Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, together with the positions of MAs, while guaranteeing the individual requirement of each EHR. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs’ requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

This paper investigates detection orientated integrated sensing and communication (ISAC) system aided by hybrid reconfigurable intelligent surface (RIS). Target detection is conducted through communication signal echoes under the practical condition of unknown attenuation coefficient and sensing noise covariance, which makes our study more challenging than existing pertinent works. Firstly, we develop a closedform based generalized likelihood ratio test (GLRT) detector, which first effectively extrapolates unknown parameters through maximum likelihood estimation and then conducts hypothesis testing. Besides, we derive the asymptotic detection probability of the proposed GLRT detector in an analytic form, which is highly accurate for moderate sample size. Based on the above analysis, we propose robust beamforming design to maximize the worst-case detection probability while ensuring ergodic communication rate in awareness of channel state information (CSI) uncertainties. We provide a semidefinite programming (SDP) formulation to solve the robust beamforming problem. Additionally, by converting the variational and ergodic forms in robust formulation into explicit approximations, we further develop an efficient second order cone programming (SOCP) based solution, which is highly reliable when the CSI uncertainty becomes low. Numerical results validate the efficacy of the proposed GLRT detector, the correctness of the detection performance analysis, and the benefit of robust beamforming against CSI uncertainty.

Integrated sensing and communication (ISAC) is a key technology for future 6G networks. Most existing studies focus on monostatic and/or bistatic setups with limited coverage and capabilities. Networked ISAC systems with distributed base stations (BSs) can overcome these limitations. Moreover, movable antenna (MA) architectures offer improved ISAC performance over fixed-position antennas (FPAs) by enabling adaptable antenna movement. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and position configuration of transmit/receive MA towards maximizing the sum rate for both downlink (DL) and uplink (UL) users. The optimization problem is highly difficult due to the unique channel model derived from the position coefficient of the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying the MA framework in the networked ISAC system.

This paper considers an active reconfigurable intelligent surface (RIS) aided device-based uplink integrated sensing and communication (ISAC) system. In this context, base station (BS) receives pilot and communication signals transmitted concurrently from mobile users to provision sensing and communication services. For the considered setup, we investigate beamforming design by jointly optimizing RIS configuration, mobile users’ transmit power and linear combiner at the BS to minimize Cramér-Rao bound (CRB) of angle-of-arrival (AoA) estimation for the sensing users while ensuring spectral efficiency of communication users. The considered device-based sensing paradigm raises unique challenge since communication signals contribute to noise covariance in AoA measurements, which leads to a highly complicated CRB expression. To resolve this challenge, we transfer the problem into a quartic form, equivalently represent covariance matrix inverse into an equation condition, decouple the intractable covariance equality constraint by introducing splitting variables followed by penalty dual-decomposition (PDD) methodology, which develops an iterative process updating all variable blocks alternatively. Extensive numerical results verify the effectiveness of our proposed algorithm and demonstrate the significant advantage of device-based sensing scheme over the device-free counterpart when the sensing targets can get connected in the ISAC network.

This paper investigates hybrid reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario that utilizes the echoes of communication signals to accomplish target detection without prior knowledge on signal attenuation coefficient and sensing noise covariance. To realize effective detection, we develop an analytic based generalized likelihood ratio test (GLRT) detector and theoretically analyze its detection performance. Based on that, we further develop an efficient iterative optimization process to conduct robust beamforming design that improves detection performance against channel state information (CSI) uncertainty while guaranteeing achievable ergodic communication rates. Numerical results verify the effectiveness of our proposed GLRT detector, the correctness of our performance analysis, and the benefit of the developed robust beamforming design.

Federated learning (FL) enables the training of a global machine learning model among multiple local clients in a collaborative fashion without directly sharing the details of their data. Due to this advantage, it has been utilized in a wide range of applications where privacy is a critical concern and has attracted great attention for graph representation learning (GRL). Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. Unlike existing techniques, it utilizes generative models as bridges to enable information sharing among clients, thereby facilitating the collaborative training of GRL models. Instead of directly using the generative model trained on each client individually, we aggregate them into the globally generative model to gain a global view of the entire graph, which effectively alleviates the heterogeneity and complementarity issues simultaneously. We formulate the training of the generative and GRL models as a min-max adversarial learning problem and theoretically prove the convergence. Furthermore, the effectiveness of the method is demonstrated using experimental results on six real-world datasets.

In this paper, we study employing movable components on both base station (BS) and intelligent reflecting surface (IRS) in a wideband terahertz (THz) multiple-input-single-output (MISO) system, where the BS is equipped with a movable antenna (MA) array and the IRS consists of movable subarrays. To alleviate double beam squint effect caused by the coupling of beam squint at the BS and IRS, we propose to maximize the minimal received power across a wide THz spectrum by delicately configuring the positions of MAs and IRS subarrays, which is highly challenging. By adopting majorization-minimization (MM) methodology, we develop an algorithm to tackle the aforementioned optimization. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of utilizing movable components on the BS and IRS to mitigate double beam squint effect in wideband THz communications.

