the suspicious link, the analog jamming and monitoring beamforming vectors at the SC and the jamming signal's power level under transmit power, successful monitoring, and self-interference power constraints at the SC, along with unit modulus constraint on the elements of the radio frequency analog beamforming vectors. The resulting optimization problem is quite challenging due to the tight coupling of the design variables in the objective function and constraints. To solve it, we develop a novel algorithm based on the penalty dual decomposition (PDD) technique, where the exacting constraints are penalized and dualized into the objective function as augmented Lagrangian components. The proposed PDD-based algorithm performs double-loop iterations, i.e., the inner loop resorts to the concave-convex procedure to update the optimization variables; while the outer loop adjusts the Lagrange multipliers and penalty parameter of the augmented Lagrangian cost function. We show that the proposed PDD-based joint beamforming and jamming algorithm converges to a stationary solution of the original problem. Based on our simulation results, the proposed algorithm achieves significantly better performance than the conventional beamforming and jamming algorithms.

This paper considers the uplink of multiuser multiple-input multiple-output systems, where several mobile stations (MSs) cooperatively transmit hybrid messages, including common messages and private messages, to a single base station (BS). We aim to jointly design transmit precoding at the MSs' side and antenna selection at the BS side to maximize the achievable system throughput while reducing implementation complexity. The problem at hand is nonconvex and difficult to solve due to the antenna selection constraint. By exploiting the problem structure and linear relaxation, we propose using the Frank-Wolfe method and the well-known weighted mean-square error minimization approach to tackle the problem, leading to an efficient iterative algorithm. Moreover, due to the large number of antennas, the sparsity of antenna selection is also taken into account by introducing an l0-norm penalty function into the objective function. To tackle this nonconvex and discontinuous problem, we resort to quadratic approximation with smooth optimization and extend our proposed algorithm to the sparse optimization problem. The convergence of the proposed algorithms is analyzed and its effectiveness is verified by numerical examples in terms of the achieved system throughput.

By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multi-user time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency.

In this paper, we consider latency-minimization resource allocation for a multi-user mobile edge computation offloading (MECO) system. First, we develop a novel partial computation offloading model and then formulate the weighted-sum latency-minimization problem by optimally allocating the communication and computation resources. After that, the closed-form expression for the optimal data segmentation strategy is derived. Based on this result, we transform the original problem into a piecewise convex optimization problem and propose a sub-gradient algorithm to find the optimal resource allocation solution. Moreover, we analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution is devised. Finally, numerical results show that the partial computation offloading model can achieve a better performance than other two baseline schemes.

This paper investigates multiuser full-duplex (FD) multiple-input multiple-output (MIMO) relay systems. We study joint design of the base station (BS) beamforming matrix and the relay station (RS) amplify-and-forward (AF) transformation matrix to maximize the system sum rate with only imperfect channel state information (CSI) at the RS. To deal with highly coupled design variables in the objective function and constraints in the optimization problem, we develop a novel algorithm based on the penalty dual decomposition (PDD) algorithmic framework. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

This paper studies the multiple-input multiple- output (MIMO) millimeter wave (mmWave) systems with non-linear power consumption model for 5G network. A new non-linear power consumption model is investigated, consisting of the power cost generated by the circuit and the non-linear power amplifiers. In this work, we aim to optimize the hybrid transceiver to maximize the system capacity subject to the resultant non-linear power constraint. In order to address this problem, we first transform the original optimization problem to a more tractable problem based on the weighted minimum mean squared error (WMMSE) approach. Then, we propose a novel transceiver design algorithm based on the penalty dual decomposition (PDD) optimization framework to address this problem. Moreover, a simplified algorithm is also proposed by using linear approximation. The effectiveness of the proposed algorithm is verified by simulation results.

In this paper, we study the problem of distributed blind equalization in single-input multi-output (SIMO) systems, wherein the channels of networked systems share some similarities. This corresponds to a multi-task optimization problem. To tackle this problem, an adaptive distributed generalized Sato algorithm (d-GSA) using the diffusion cooperation rule is proposed. In the proposed d-GSA, only the scalar of equalizer output is combined and transmitted among neighbors, which significantly reduces the cost of computation and communication. The performance of d-GSA is analyzed theoretically and verified by numerical simulations. Results show that the d-GSA outperforms the corresponding non-cooperative GSA.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output (MU-MIMO) millimeter wave (mmWave) systems, where a number of mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation (5G) wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers (LNA). We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog) and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems which are solved via an alternating optimization (AO) method. Specifically, the antenna selection matrix is optimized via the concave-convex procedure (CCCP); the weighted mean-square error minimization (WMMSE) approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization (MO). The convergence of the proposed algorithm is analysed and its effectiveness is verified by simulation.

