The asymmetrical uplink and downlink transceiver architecture has emerged as a promising solution to reduce hardware cost and complexity in massive multiple-input multiple-output (MIMO) systems, especially under scenarios with dense antenna deployments, such as uniform planar arrays (UPAs). However, accurate full-dimensional channel state information (CSI) recovery becomes more challenging than uniform linear array (ULA) scenarios due to the significantly reduced number of radio frequency (RF) chains. Directly extending the ULA channel recovery method into UPAs will not only result in high computational complexity but also introduce angle estimation ambiguity owing to the extra vertical array dimension. To address these challenges, we propose a channel recovery framework for UPA-assisted massive MIMO systems with asymmetrical transceiver architectures. First, we introduce the concept of the mixed angle to deal with the low elevation angular resolution originating from the compact array form, and a virtual array is then constructed based on the mixed angle via the spatial correlation matrix. After that, an antenna selection algorithm is designed to maximize the virtual array aperture with a minimal number of RF chains, and a low-complexity UPA-based modified newtonized orthogonal matching pursuit (UPA-based mNOMP) channel recovery algorithm is developed to enable accurate full-dimensional CSI reconstruction. Finally, the imperfect spatial correlation matrix is considered and a orthogonal rank-one matrix pursuit-based spatial correlation matrix recovery algorithm is proposed to recover the spatial correlation matrix from its spatial sparse measurements by exploiting the low-rank property of massive MIMO channels. Simulation results validate the superiority of the proposed algorithms in achieving excellent full-dimensional channel recovery performance for asymmetrical transceiver-based massive MIMO systems with UPAs.

Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead. Recent works have explored the capability of out-of-band (OOB) modalities in obtaining spatial characteristics of wireless channels and reducing pilot overhead in single-BS single-user/multi-user systems. However, applying OOB modalities for multi-BS selection towards dense cell deployments leads to high coordination overhead, i.e, excessive computing overhead and high latency in data exchange. How to leverage OOB modalities to eliminate pilot overhead and achieve efficient multi-BS coordination in multi-BS systems remains largely unexplored. In this paper, we propose a novel OOB modality synergy (OMS) based mobility management scheme to realize multi-user beam prediction and proactive BS selection by synergizing two OOB modalities, i.e., vision and location. Specifically, mobile users are initially identified via spatial alignment of visual sensing and location feedback, and then tracked according to the temporal correlation in image sequence. Subsequently, a binary encoding map based gain and beam prediction network (BEM-GBPN) is designed to predict beamforming gains and optimal beams for mobile users at each BS, such that a central unit can control the BSs to perform user handoff and beam switching. Simulation results indicate that the proposed OMS-based mobility management scheme enhances beam prediction and BS selection accuracy and enables users to achieve 91% transmission rates of the optimal with zero pilot overhead and significantly improve multi-BS coordination efficiency compared to existing methods.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

We investigate wideband mmWave massive multiple-input multiple-output (MIMO) beamforming for near-field/far-field integrated sensing, communication and computation over-the-air (ISCCO) systems with multi-antenna receivers, a scenario that has not been addressed in existing works focusing on single-antenna receivers for near-field beamforming. The data from integrated sensing and communication devices is transmitted to a multi-antenna access point for data fusion by utilizing over-the-air computation, which improves spectral efficiency and reduces overhead through the addition of analog waves. We formulate the near-field/far-field wideband mmWave massive MIMO beamforming problem by maximizing the computational mean square error performance over subcarriers while guaranteeing the sensing performance measured by Cram´er-Rao bound subject to the power constraint. We propose two approaches for solving this problem. The first approach provides a fully-digital scheme serving as a performance benchmark by using the alternating direction method of multipliers algorithm. The second approach aims to further reduce computational complexity by multibeam beamforming with respect to the carefully designed analog beamformer based on the approximated channel. Simulation results demonstrate the effectiveness and low complexity of our proposed multibeam beamformer, applicable to near-field/far-field wideband mmWave ISCCO systems.

Cascaded reconfigurable intelligent surfaces (RISs) greatly improve network coverage and reliability. This paper examines the outage probability (OP) of multi-cascaded RISs (MCRISs)-aided systems over doubly-correlated Rayleigh multiple-input multiple-output (MIMO) channels. The moment-generating function is invoked to convert the OP into a numerical inversion of Laplace integral. To capture profound insights, we conduct an in-depth asymptotic investigation into the outage behavior of MCRIS-aided MIMO communications by leveraging random matrix theory in the high-SNR regime. The asymptotic results reveal that the firsthRISs with the smallest number of reflective elements primarily dictate the bottleneck of reliability performance, wherehis influenced by the variation in the number of reflective elements across the RISs. Specifically, smaller variations result in a largerh, while greater disparities reduce it. Additionally, we identify an “unsaturation effect” that occurs when the performance margin, or residual spatial degree of freedom (DoF), after propagation through the firsthRISs cannot be evenly distributed between the transmitter and the RISs. This effect slows down the decline of the OP with increasing SNR. More cascaded RISs impair the spatial DoF of wireless communications, resulting in the loss of half of the independent fading channels compared to the system without the support of RIS as the cascaded number of RISs increases. Majorization theory is subsequently applied to unveil the negative effect of doubly-spatial correlation on system reliability. Finally, Monte Carlo simulations are carried out for validations.