This paper proposes a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) transmission scheme termed spatial scattering shift keying (SSSK), which exploits spatial scattering modulation (SSM) to encode information through the indices of channel scatterers rather than conventional symbol constellations. The proposed SSSK achieves superior reliability compared to amplitude-phase modulation (APM) schemes, while simultaneously reducing hardware complexity. Specifically, the scatterer-index-based signaling mechanism mitigates the detection complexity inherent in APM systems by avoiding explicit symbol-level demodulation. In addition, we illustrate the advantages of SSSK by investigating the interaction between SSSK and fading channels. We derive closed-form expressions for the average bit error probability (ABEP) tight upper bound of the proposed scheme using two different approaches based on the greedy detection algorithm. To gain more insights, we further derive the asymptotic ABEP expression and diversity gain. To characterize the performance, we rigorously derive tight upper bounds on the ABEP using two complementary approaches: union bound and pairwise error probability analysis under a greedy detection framework. Furthermore, asymptotic ABEP expressions are established to reveal the achievable diversity gain. Moreover, we design maximum likelihood (ML) detectors with serial and parallel architectures and corresponding ABEP upper bounds. Simulations validate the analytical derivations and demonstrate SSSK outperforms APM in ABEP at high signal-to-noise ratios. The proposed greedy detector reduces computational complexity compared to the serial ML detector while maintaining comparable ABEP performance.

This paper investigates group-level multi-user precoding schemes for multi-antenna multi-subcarrier systems. Existing literature often presupposes an individual precoder for each tone, uniformly applying standard narrowband precoding techniques to each subcarrier. Such solutions, however, are unfeasible in practice due to unrealistic hardware cost and computational complexity. Motivated by realistic industrial protocol that allocates one common precoder for multiple subcarriers, this paper investigates a more viable sum-rate maximization paradigm employing group-level precoding. We first introduce two innovative schemes for assessing spectral efficiency of subcarrier blocks, that takes into account the fluctuating signal-to-interference-and-noise-ratios (SINRs) across all tones. Our formulation shows that these group-level precoding strategies present highly nonconvex challenges. To address these difficulties, we adopt successive convex approximation (SCA) methodology, which resolves the original nonconvex challenge via iteratively convexifying subproblems. Moreover, we devise low-complexity methods utilizing gradient projection, obviating the necessity for numerical solvers. Numerical experiments affirm the convergence and efficacy of our proposed algorithms. Notably, our 5G link-level simulations reveal that, compared to traditional methods, group-level precoding not only ensures a more uniform distribution of SINRs across subcarriers but also significantly improves throughput performance.

This paper studies an intelligent reflecting surface (IRS)-aided multi-antenna simultaneous wireless information and power transfer (SWIPT) system where anM-antenna access point (AP) servesKsingle-antenna information users (IUs) andJsingle-antenna energy users (EUs) with the aid of an IRS with phase errors. We explicitly concentrate on overloaded scenarios whereK+J>MandK≥M. Our goal is to maximize the minimum throughput among all the IUs by optimizing the allocation of resources (including time, transmit beamforming at the AP, and reflect beamforming at the IRS), while guaranteeing the minimum amount of harvested energy at each EU. Towards this goal, we propose two user grouping (UG) schemes, namely, the non-overlapping UG scheme and the overlapping UG scheme, where the difference lies in whether identical IUs can exist in multiple groups. Different IU groups are served in orthogonal time dimensions, while the IUs in the same group are served simultaneously with all the EUs via spatial multiplexing. The two problems corresponding to the two UG schemes are mixed-integer non-convex optimization problems and difficult to solve optimally. We first provide a method to check the feasibility of these two problems, and then propose efficient algorithms for them based on the big-M formulation, the penalty method, the block coordinate descent, and the successive convex approximation. Simulation results show that: 1) the non-robust counterparts of the proposed robust designs are unsuitable for practical IRS-aided SWIPT systems with phase errors since the energy harvesting constraints cannot be satisfied; 2) the proposed UG strategies can significantly improve the max-min throughput over the benchmark schemes without UG or adopting random UG; 3) the overlapping UG scheme performs much better than its non-overlapping counterpart when the absolute difference betweenKandMis small and the EH constraints are not stringent.

Integrated sensing and communication (ISAC) capability is envisioned as one key feature for future cellular networks. Classical half-duplex (HD) radar sensing is conducted in a “first-emit-then-listen” manner. One challenge to realize HD ISAC lies in the discrepancy of the two systems’ time scheduling for transmitting and receiving. This difficulty can be overcome by full-duplex (FD) transceivers. Besides, ISAC generally has to comprise its communication rate due to realizing sensing functionality. This loss can be compensated by the emerging reconfigurable intelligent surface (RIS) technology. This paper considers the joint design of beamforming, power allocation and signal processing in a FD uplink communication system aided by RIS, which is a highly nonconvex problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Besides, by wisely exploiting alternative direction method of multipliers (ADMM) and optimality analysis, we further develop a low complexity solution that updates all variables analytically and runs highly efficiently. Numerical results are provided to verify the effectiveness and efficiency of our proposed algorithms and demonstrate the significant performance boosting by employing RIS in the FD ISAC system.