Wireless transmission with lens antenna arrays is becoming more and more attractive for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with limited radio frequency (RF) chains due to their energy-focusing capability. In this paper, we consider the joint design of beam selection and precoding to maximize the sum rate of a downlink single-sided lens MU-MIMO mmWave system under transmit power constraints. We first formulate the optimization problem into a tractable form using the popular weighted minimum mean squared error (WMMSE) approach. To solve this problem, we then propose an efficient joint beam selection and precoding algorithm based on the innovative penalty dual decomposition (PDD) method. Simulation results demonstrate that our proposed algorithm can achieve near-optimal performance when compared to the fully digital precoding scheme and thus outperform the competing methods.

This work studies the joint transceiver design for a full-duplex (FD) cloud radio access network (C- RAN) with simultaneous wireless information and power transfer (SWIPT). In the considered network, a number of FD remote radio heads (RRHs) receive information from uplink users (UUs), while transmitting both information and energy to a set of half-duplex (HD) downlink users (DUs) with power splitting receivers. Based on the particular problem structure, a block coordinate descent (BCD) method is proposed to minimize the total transmission power subject to both uplink-downlink quality of service (QoS) constraints and energy harvesting (EH) constraints. Although the problem has complicated constraints coupling a set of transceivers, uplink transmit power levels, and receive power splitting ratios, we prove that the proposed BCD algorithm converges to a Karush-Kuhn- Tucker (KKT) solution. Simulation results validate the effectiveness of the proposed algorithm as compared with the traditional HD scheme.

This study investigates the problem of physical layer security for amplify‐and‐forward (AF) multiple‐input multiple‐output (MIMO) relay systems operating in the presence of a passive eavesdropper. Specifically, the authors consider the robust design of an artificial noise (AN)‐assisted non‐linear transceiver employing Tomlinson–Harashima precoding (THP), with imperfect knowledge of the legitimate channel states. The design problem can be reformulated as a two‐level optimisation, where the outer problem aims to optimise the source precoder as a function of the relay precoder, while the inner problem at the relay aims to jointly optimise the relay precoder as well as the power allocation between the AN and the information‐bearing signals. To solve the inner problem, the authors adopt a bisection method which attempts to maximise the AN power level, to confuse the eavesdropper, while satisfying the mean‐squared‐error requirement for the intended user. Some relaxation for the objective function is applied to transform the problem into a standard convex optimisation one. Regarding the outer problem, closed‐form solutions for the precoders can be derived by an iterative method based on the Karush–Kuhn–Tucker conditions. Simulation results illustrate the superior secrecy performance provided by the proposed non‐linear transceiver design with AN and THP.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output millimeter wave systems, where several mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers. We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog), and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate under a set of constraints. The corresponding optimization problem is nonconvex and difficult to solve, mainly due to the receive antenna selection and constant modulus constraints on the analog receiving matrix. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems, which are solved via an alternating optimization method. The latter iteratively updates the antenna selection matrix, the transmit beamforming vectors, and the hybrid receiving matrices by sequentially addressing each subproblem while keeping the other variables fixed. Specifically, the antenna selection matrix is optimized via the concave-convex procedure; the weighted mean-square error minimization approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization. The convergence of the proposed algorithm is analyzed and its effectiveness is verified by simulation.

This paper addresses joint transceiver design for secure downlink communications over a multiple-input multiple-output relay system in the presence of multiple legitimate users and malicious eavesdroppers. Specifically, we jointly optimize the base station (BS) beamforming matrix, the relay station (RS) amplify-and-forward transformation matrix, and the covariance matrix of artificial noise, so as to maximize the system worst-case secrecy rate in the presence of the colluding eavesdroppers under power constraints at the BS and the RS, as well as quality of service constraints for the legitimate users. This problem is very challenging due to the highly coupled design variables in the objective function and constraints. By adopting a series of transformation, we first derive an equivalent problem that is more tractable than the original one. Then, we propose and fully develop a novel algorithm based on the penalty concave-convex procedure (penalty-CCCP) to solve the equivalent problem, where the difficult coupled constraint is penalized into the objective and the resulting nonconvex problem is solved at each iteration by resorting to the CCCP method. It is shown that the proposed joint transceiver design algorithm converges to a stationary solution of the original problem. Finally, our simulation results reveal that the proposed algorithm achieves better performance than other recently proposed transceiver designs.