Reconfigurable intelligent surface (RIS) has been envisioned as a highly promising technology for future wireless communication networks. Very recently, a novel beyond diagonal (BD)-RIS architecture has been proposed. This new architecture remarkably extends the traditional diagonal RIS model and yields much more powerful beamforming capability. Meanwhile, however, the emerging symmetry and orthogonality conditions imposed onto BD-RIS’ reflection matrix make its optimization highly difficult, especially when BD-RIS must satisfy numerous additional constraints. This difficulty arises in many BD-RIS applications and has remained unsolved so far. To resolve the above challenge, leveraging the penalty dual decomposition methodology, this paper proposes a novel unified approach that can optimize BD-RIS configuration when it is involved in any number of nonconvex constraints. Especially, we utilize our new approach to solve the power minimization and energy efficiency maximization problems when BD-RIS involves multiple quality-of-service constraints, which have not yet been solved in the literature. Besides, our new approach can also efficiently solve the sum-rate maximization in the BD-RIS assisted system by providing a new analytic-update-based solution, which is more efficient than existing methods. Extensive numerical results demonstrate the effectiveness of our new approach and the significant benefit of BD-RIS over the conventional diagonal RIS.

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Channel estimation (CE) of RIS device is a critical but also challenging issue for its development. The mainstream of existing CE methods is confined to the so-called cascaded channel (CscdChn) estimation scheme, which treats the multiplicative two-hop RIS channels as an effective one and measures it as a whole. This CscdChn training method suffers from severe double-fading attenuation loss, which significantly degrades the CE accuracy. In this paper, we propose a novel RIS-transmitting (RIS-TX) based CE scheme, which has lower pilot overhead than CscdChn scheme and effectively overcomes the double-fading curse via incorporating only one single transmit radio frequency (RF)-chain into RIS. We develop highly efficient gradient descent (GD) and penalty duality decomposition (PDD)-based solutions to resolve the pilot design task for the RIS-TX CE scheme, which is a difficult quartic optimization problem. Our designed pilot signal outperforms the discrete Fourier transform (DFT) sequence, which is reported to be optimal for CscdChn scheme. Besides, both theoretical analysis and numerical results demonstrate that our proposed RIS-TX scheme exhibits distinct performance characteristics as opposed to its CscdChn counterpart and yields superior accuracy when RIS device is not extremely large.

This paper studies integrated sensing and communication (ISAC) technology in a full-duplex (FD) uplink communication system. As opposed to the half-duplex system, where sensing is conducted in a first-emit-then-listen manner, FD ISAC system emits and listens simultaneously and hence conducts uninterrupted target sensing. Besides, impressed by the recently emerging reconfigurable intelligent surface (RIS) technology, we also employ RIS to improve the self-interference (SI) suppression and signal processing gain. As will be seen, the joint beamforming, RIS configuration and mobile users' power allocation is a difficult optimization problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Numerical results demonstrate the effectiveness of our proposed solution and the great benefit of employing RIS in the FD ISAC system.

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

The rising reconfigurable intelligent surface (RIS) is a promising technology and a multitude of literature focuses on its channel estimation (CE), which is a critical and challenging task. Most existing works adopt a type of “cascaded channel” training scheme, where the “two-hop” channel cascaded by RIS is treated as one and measured by one shot. As unveiled by the latest researches, however, the concatenated channel suffers from severe fading loss and hence seriously degrades the CE precision. To resolve this difficulty, this paper proposes a novel training scheme. Specifically, being equipped with one transmit RF chain, the RIS broadcasts pilot signals to all other devices during the training period. This novel scheme can overcome double fading loss at a low hardware cost. The pilot design of the newly proposed training scheme is a difficult quartic optimization problem and we develop a gradient descent (GD) based solution to resolve it. Numerical results verify the effectiveness of our solution and demonstrate our training scheme can significantly outperform the traditional cascaded channel training method.

Although the reconfigurable intelligent surface (RIS) technology is envisioned promising to enhance communication from all aspects, including physical-layer security, increasing concerns have lately been cast onto its defects—the severe “double-fading” loss and its confined-to-half-space coverage. Diverse novel RIS architectures have recently emerged to partially overcome these shortcomings, yet perfect solution is still absent. This paper proposes a novel double-faced active (DFA)-RIS structure to surmount the above two prominent defects simultaneously. Furthermore, we utilize the DFA-RIS to promote secrecy performance via jointly designing access point (AP)’s beamforming and DFA-RIS configuration towards maximizing sum secrecy rate (SR). The optimization problem is highly challenging due to the constraints deriving from the DFA-RIS architecture, especially the presence of power splitting parameters. By leveraging majorization–minimization (MM) and penalty dual decomposition (PDD) methods, we develop an efficient solution that updates all variables via convex optimization techniques. Our proposed solution is significant and general as it is applicable to all other cutting-the-edge RIS architectures to maximize sum SR, which has not yet been thoroughly worked out. Numerical results verify the convergence and effectiveness of our proposed algorithm and demonstrate that our proposed DFA-RIS architecture outperforms all other state-of-the-art RIS techniques to enhance communication security.

Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to the severe double path loss, the traditional passive RIS can provide sufficient gain only when receivers are very close to the RIS. Moreover, RIS cannot provide signal coverage for the receivers at the back side of it. To address these drawbacks in practical implementation, we introduce a novel reflection and transmission dual-functional active RIS (DF-ARIS) architecture in this paper, which can simultaneously realize reflection and transmission functionalities with active signal amplification to significantly extend signal coverage and enhance the quality-of-service (QoS) of all users. The problem of joint transmit beamforming and dual-functional active RIS design is investigated in RIS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both sum-rate maximization and power minimization problems are considered. To address their non-convexity, we develop efficient iterative algorithms to decompose them into several separate design problems, which are efficiently solved by exploiting fractional programming (FP) and Riemannian-manifold optimization techniques. Simulation results demonstrate the superiority of the proposed dual-functional active RIS architecture and the effectiveness of our proposed algorithms over various benchmark schemes.

Although reconfigurable intelligent surface (RIS) technology has manifested great potentials in improving wireless network’s power saving, most existing literature restricts to pure physical (PHY) layer beamforming design and neglects the impact of media access control (MAC) layer’s data traffic flows. Simultaneously, current RIS technology suffers from defects — the severe fading loss and the limitation of half-space coverage. This paper aims to perform a cross-layer design via jointly optimizing MAC layer scheduling and PHY layer RIS beamforming to reduce power consumption. Besides, we propose a novel double-faced-active (DFA)-RIS architecture to promote RIS’ capability. The proposed design task leads to a highly challenging stochastic problem to minimize long-term power consumption while stabilizing queues. Inspired by Lyapunov control theory, we propose an online optimization strategy to resolve this challenge. Via exploiting alternative directional method of multipliers (ADMM), we develop an analytic-based solution to solve the online sub-problems highly efficiently without resorting to any numerical solvers. Our strategy theoretically guarantees all queues’ stability and achieves a tunable trade-off between the power expenditure and queue lengths. Extensive numerical results are presented to demonstrate the effectiveness of our proposed cross-layer design and the DFA-RIS’ advantage over other cutting-the-edge RIS architectures.

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links.

With the development of blockchain and communication techniques, the Metaverse is considered as a promising next-generation Internet paradigm, which enables the connection between reality and the virtual world. The key to rendering a virtual world is to provide users with immersive experiences and virtual avatars, which is based on virtual reality (VR) technology and high data transmission rate. However, current VR devices require intensive computation and communication, and users suffer from high delay while using wireless VR devices. To build the connection between reality and the virtual world with current technologies, mobile augmented reality (MAR) is a feasible alternative solution due to its cheaper communication and computation cost. This paper proposes an MAR-based connection model for the Metaverse, and proposes a communication resources allocation algorithm based on outer approximation (OA) to achieve the best utility. Simulation results show that our proposed algorithm is able to provide users with basic MAR services for the Metaverse, and outperforms the benchmark greedy algorithm.

Dual-function radar-communication (DFRC) systems, which can efficiently utilize the congested spectrum and costly hardware resources by employing one common waveform for both sensing and communication (S&C), have attracted increasing attention. While the orthogonal frequency division multiplexing (OFDM) technique has been widely adopted to support high-quality communications, it also has great potentials of improving radar sensing performance and providing flexible S&C. In this paper, we propose to jointly design the dual-functional transmit signals occupying several subcarriers to realize multi-user OFDM communications and detect one moving target in the presence of clutter. Meanwhile, the signals in other frequency subcarriers can be optimized in a similar way to perform other tasks. The transmit beamforming and receive filter are jointly optimized to maximize the radar output signal-to-interference-plus-noise ratio (SINR), while satisfying the communication SINR requirement and the power budget. An majorization minimization (MM) method based algorithm is developed to solve the resulting non-convex optimization problem. Numerical results reveal the significant wideband sensing gain brought by jointly designing the transmit signals in different subcarriers, and demonstrate the advantages of our proposed scheme and the effectiveness of the developed algorithm.

Reconfigurable intelligent surface (RIS) is envisioned as a promising technology to enhance future wireless communication systems. Very recently, a novel active RIS architecture has been proposed via introducing amplifiers into the reflecting elements. Although these embedded amplifiers can effectively extend the RIS coverage, they also bring non-negligible energy expenditure. To overcome this drawback, this paper proposes a novel sub-array based structure, which divides the entire RIS array into multiple sub-arrays with each being flexibly turned on/off. We aim to minimize the power consumption of the whole system via jointly activating sub-arrays and designing beamforming, which is highly challenging due to its combinatorial nature. Via inducing the group sparsity and leveraging the majorization-minimization (MM) approach, we develop an efficient solution to resolve this challenge. Numerical results demonstrate that our proposed sub-array structure can significantly reduce the power consumption compared to the conventional “all-on” scheme.

In this paper, we consider the weighted sum of transferred power maximization under the secrecy rate (SR) constraints in a secure simultaneous wireless information and power transfer (SWIPT) communication network assisted by reconfigurable intelligent surfaces (RIS). To tackle this challenging problem, we combine the cutting-the-edge successive convex approximation (SCA) and penalty dual decomposition (PDD) methods and have successfully developed a novel iterative solution. Compared to the existing literature, our newly proposed algorithm can apply to the most generic system setting that has arbitrary number of information and/or energy receivers. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of RIS deployment.

In this paper, we investigate the physical layer security of multiuser multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence systems, in which the strong radar signals are exploited as inherent jamming signals to disrupt mali-cious receptions. The transmit beamformers of communication and radar systems are jointly designed to ensure the secure transmission by minimizing the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while satisfying the quality-of-service (QoS) of legitimate transmission, the requirement of radar target detection, and the transmit power constraints of radar and communication systems. An efficient fractional programming (FP) and semi-definite relaxation (SDR) based algorithm is proposed to solve the non-convex optimization problem. Simulation results verify the advancement of the proposed joint transmit beamforming on secure transmission for radar and communication coexistence systems and the effectiveness of the associate design algorithm.