In this paper, we propose joint transceiver design algorithms for the full-duplex K -pair multiple-input multiple-output interference channel with simultaneous wireless information and power transfer. To mitigate and exploit the complex interference, we consider two important utility optimization problems, i.e., the sum power minimization problem and the sum-rate maximization problem. In the first problem, our aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization (AO) and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, where the concave-convex procedure (CCCP), the minimum mean-square error (MMSE) criterion, and the semidefinite relaxation technique are, respectively, employed to compute the vectors of power splitting ratios, the receiving matrices, and the transmitting beamforming vectors. Two simplified algorithms based on fixed beamformers, namely, the maximum ratio transmission and the maximum signal-to-interference-leakage beamformers are also proposed. In the second problem, our aim is to maximize the sum-rate under additional power and EH constraints. Due to the highly non-convex nature of this problem, we first reformulate it into an equivalent-weighted MMSE problem by introducing suitable weight factors, such that the global optima of the two problems are identical. Then, by utilizing the concept of AO and CCCP, we show that the equivalent problem can be efficiently solved. Again, with the aid of the fixed beamformers, two simplified algorithms are provided to reduce the computational complexity. Simulation results are presented to validate the effectiveness of the proposed algorithms.

This paper investigates a cellular network powered by a micro-grid (MG) in the context of green communications, which integrates the conventional generators, energy storage devices, and renewable energy generators, so as to supply electricity to base stations (BSs). Under this model, we study the efficiency aspect of the MG-powered cellular network from the economical perspective. Specifically, the concept of cost efficiency (CE) is employed to measure the sum rate delivered per dollar. Then, our goal is to maximize this CE subject to a series of constraints, including multi-variable coupling and time coupling constraints. Particularly, we assume the zero-forcing beamforming scheme employed by the BSs. To address this established fractional CE optimization problem, we first apply the Dinkelbach method, and then propose a low-complexity algorithm based on the alternating direction method of multipliers approach to jointly schedule power generation in the MG and optimize transmit power for BSs. We introduce a number of auxiliary variables to design a special variable splitting scheme so that the coupling inequality constraints can be separable among two variable sets. Consequently, the proposed algorithm only incorporates simple updates in each step and thus can be implemented in a parallel and completely distributed fashion. Simulation results demonstrate the convergence and energy scheduling performance of the proposed algorithm.

This paper investigates the design of robust nonlinear transceivers conceived for multiple-input multiple-output full-duplex wireless-powered relay networks in the face of realistic imperfect channel state information (CSI). A novel self-energy recycling aided relaying protocol is employed, whereby the relay node benefits from energy harvesting (EH) gleaned from the self-interfering link in addition to its primary energy. The proposed nonlinear transceiver relies on a Tomlinson-Harashima (TH) precoder along with an amplify-and-forward (AF) relaying matrix and a linear receiver, where the TH precoder is composed of a feedback matrix and a source precoding matrix. Two different criteria are considered for the robust design of the nonlinear transceiver in the presence of channel estimation errors modeled by the Gaussian distribution. The first one aims to minimize the mean-squared-error (MSE) at the destination subject to a transmit power constraint at the source and an EH constraint at the relay. The resultant optimization problem is converted to four subproblems and solved via an alternating optimization (AO) algorithm that iteratively updates the transceiver coefficients by sequentially addressing each subproblem, while keeping the other matrix variables fixed. The second design criterion aims to minimize the transmit power at the source under both MSE and EH constraints. Similarly, an AO-based iterative algorithm is proposed for solving this problem. Our simulation results show that the robust design advocated is capable of alleviating the effects of CSI errors, hence improving the robustness of the system over that of the corresponding linear designs.

This paper studies joint transceiver design for a full-duplex (FD) cloud radio access network with simultaneous wireless information and power transfer. In the considered network, a number of FD remote radio heads receive information from uplink users, while transmitting both information and energy to a set of half-duplex (HD) downlink users with power splitting receivers. We aim to minimize the total power consumption with both uplink-downlink quality of service constraints and energy harvesting constraints. The resulting problem is challenging, because various design parameters, such as the transceiver beamformers, the uplink transmit power, and the receive power splitting ratios, are tightly coupled in the constraints. Four different solution approaches are proposed for the joint transceiver design problem, each one leading to a different numerical algorithm. In particular, a block coordinate descent method is proposed, and by exploiting the problem structure, we prove that the algorithm converges to a Karush-Kuhn-Tucker solution, despite the coupling of various design variables in the constraints. Simulation results validate the effectiveness of the proposed algorithms as compared with the traditional HD scheme.

This paper investigates a multi-cell system powered by a micro-grid (MG), where the conventional generators (CGs), energy storage devices (ESDs) and renewable energy generators (REGs) are scheduled to supply electricity to base stations (BSs) at different prices respectively. Under this energy schedule model, we study the efficiency aspect of the MG-powered multi-cell systems from the economical perspective, and propose a new concept of efficiency, which is referred to as cost efficiency (CE). Specifically, we consider the ratio of the sum rate for all BSs to the total energy cost that the system spends in providing electricity for BSs. Assuming that the zero-forcing (ZF) beamforming scheme is employed by the BSs, our goal is to maximize the CE by jointly scheduling energy in the MG and allocating the transmit power at the BSs. We apply the Lagrange duality decomposition technique and Dinkelbach method to address the established CE optimization problem. Simulations are provided to validate the effectiveness of the proposed algorithm.