Reconfigurable intelligent surface (RIS) is a promising solution to adaptively manipulate wireless propagation with low-cost passive devices. However, the traditional passive RIS can offer sufficient signal strength only when receivers are very close to it. Moreover, the users at the back side of it cannot be well served due to its reflective property. In this paper we introduce a novel reflection and relay dual-functional RIS architecture, which can simultaneously realize passive reflection and active relay functionalities. The problem of joint transmit beamforming and dual-functional RIS design is investigated to maximize the achievable sum-rate of a multiuser multiple-input single-output (MU-MISO) system. Based on fractional programming (FP) theory and majorization-minimization (MM) technique, we propose an efficient iterative transmit beamforming and RIS design algorithm. Simulation results demonstrate the superiority of the introduced dual-functional RIS architecture and the effectiveness of the proposed algorithm.

Owing to the unique advantages of low cost and controllability, reconfigurable intelligent surface (RIS) is a promising candidate to address the blockage issue in millimeter wave (mmWave) communication systems, consequently has captured widespread attention in recent years. However, the joint active beamforming and passive beamforming design is an arduous task due to the high computational complexity and the dynamic changes of wireless environment. In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm to jointly design active hybrid beamformer at the base station (BS) side and passive beamformer at the RIS side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a maximum entropy based DRL algorithm, which can explore more stochastic policies than deterministic policy, to design active analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method. The experimental results demonstrate that our proposed SAC algorithm can achieve better performance compared with conventional optimization algorithm and DRL algorithm.

Reconfigurable intelligent surface (RIS) is considered as an extraordinarily promising technology to solve the blockage problem of millimeter wave (mmWave) communications owing to its capable of establishing a reconfigurable wireless propagation. In this paper, we focus on a RIS-assisted mmWave communication network consisting of multiple base stations (BSs) serving a set of user equipments (UEs). Considering the BS-RIS-UE association problem which determines that the RIS should assist which BS and UEs, we joint optimize BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate of the system. To solve this intractable non-convex problem, we propose a soft actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming and BS-RIS-UE association design algorithm, which can learn the best policy by interacting with the environment using less prior information and avoid falling into the local optimal solution by incorporating with the maximization of policy information entropy. The simulation results demonstrate that the proposed SAC-DRL algorithm can achieve significant performance gains compared with benchmark schemes.

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement.

Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes.

Reconfigurable intelligent surface (RIS) is regarded as a promising technology with great potential to boost wireless networks. Affected by the “double fading” effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS. Recently, active RIS is introduced to combat the double fading effect by actively amplifying incident signals with the aid of integrated reflection-type amplifiers. In order to reduce the hardware cost and energy consumption due to massive active components in the conventional fully-connected active RIS, a novel hardware-and-energy efficient sub-connected active RIS architecture has been proposed recently, in which multiple reconfigurable electromagnetic elements are driven by only one amplifier. In this paper, we first develop an improved and accurate signal model for the sub-connected active RIS architecture. Then, we investigate the joint transmit precoding and RIS reflection beamforming (i.e., the reflection phase-shift and amplification coefficients) designs in multiuser multiple-input single-output (MU-MISO) communication systems. Both sum-rate maximization and power minimization problems are solved by leveraging fractional programming (FP), block coordinate descent (BCD), second-order cone programming (SOCP), alternating direction method of multipliers (ADMM), and majorization-minimization (MM) methods. Extensive simulation results verify that compared with the conventional fully-connected structure, the proposed sub-connected active RIS can significantly reduce the hardware cost and power consumption, and achieve great performance improvement when power budget at RIS is limited.

Power saving and battery-life extension have always been a critical concern for IoT network deployment. One effective solution is to switch wireless devices into sleep mode to save power. This paper considers the power control in an IoT network via jointly activating IoT sensors and designing their transmit beamforming. Besides, inspired by the great potential of reconfigurable intelligent surface (RIS) in energy saving, we additionally introduce RIS to further lower the sensors’ power consumption. The considered problem is highly challenging due to its combinatorial nature, the highly non-convex quality-of-service (QoS) constraint and the hardware restrictions from the RIS. By exploiting the cutting-the-edge majorization minimization (MM) and the penalty dual decomposition (PDD) frameworks, we have successfully developed highly efficient solutions to tackle this problem. Our proposed solutions can achieve nearly identical performance with that of the exhaustive search but with a much lower complexity. Besides, as revealed by the numerical experiments, our proposed sensor activation scheme can switch off a large portion of sensors under mild QoS requirements, which significantly reduces power expenditure. Moreover, the deployment of RIS can bring an additional 45% – 70% power saving compared to the no-RIS case.

Intelligent reflecting surface (IRS) has been widely considered as one of key enabling techniques for the future wireless networks owing to its ability of constructing favorable propagation environment by controlling the phase shifts of reflected electromagnetic (EM) waves that impinge on the surface. While an IRS only focuses on the reflective implementation, recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual-functionality of manipulating signal reflection and transmission. Thus, an IOS can provide service coverage for both sides of it. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system, in which the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Our goal is to jointly optimize the transmit beamformers at base station (BS), the reflective and transmissive phase-shifts of IOS, and the reflection-to-transmission ratio of IOS to minimize the total transmit power for the MU-MISO system, subject to the signal-to-interference-plus-noise ratio (SINR) requirements of individual users. An efficient iterative algorithm is presented to solve this non-convex optimization problem. Simulation results verify the advantage of the IOS-assisted wireless communication system and the efficiency of the associate beamforming design algorithm.

Symbol-level precoding (SLP), which can convert the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol error rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol SLP design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop an efficient design algorithm for a typical max-min fairness problem. This practical G-SLP strategy divides all users into several groups. SLP is utilized for the users within each group to convert intra-group MUI into constructive interference, meanwhile the inter-group MUI is also suppressed. In particular, we first use Lagrangian and Karush-Kuhn-Tucker (KKT) conditions to simplify the G-SLP design problem and then propose an iterative majorization-minimization (MM) based algorithm to solve it. Simulation results illustrate that the proposed G-SLP strategy dramatically reduces the computational complexity without causing significant performance loss compared with the traditional SLP scheme.

Internet of things (IoT) technology is an essential enabler to realize ubiquitous connections and pervasive intelli-gence for the future wireless communication system. The energy self-sustainability based on the wireless power transfer technique and the coexistence with heterogeneous networks will become two predominant attributes of IoT networks. In this paper we consider the system design in a context of coexistence of a wireless powered sensor network and a cellular system, both of which share common spectrum bandwidth and are assisted by intelligent reflecting surface (IRS). Specifically, the wireless sensors exploit the harvested energy from the cellular base station (BS) to transfer information to a data sink. We aim to design a cooperation scheme via jointly optimizing the time allocation of channel use, collaborative beamforming across networks and IRS phase-shifting control to improve the sensing network's throughput while guaranteeing the cellular users' quality of service. This design problem leads to a highly nonconvex and difficult mathematical optimization problem. Via utilizing the penalty-duality-decomposition (PDD) and successive convex approximation (SCA) methods, we have managed to develop an alternative optimization solution. Nu-merical results verify the effectiveness of our algorithm and demonstrate the benefits that come from the cooperative network design.

In dual-functional radar-communication (DFRC) systems, the transmit beamforming has attracted extensive attentions since it can simultaneously provide radar sensing functionality and high-rate wireless communications. Unlike the conventional linear precoding technology, in this paper we propose to employ the recently emerged symbol-level precoding technique in DFRC systems, expecting to take advantages of the multiuser interference for improving both radar and communication performance. The difference between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of communication users and the constant envelope power constraint. Some derivations are developed based on the penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the non-convex problem into two solvable sub-problems, which are iteratively solved using efficient algorithms. Simulations illustrate the effectiveness of the symbol-level precoding in DFRC systems and the proposed algorithm.

Intelligent reflecting surface (IRS) is deemed as a promising and revolutionizing technology for future wireless communication systems owing to its capability to intelligently change the propagation environment and introduce a new dimension into wireless communication optimization. Most existing studies on IRS are based on an ideal reflection model. However, it is difficult to implement an IRS which can simultaneously realize any adjustable phase shift for the signals with different frequencies. Therefore, the practical phase shift model, which can describe the difference of IRS phase shift responses for the signals with different frequencies, should be utilized in the IRS optimization for wideband and multi-band systems. In this paper, we consider an IRS-assisted multi-cell multi-band system, in which different base stations (BSs) operate at different frequency bands. We aim to jointly design the transmit beamforming of BSs and the reflection beamforming of the IRS to minimize the total transmit power subject to signal to interference-plus-noise ratio (SINR) constraints of individual user and the practical IRS reflection model. With the aid of the practical phase shift model, the influence between the signals with different frequencies is taken into account during the design of IRS. Simulation results illustrate the importance of considering the practical communication scenario on the IRS designs and validate the effectiveness of our proposed algorithm.

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

A typical wireless sensor network (WSN) has multiple sensors, each of which obtains noisy observation of one common physical event and sends the observation to a fusion center (FC) for post-processing. This paper provides a comprehensive research on robust linear transceiver design in the presence of imperfect channel-state information (CSI) in multi-input multi-output (MIMO) WSNs. For the CSI uncertainty, the classical ellipsoid model is assumed, where channel estimation errors live in an ellipsoid. Two robust beamforming problems are considered: 1) the worst-case mean-square-error (MSE) minimization with limited power and 2) sum-power minimization with guaranteed worst-case MSE. For both problems, centralized and decentralized solutions are developed. Moreover, an interesting fundamental relation between the metrics of signal-to-noise ratio (SNR) and MSE has been presented, which extends our proposed solutions to handle the transceiver design when SNR is considered. Extensive numerical results are presented to confirm our findings.

This paper considers joint precoder design to optimize throughput, power consumption and energy efficiency (EE) in the context of multi-antenna wireless sensor networks with coherent multiple access channels. To maximize throughput, both centralized and decentralized algorithms are developed. Our centralized algorithm obtains a new second order cone programming formulation of the problem, which is different from related works and can apply to more generic system setup compared to existing literature. In addition, noting the fact that all existing solutions in literature are centralized based, we propose a novel decentralized solution and analyses its convergence. Besides the throughput maximization, the power consumption and EE problems are also attacked. To optimize these two metrics, a decentralized algorithm based on dual-decomposition and block successive upper-bound method has been developed, which runs in parallel with semi-analytical solutions and has provable strong convergence. A sufficient condition for the validity of the decentralized method is obtained. Extensive numerical results are presented to consolidate our findings.