This paper addresses the transceiver design problem for secure downlink communications over a multiple-input multiple-output (MIMO) relay system in the presence of multiple eavesdroppers. A new algorithm based on alternating optimization (AO) is first proposed to maximize the signal-to-noise ratio (SNR) of a legitimate receiver under power constraints at the base station (BS) and the relay station (RS) and a set of secrecy constraints, by using the semidefinite relaxation (SDR) technique. To reduce complexity, a simplified design algorithm based on switched relaying (SR) is also proposed, in which both the BS and the RS are equipped with a codebook of permutation matrices. Based on this codebook, we construct a number of latent transceivers, each consisting of a BS beamforming vector and an optimally scaled RS permutation matrix. We use the bisection search and second-order cone programming (SOCP) techniques to design each latent transceiver and choose the optimal one with the largest SNR. We also develop an efficient approach to construct the codebook of permutation matrices. Our results show that the SR based algorithm significantly reduces the computational complexity while maintaining a similar performance to the AO based algorithm.

This paper studies joint source transmit beamforming and relay amplification matrix design to achieve rate maximization for full-duplex (FD) MIMO amplify-and-forward (AF) relay systems with consideration of relay processing delay (RPD). The problem is difficult to solve due mainly to the self-interference constraint induced by the RPD. In this paper, we first propose a penalty-based algorithmic framework, called P-BSUM, for a class of constrained optimization problems with difficult equality constraints in addition to some convex constraints. We then apply the P-BSUM algorithm to the rate maximization problem and obtain a simple iterative algorithm. Finally, numerical results illustrate the efficiency of the proposed algorithm.

In this paper, we propose a joint transceiver design algorithm for the full-duplex (FD) K-pair multiple- input multiple-output (MIMO) interference channel with simultaneous wireless information and power transfer (SWIPT). The aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, aimed at successively optimizing: 1) the power splitting (PS) vectors of the EH nodes; 2) the receive beamforming vectors; 3) the transmit beamforming vectors.The first step is carried out based on concave-convex procedure (CCCP), the second step is based on the minimum mean square error (MMSE) criterion and the third step resorts to using semidefinite relaxation (SDR). Simulation results are presented to validate the effectiveness of the proposed algorithm.

In this paper, a low-complexity turbo detection scheme based on a weighted factor graph (FG) serial-schedule (SS) belief propagation (BP) equalization method is proposed for Faster-than-Nyquist (FTN) signaling. The iterative equalization method is applied to mitigate the severe intersymbol interference (ISI) introduced by FTN signaling. In order to reduce the complexity of the equalization method, Gaussian approximation (GA) is used to calculate the log-likelihood ratio (LLR). Thus, the computational complexity is merely linear with the number of ISI taps. Furthermore, LDPC, as an efficient coding technique, results in performance that approaches the Shannon limit in this turbo detection scheme. The simulation results show that the proposed weighted FG-SS-BP-based turbo detection method performs close to the optimal detector under ISI-free conditions with very low complexity.

This paper investigates the design of vector perturbation (VP) precoding using lattice reduction (LR) based on a multi-branch (MB) strategy for multi- user multiple-input multiple-output (MU-MIMO) systems. The MB strategy constructs a group of branches for transmitting data streams according to a pre-designed ordering scheme. For each branch, an LR-aided minimum mean square error (MMSE) VP precoder is proposed and three methods are devised for the perturbation vector design. We also develop an effective scheme to design the transmit ordering patterns with appropriate structures and a suitable selection mechanism to choose the best one. Simulation results show that the proposed MB-LR-MMSE-VP algorithm achieves a better bit error rate (BER) performance than existing VP precoding schemes.

In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for direction-of-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction consists of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recursive least squares (RLS), denoted as ALRD-RLS and modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the auxiliary parameter vector. Simulations for large sensor arrays with both uncorrelated and correlated sources are presented, showing that the proposed algorithms are superior to existing techniques.

In this paper, we investigate a multiuser multiple-input single-output relay system with simultaneous wireless information and power transfer, where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. Assuming that both base station (BS) and relay station (RS) are equipped with multiple antennas, we study the joint transceiver design problem for the BS beamforming vectors, the RS amplify-and-forward transformation matrix, and the power splitting (PS) ratios at the single-antenna receivers. The aim is to minimize the total transmission power of the BS and the RS under both signal-to-interference-plus-noise ratio and EH constraints. First, an iterative algorithm based on alternating optimization (AO) and with guaranteed convergence is proposed to successively optimize the transceiver coefficients. This AO-based approach is then extended into a robust transceiver design against norm bounded errors in channel state information (CSI), by using semidefinite relaxation and the S-procedure. Second, a novel design scheme based on switched relaying (SR) is proposed that can significantly reduce the computational complexity and overhead of the AO-based designs while maintaining a similar performance. In the proposed SR scheme, the RS is equipped with a codebook of permutation matrices. For each permutation matrix, a latent transceiver is designed, which consists of BS beamforming vectors, optimally scaled RS permutation matrix, and receiver PS ratios. For the given CSI, the optimal latent transceiver with the lowest total power consumption is selected for transmission. We propose concave-convex procedure-based and subgradient-type iterative algorithms, respectively, to design the latent transceivers under perfect and imperfect CSI. Simulation results are presented to validate the effectiveness of all the proposed algorithms.

In this paper, we consider the power splitting technique for multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH) respectively. Specifically, assuming norm-bounded errors (NBE) in the channel state information (CSI), we study the robust joint beamforming and power splitting (JBPS) design problem, where the total transmission power is minimized subject to both signal-to-interference- plus-noise ratio (SINR) and EH constraints. We first propose an efficient approximation method based on semidefinite relaxation (SDR) for solving the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. Then, a low complexity algorithm is proposed using EH relaxation and cutting-set philosophy, which partitions the original problem into an alternating sequence of optimization and worst-case analysis subproblems with guaranteed convergence. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.

In this work, the authors propose two low‐complexity variable forgetting factor (VFF) mechanisms for recursive least squares‐based adaptive beamforming algorithms. The proposed algorithms are designed according to the linearly constrained minimum variance (LCMV) criterion and operate in the generalised sidelobe canceller structure. To obtain a better performance of convergence and tracking, the proposed VFF mechanisms adjust the forgetting factor by employing updated components related to the time‐averaged LCMV cost function. They carry out the analyses of the proposed algorithms in terms of the computational complexity and the convergence properties and derive an analytical expression of the steady‐state mean‐square‐error. Simulation results in non‐stationary environments are presented, showing that the adaptive beamforming algorithms with the proposed VFF mechanisms outperform the existing methods at a significantly reduced complexity.

In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and M-ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.

In this work, we propose an adaptive set-membership (SM) reduced-rank filtering algorithm using the constrained constant modulus (CCM) criterion for beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM technique for adaptive implementation. The filter weights are updated only if the bounded constraint cannot be satisfied. In addition, we also propose a scheme of time-varying bound and incorporate parameter dependence to characterize the environment for improving the tracking performance of the proposed algorithm. Simulation results show that the proposed adaptive SM reduced-rank beamforming algorithm with dynamic bounds achieves superior performance to previously reported methods at a reduced update rate.

In this study, we propose a robust transceiver scheme with switched preprocessing (SP) for K-user multiple-input multiple-output (MIMO) interference channels. The channel state information (CSI) available is assumed to be imperfect under norm-bounded errors (NBE). Each transmitter is provided with a codebook of permutation matrices, so that each arrangement of permutation matrices among the K transmitters will generate a group of K parallel transceivers. The optimum transceiver group within the class of all possible such groups is chosen by a suitable selection mechanism for data transmission. To design each transceiver group, we adopt a worst-case design approach to minimize the maximum per user MSE. We show that the proposed transceiver design problem can be partitioned into an alternating sequence of optimization and worst-case analysis subproblems, which involves solving Second-Order Cone Programming (SOCP) problems. Simulation results show that the performance of the proposed SP-based transceiver is significantly better than existing methods in the presence of imperfect CSI.1.

In this work, we propose a transceiver strategy with switched preprocessing (SP) for interference suppression in K-pair multiple-input multiple-output (MIMO) interference channels. Each transmitter is equipped with a codebook of permutation matrices. For the given MIMO interference channel, all the combinations of permutation matrices among the transmitters can create a number of parallel transceivers. Based on the given channel state information (CSI) and a block of transmit symbols, the optimum transceiver branch is chosen by a suitable selection criterion for transmission. For each branch, we introduce a robust transceiver design algorithm based on minimizing the mean square error (MSE) criterion. The selection criterion is designed to minimize the Euclidean distance between the true transmit symbol vector and the pre-estimated noiseless received vector. Simulation results show that the performance of the proposed technique is significantly better than prior art in the case of imperfect CSI.