A two-hop parallel-relay network is considered in this paper. Conventional schemes investigating the coding strategies at the relay(s) have largely focused on hard encoding, with the exception of one pioneering strategy that proposed a soft distributed encoding using the soft estimate (tanh-based) information. To fully harness the gain promised by soft encoding, this paper focuses on distributed soft encoding strategies at the relays. Unlike the previous work that favors the tanh form for soft encoding, we advocate the range-limited log-likelihood ratios (rLLR) as a better way for the relays to capture the reliability of the messages sent by the sender, and especially to further soft-encode these messages. Based on this, we develop a simple but effective soft-encoding relay (SoER) strategy that exploits the useful features of rLLR. Specifically, the close resemblance of rLLR to the tanh form allows us to derive a very simple convolutional encoding, and the piece-wise linearity of rLLR allows us to evaluate the codeword probability density function (PDF) analytically, which further allows us to derive a Viterbi decoding algorithm using a more precise PDF (in addition to the Gaussian-approximated Viterbi algorithm). We finally extend the nonrecursive convolutional SoER strategy to the turbo SoER strategy. Simulation results confirm the efficiency of the new proposed SoER schemes.

This paper investigates a cooperative transmission scheme for a multi-source single-destination system through signal-superposition-based braid coding. The source nodes take turns to transmit, and each time, a source “overlays” its new data together with (some or all of) what it overhears from its partner(s) using signal superposition, in a way similar to French-braiding the hair. We demonstrate how the resultant braid coding can be effectively employed in M-to-1 data collection networks to achieve progressive cooperation. We analyze two subclasses of braid coding, the nonregenerative and the regenerative cases, and, using the pairwise error probability (PEP) as a figure of merit, derive the optimal weight parameters theoretically for each class. For the regenerative case, a modified Viterbi maximum-likelihood (ML) estimator is proposed, whose complexity is linear to the message length. We compute the (Euclidean) free distance, and identify the memory size that strikes the best balance between performance and complexity. The proposed cooperative framework based on braid coding is general and subsumes several previous superposition modulation-based cooperative schemes as its special case. Simulations confirm the efficiency of the proposed schemes.

This paper considers soft-message forwarding in a 2-hop wireless network. Previous methods have only considered the source-relay channel quality but ignored the relay-destination channel quality, causing potential sub-optimality especially in a parallel-relay setting. This paper takes a centralized approach by accounting for all the individual channel-segments, and proposes a “Z-forward” strategy, in which the i-th relay represents the forward messages in a parameterized piece-wise linear form: θi-truncated log-likelihood ratio (LLR) of its reception. This message representation not only is numerically stable, and soft-information-preserving, but also allows us to analytically derive the end-to-end bit error rate (with maximal ratio combining (MRC)), and to compute the optimal values of θinumerically. The results confirm that previous message-forward proposals, however a good performance in a single-relay setting, will considerably degrade as the the number of relays increases. Next, to further simplify the design, we propose a single threshold θ for all the relays, in lieu of one for each, and show that it strikes a balance between performance and computation. Additionally, with Z-forward, we are able to derive the exact probability density function (pdf) of the final reception at the destination, and subsequently to develop the maximum likelihood (ML) estimator. Extensive simulations are presented to verify the efficiency of the new schemes.

This paper proposes an optimal estimate-forward strategy, termed Z-forwarding, for 2-hop parallel-relay systems. The previous tanh-forwarding strategy, which is optimized for the single-relay system is shown to be no longer optimal for a parallel-relay system. Instead, a new, parametrically-optimized Z-forwarding strategy is proposed, where the relay re-transmits a nonlinear but piece-wise linear function of the log-likelihood ratio (LLR) of the source signal. By analytically formulating the end-to-end bit error rate (BER), optimal thresholds that minimize the BER are computed as a function of all the source-relay and relay-destination channels. Maximum likelihood (ML) detector is also developed for the destination to recoup all the diversity gains from the multiple relays. It is shown that Z-forwarding strategy delivers a performance comparable to tanh-forwarding in a single relay system, but considerably better than tanh-forwarding (as well as amplify-forward and decode-forward) in a parallel-relay system.

This paper proposes an efficient method for optimal joint precoding-postcoding design in a multi-input multi-output (MIMO) multi-sensor noisy observation context - a problem that is of great interest to the multi-relay MIMO transmission system. A set of wireless sensors, each provisioned with a different number of antennas and a different power constraint, precode and send their noisy observations of the same data to a common fusion center, which postcodes the data to make a best estimate of the original data. Taking the mean square error as a performance metric, we show that the optimal joint precoding-postcoding design problem is non-convex. Leveraging the alternative minimization framework, we are able to decompose it to two convex subproblems, one of which promises closed-form solutions. However, unlike previous studies that assume a total power constraint, the condition of individual power constraint and individual noise uncertainty at each sensor has tremendously complicated the second convex subproblem. Rather than numerically solve it via conventional convex optimization tools, we attack it analytically by transforming, approximating, and decomposing it to a set of new problems. We show that the new problems can be efficiently tackled via the Karush-Kuhn-Tucker conditions in an iterative manner. Simultions show that it leads to a convergence much faster and more robust than the conventional convex optimization tools.