Everyday a large number of records of surfing internet are generated. In various situations when the authors are analysing internet data they do not know the cluster structure of the author's database of traffic features, such as when the border of cluster members is vague, and the clusters’ partitions have different shapes, how to establish an algorithm to solve the clustering problem? Adaptive clustering algorithms can meet this challenge. Moreover, how to determinate the number of clusters when not only fuzzy cluster but also hard cluster are used? To address those problems, a new cluster validity index is proposed in this study. The proposed index focuses on the information of the geometrical structure of dataset by analysing the neighbourhood of data objects, which makes the index independent of the traditional fuzzy membership matrix. The new index consists of two parts, namely the ‘compactness’ and ‘separation measure’. The compactness indicates the degree of the similarity among the data objects in the same cluster. The separation measure indicates the degree of dissimilarity among the data objects in different clusters. The performance of their proposed index is excellent underpinned by the outcomes from the experiments based on both artificial datasets and real world datasets.

In this study, a two-hop wireless sensor network with multiple relay nodes is considered where the amplify-and-forward (AF) scheme is employed. Two algorithms are presented to jointly consider interference suppression and power allocation (PA) based on the minimization of the symbol error rate (SER) criterion. A stochastic gradient (SG) algorithm is developed on the basis of the minimum-SER (MSER) criterion to jointly update the parameter vectors that allocate the power levels among the relay sensors subject to a total power constraint and the linear receiver. In addition, a conjugate gradient (CG) algorithm is developed on the basis of the SER criterion. A centralized algorithm is designed at the fusion center. Destination nodes transmit the quantized information of the PA vector to the relay nodes through a limited-feedback channel. The complexity and convergence analysis of the proposed algorithms are carried out. Simulation results show that the proposed two adaptive algorithms significantly outperform the other previously reported algorithms.

This paper proposes the design of robust transceivers with Tomlinson-Harashima precoding (THP) for multiple-input-multiple-output relay systems with amplify-and-forward protocols based on a multibranch (MB) strategy. The MB strategy employs successive interference cancellation on several parallel branches, which are equipped with different ordering patterns so that each branch produces transmit signals by exploiting a certain ordering pattern. For each parallel branch, the proposed robust nonlinear transceiver design consists of THP at the source along with a linear precoder at the relay and a linear minimum-mean-square-error receiver at the destination. By taking the channel uncertainties into account, the source and relay precoders are jointly optimized to minimize the mean square error. We then employ a diagonalization method along with some attributes of matrix-monotone functions to convert the optimization problem with matrix variables into an optimization problem with scalar variables. We resort to an iterative method to obtain the solution for the relay and the source precoders via Karush-Kuhn-Tucker conditions. An appropriate selection rule is developed to choose the nonlinear transceiver corresponding to the best branch for data transmission. Simulation results demonstrate that the proposed MB-THP scheme is capable of alleviating the effects of channel state information errors and improving the robustness of the system.

In this work, we propose a novel adaptive reduced-rank strategy based on joint interpolation, decimation and filtering (JIDF) for large multiuser multiple-input multiple-output (MIMO) systems. In this scheme, a reduced-rank framework is proposed for linear receive processing and multiuser interference suppression according to the minimization of the bit error rate (BER) cost function. We present a structure with multiple processing branches that performs dimensionality reduction, where each branch contains a group of jointly optimized interpolation and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the interpolation and receive filters along with a low-complexity decimation technique. Simulation results are presented for time-varying environments and show that the proposed MBER-JIDF receive processing strategy and algorithms achieve a superior performance to existing methods at a reduced complexity.

In this work, we study a joint iterative power allocation and interference suppression technique based on the minimization of the bit error rate (BER) cost function for cooperative direct-sequence code division multiple-access (DS-CDMA)systems that employ multiple relays and the amplifyand-forward (AF) strategy. We develop stochastic gradient (SG) algorithms based on the minimum-BER (MBER) criterion to jointly update the parameter vectors that allocate the power levels among the relays subject to a power constraint and the linear receiver. Simulation results show that the proposed adaptive algorithms significantly outperform the two other comparison schemes.

This paper investigates a robust Tomlinson-Harashima precoding (THP) design for multiple-input multiple-output (MIMO) relay systems based on a multi-branch (MB) strategy. The proposed scheme employs a parallel MB structure at the source according to different pre-stored ordering patterns. For each parallel branch, the robust nonlinear transceiver design consists of a TH precoder at the source along with a linear precoder at the relay and a linear minimum-mean-squared-error (MMSE) receiver at the destination. By taking the channel uncertainties into account, the source and relay precoders are jointly optimised to minimise the MSE. We can finally use an iterative method to obtain the solution for the relay and the source precoders via Karush-Kuhn-Tucker (KKT) conditions. An appropriate selection rule is developed to choose the nonlinear transceiver corresponding to the best branch for data transmission. Simulation results demonstrate that the proposed MB-THP scheme outperforms existing transceiver designs with perfect and imperfect channel state information (CSI).

An improved construction scheme of efficient extended irregular repeat-accumulate (eIRA) codes with easy check matrix encoding is proposed in this paper. At the dual diagonal parts of the right check matrices of the codes, the distances of the nodes are enlarged in their corresponding Tanner graphs by increasing the distances between the adjacent “1” of the matrices. Thus, the probabilities of small loops in the Tanner graphs of the codes are reduced. Therefore, it decreases the error self-feedback and obtains good decoding performance. Moreover, with an extrinsic information transfer (EXIT) chart method, the degree profile of the code can be optimally designed to meet the requirement of optimal decoding performance. Simulation results show that our proposed coding scheme slightly outperforms the contrast original eIRA code about 0.05–0.1 dB at a bit-error-ratio (BER) of 10−5with the same binary phase shift keying (BPSK) system in an additive white Gaussian noise (AWGN) channel. In addition, it obtains lower encoding and decoding complexity since it employs the sparse check matrices for encoding and applies the easy addressing operations both for encoding and decoding. Therefore, the proposed scheme of constructing eIRA codes can be efficiently applied in wireless digital communications.

In this work, we propose a low-complexity adaptive transceiver algorithm for the K-pair multiple-input multiple-output (MIMO) interference channels. The proposed algorithm is based on the joint optimization of transmit and receive vectors using the constrained constant modulus (CCM) criterion. We firstly derive CCM-based expressions for the transmit and receiver vectors. Then, we develop recursive least-squares (RLS) adaptive algorithms for their efficient implementation. Unlike earlier block-based transceivers for MIMO interference channels, the proposed algorithms have low computational complexity and can track the time-varying channels and interference as changes occur in the surrounding wireless environment. In particular, simulation results show that the proposed adaptive algorithms achieve the performance of the Sum-MSE algorithm at a much reduced complexity.

In this work, we propose a minimum mean squared error (MMSE) robust base station (BS) precoding strategy based on switched relaying (SR) processing and limited transmission of side information for interference suppression in the downlink of multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on two optimization criteria to design the robust precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the best interleaving matrix is selected to build the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of imperfect CSI.

This paper addresses the problem of robust Tomlinson-Harashima precoding (THP) for multiple-input multiple-output (MIMO) relay systems. The robust nonlinear transceiver design consists of a TH precoder at the source along with a linear precoder at the relay and an minimum-mean-squared-error (MMSE) receiver at the destination. The imperfect channel state information (CSI) is considered. By taking the channel uncertainties into account, the source and relay precoders are jointly optimised to minimise the mean-squared-error (MSE).We finally use an iterative method to obtain the solution for relay and source precoders via Karush-Kuhn-Tucker (KKT) conditions. Simulation results demonstrate that the proposed scheme outperforms existing transceiver designs with perfect and imperfect CSI.

In this work, the authors propose a low‐complexity variable forgetting factor (VFF) mechanism for recursive least‐squares algorithms in interference suppression applications. The proposed VFF mechanism employs an updated component related to the time average of the error correlation to automatically adjust the forgetting factor in order to ensure fast convergence and good tracking of the interference and the channel. Convergence and tracking analyses are carried out and analytical expressions for predicting the mean‐squared error of the proposed adaptation technique are obtained. Simulation results for a direct‐sequence code‐division multiple access system are presented in non‐stationary environments and show that the proposed VFF mechanism achieves superior performance to previously reported methods at a reduced complexity.

In this work, we propose a low-complexity variable forgetting factor (VFF) mechanism for blind adaptive constrained constant modulus (CCM) recursive least square (RLS) algorithms applied to linear interference suppression in direct-sequence code division multiple access (DS-CDMA) systems. The proposed VFF mechanism employs an updated component relating to the time average of the constant modulus (CM) cost function to automatically adjust the forgetting factor in order to ensure good tracking of the interference and the channel. Analytical expressions for predicting the mean-squared error of the proposed adaptation technique are obtained. Simulation results show that the proposed VFF mechanism achieves superior performance to existing methods at a reduced complexity.

Turbo codes are near Shannon limit channel codes widely used in space communication systems and so on at low signal-to-noise rate (SNR). Timing recovery is one of the key technologies for these systems to work effectively. In this paper, an efficiently iterative timing recovery with Turbo decoding is presented. By maximizing the sum of the square of soft decision metrics from Turbo decoding, it can obtain accurate timing acquisition. And a computation-efficient approximate gradient descent method is adopted to obtain rough estimate of timing offset. Another merit of it is that, by the proposed method, a rate-1/6 Turbo coded binary phase shift keying (BPSK) system can even work at very low SNR (Es/N0) about -7.44 dB without any pilot symbol. Finally, the whole timing recovery scheme is accomplished where the proposed method is combined with Mueller-Muller (M&M) timing recovery which performs the timing track. Simulation results indicate that the Turbo coded BPSK system with rather large timing errors by the proposed scheme can achieve performance within 0.1 dB of the ideal code with reasonable computations and storages.