This paper considers a multi-input multi-output (MIMO) wireless relay network, where the source S communicates wirelessly to the destination D via the help of a nonregenerative relay R. Previous studies are almost exclusively based on the assumptions of equal time-duration phases for S-R and R-D transmission and equal number of multiple antennas for every node, and the results concerning arbitrary time scheduling and arbitrary antenna arrays are not yet known. To achieve the best end-to-end data rate, this paper considers joint source and relay beamforming design combined with time scheduling and subchannel pairing, in accordance to respective link conditions. We first propose a practical beamforming framework that is inspired by the optimal MIMO relay beamforming structure. It is shown that in this framework, after subchannel pairing, the joint beamforming design problem can be transformed to a joint optimal power allocation problem. Since the overall optimization problem is nevertheless very difficult, involving both continuous and integer variables as well as a non-convex objective function make, we solve it by enumerating integer variables, and with each given integer values, performing alternative optimization and decomposing the problem into convex subproblems. Numerical results confirm the effectiveness of our approach, and it is shown that relaxing the equal time-duration constraint and pairing subchannels optimally can significantly improve the end-to-end capacity.

This paper considers optimal transceiver design for a multi-terminal multi-inputmulti-output (MIMO) system, where L sensors wirelessly communicate individually-contaminated observations of the same source to the fusion center. The constraint that each sensor has individual power cap significantly complicates the non-convex optimization problem, and the optimal (linear) precoding and postcoding are not previously known. Using the signal-to-noise-ratio (SNR) as the performance metric, and employing the alternative minimization approach, we decompose the original problem into multiple subproblems that will run iteratively. The key results include the development of a closed-form solution to the optimal postcoder given the precoders, and the development of a closed-form solution for the ε-optimal precoders given the postcoder. The former is achieved via eigenvalue decomposition, and the latter is achieved by bounding the optimal solutions from above and from below, designing a series of fast-converging bisection search, and developing the closed-form analytical solution for each search. The convergence and the complexity of the proposed algorithm is analyzed and simulations are provided to confirm the efficiency of our proposal.

This paper considers wireless sensor networks with limited bandwidth, where sensors must transmit sensing results that are distorted with noise uncertainty, to the fusion center reliably and efficiently. A class of rate-1 linear complex-field coding (CFC) transmission strategy is developed, to effectively protect the noisy observation of the signals without any bandwidth expansion. Pairwise error probability (PEP) is analyzed, design criterion for optimal complex-field coding strategies is derived, and optimal codes are examined. In addition to maximum likelihood (ML) decoding, a low-complexity decoding scheme termed partially nulling and canceling (PNC) is also proposed. The PNC decoder, representing a midway tradeoff between sphere decoding and (linear) minimum mean square error (MMSE) detection, strikes a good balance between complexity and performance. Extensive simulations show that the proposed CFC is able to reap considerable gains without any bandwith epansion!

We consider the relaying of binary antipodal signals across two hops via soft regeneration and soft error protection. The signals at the input of the regenerator are degraded by additive white Gaussian noise (AWGN). Traditional approaches either directly relay the noisy analog waveform to the destination (thus missing the opportunity for coding gain), or make hard detection and then re-encode the bits using a digital error correction code (which may risk error propagation). This paper proposes a new class of soft-input soft-output (SISO) encoding strategies that are shown to outperform the conventional hard and soft forwarding schemes. Making essential use of the trellis structure, the soft encoder and the maximum likelihood decoder both operate efficiently in linear time, and support variable block sizes and code rates. Practical applications in relay communication are discussed. Simulations confirm the outstanding performance of the proposed scheme.

In the paper, a new distributed coding scheme for parallel relay systems is proposed, in which a sender communicates to a destination that is two hops away via two (or more) parallel relays. The key idea is the exploitation of a (rate-1) soft convolutional encoder at each of the parallel relays, to collaboratively form a simple but powerful distributed analog coding scheme to achieve efficient forwarding of soft reliability messages. We detail the encoding and decoding process of the proposed soft-encoding distributed coding. As the input of the encoder would affect the overall performance, we analyze what form of messages at the relay is most appropriate to be forwarded to the destination. The range-limited log likelihood ratio (range-limited LLR) is chosen as the input. The optimality of the range-limited LLRs as the best form of relaying messages is verified by the simulation results. Our new distributed coding scheme can obviously outperform the existing ones.

This paper presents a new paradigm for image transmission through analog error correction codes. Conventional schemes rely on digitizing images through quantization (which inevitably causes significant bandwidth expansion) and transmitting binary bit-streams through digital error correction codes (which do not automatically differentiate the different levels of significance among the bits). To strike a better overall performance in terms of transmission efficiency and quality, we propose to use a single analog error correction code in lieu of digital quantization, digital code and digital modulation. The key is to get analog coding right. We show that this can be achieved by cleverly exploiting an elegant “butterfly” property of chaotic systems. Specifically, we demonstrate a tail-biting triple-branch baker's map code and its maximum-likelihood decoding algorithm. Simulations show that the proposed analog code can actually outperform digital turbo code, one of the best codes known to date! The results and findings discussed in this paper speak volume for the promising potential of analog codes, in spite of their rather short history.