A novel frequency offset tracking algorithm for Space-Time Block Coded (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems is proposed in this work. Tracking of a frequency offset between the transmitter and the receiver is often aided by transmitting pilots embedded in the data payload. The proposed algorithm mainly exploits the specific construction of the OFDM symbol in STBC-OFDM systems, which does not need any additional pilots or sequences in the data field, providing high efficiency in spectrum. The estimator is derived on the basis of the maximum likelihood (ML) theory. Simulation results show that in a 2 × 2 multiple input multiple output (MIMO) system, under the assumption that the antennas are uncorrelated to each other, this method can provide a significant performance improvement in terms of the estimation accuracy of the frequency offset.

In this paper we propose a novel switched-interleaving algorithm based on limited feedback for both uplink and downlink DS-CDMA systems. The proposed switched chip-interleaving DS-CDMA scheme requires the cooperation between the transmitter and the receiver, and a feedback channel sending the index of the interleaver to be used. The transmit chip-interleaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and the codebook index is sent back using a limited number of bits. In order to design the codebook, we consider a number of different chip patterns by using random interleavers, block interleavers and a proposed frequently selected patterns method (FSP). The best interleaving patterns are chosen by the selection functions of the received signal to interference plus noise ratio (SINR) for both downlink and uplink systems. Since the selection function needs to determine the best interleaver based on the channel state information, it is necessary to predict reliably the channel state information for typical delay values. We present symbol-based and block-based linear minimum mean squared error (MMSE) receivers for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional DS-CDMA (C-CDMA) systems and the existing chip-interleaving, linear precoding and adaptive spreading techniques.

In this work, a new hybrid transmit processing technique based on switched interleaving and chip-wise precoding is proposed to suppress the multiuser interference (MUI) for downlink multi-carrier code division multiple access (MC-CDMA) multiple antenna systems. A set of possible chip-interleavers are constructed and prestored at both the base station (BS) and mobile stations (MSs), which are also equipped with another codebook of quantized downlink channel state information (CSI). Each MS quantizes its own downlink CSI and feeds back the index to the BS by a low-rate feedback channel, then the selection function at the BS determines the optimum interleaver based on all users' quantized CSIs to transmit signals. Simulation results show that the performance of the proposed techniques is significantly better than prior art.

In this paper we propose a novel switched interleaving algorithm based on limited feedback for downlink DS-CDMA systems. The proposed switched chip-interleaving DS-CDMA scheme requires the cooperation among the transmitter, the receiver and a feedback channel sending the index of the interleaver to be used. The transmit chip-inter leaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and we send back the codebook index using a limited number of bits. In order to design the codebook, we consider patterns such as the block interleavers, and a selection function is designed to maximize the received signal to interference plus noise ratio (SINR). We present block-based and symbol-based linear minimum mean squared error (MMSE) receivers for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional CDMA systems and the existing chip-interleaving schemes.

In this paper we propose a novel transmission scheme based on switched interleaving and limited feedback for downlink DS-CDMA systems. The proposed switched interleaving DS-CDMA scheme (SI-CDMA) requires the cooperation among the transmitter, the receiver and a feedback channel sending the index of the interleaver to be used. The transmit chip-interleaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and the codebook index is sent back from the feedback channel using a limited number of bits. In order to design the codebook, we consider patterns such as the block interleavers, and a selection function is designed to maximize the received signal to interference plus noise ratio (SINR). We present a block-based linear minimum mean squared error (MMSE) receiver for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional CDMA systems and the existing chip-interleaving schemes.

In this paper, two low-complexity adaptive step size algorithms are investigated for blind adaptive beamforming. Both of them are used in a stochastic gradient (SG) algorithm, which employs the constrained constant modulus (CCM) criterion as the design approach. A brief analysis is given for illustrating their properties. Simulations are performed to compare the performances of the novel algorithms with other well-known methods. Results indicate that the proposed algorithms achieve superior performance, better convergence behavior and lower computational complexity in both stationary and non-stationary environments.

The paper discusses the key technologies of an all IP architecture for a B3G mobile communication system. The aim is to contribute to the technical innovation of 3G systems by exploiting the potential of an IP-based B3G wireless communication system. The discussion focuses on the realization of an all IP core network. An all IP network architecture, improvement of end-to-end QoS, and flexible service provision are among the major challenges toward the B3G communication system. However, an all IP network architecture is the goal of the evolution of wireless networks on the way to the B3G system. The paper first reviews the development of the 3G core network, and proposes technologies for the all IP architecture for communication between 3G systems and the Internet, such as the assumed all IP network architecture and the design of protocol stacks. The paper describes the architecture of a protocol stack based B3G system, and then elaborates on the functionality of the MPPP, RLC, LLC and MAC layers. Finally, for fully supporting an all IP solution, the paper suggests further research work required based on the IPv6 core network.