Many emerging artificial intelligence-driven applications rely on the availability of large-scale, stable, and fresh sensing data, underscoring the Ultra-Reliable Low-Latency Communications (URLLC). This paper proposes to amalgamate Non-Orthogonal Multiple Access (NOMA) and Hybrid Automatic Repeat reQuest with Incremental Redundancy (HARQ-IR) to accommodate reliable and real-time wireless services. The outage probability and the Average Age of Information (AAoI) are used to evaluate the transmission reliability and data freshness for HARQ-IR-NOMA schemes. To reveal physical insights as well as ease system designs, the asymptotic outage probability in the high Signal-to-Noise Ratio (SNR) regime is derived by developing a recursive dominant term approximation method. Moreover, the AAoI is deduced in terms of the outage probability by considering an M/G/1/1 queuing model. The asymptotic AAoI at high SNR is shown to be an increasing function of the diversity order-deficiency, which theoretically justifies the tradeoff between the transmission reliability and the data freshness. Furthermore, the AAoI is minimized by optimizing the transmission powers between users as well as HARQ rounds while maintaining the outage and total power constraints. The minimal AAoI is obtained by developing Geometric Programming (GP)-based and Deep Reinforcement Learning (DRL)-based methods. Finally, the numerical results are presented for verification.

Reconfigurable distributed antenna and reflecting surface (RDARS) is a new architecture for the sixth-generation (6G) millimeter wave (mmWave) communications. In RDARS-aided mmWave systems, the active and passive beamforming design and working mode configuration for reconfigurable elements are crucial for system performance. In this paper, we aim to maximize the weighted sum rate (WSR) in the RDARS-aided mmWave system. To take advantage of RDARS, we first design a reconfigurable codebook (RCB) in which the number and dimension of the codeword can be flexibly adjusted. Then, a low overhead beam training scheme based on hierarchical search is proposed. Accordingly, the active and passive beamforming for data transmission is designed to achieve the maximum WSR for both space-division multiple access (SDMA) and time-division multiple access (TDMA) schemes. For the TDMA scheme, the optimal number of RDARS transmit elements and the allocated power budget for WSR maximization are derived in closed form. Besides, the superiority of the RDARS is verified and the conditions under which RDARS outperforms RIS and DAS are given. For the SDMA scheme, we characterize the relationship between the number of RDARS connected elements and the user distribution, followed by the derivation of the optimal placement positions of the RDARS transmit elements. High-quality beamforming design solutions are derived to minimize the inter-user interference (IUI) at the base station and RDARS side respectively, which nearly leads to the maximal WSR. Finally, simulation results confirm our theoretical findings and the superiority of the proposed schemes.

Unmanned aerial vehicle (UAV) is regarded as a key enabling platform for low-altitude economy, due to its advantages such as three-dimensional (3D) maneuverability, flexible deployment, and line-of-sight (LoS) air-to-air/ground communication links. In particular, the intrinsic high mobility renders UAV especially suitable for operating as a movable antenna (MA) from the sky. In this paper, by exploiting the flexible mobility of UAV swarm and antenna position adjustment of MA, we propose a novel UAV swarm enabled two-level MA system, where UAVs not only individually deploy a local MA array, but also form a larger-scale MA system with their individual MA arrays via swarm coordination. We formulate a general optimization problem to maximize the minimum achievable rate over all ground user equipments (UEs), by jointly optimizing the 3D UAV swarm placement positions, their individual MAs’ positions (or local positions), and receive beamforming for different UEs. To gain useful insights, we first consider the special case where each UAV has only one antenna, under different scenarios of one single UE, two UEs, and arbitrary number of UEs. In particular, for the two-UE case, we derive the optimal UAV swarm placement positions in closed-form that achieves inter-UE interference (IUI)-free communication when the uniform plane wave (UPW) model holds, where the UAV swarm forms a uniform sparse array (USA) satisfying minimum safe distance constraint. While for the general case with arbitrary number of UEs, we propose an efficient alternating optimization algorithm to solve the formulated non-convex optimization problem. Then, we extend the results to the case where each UAV is equipped with multiple antennas. Numerical results verify that the proposed low-altitude UAV swarm enabled MA system significantly outperforms various benchmark schemes, thanks to the exploitation of two-level mobility to create more favorable channel conditions for multi-UE communications.

In this paper, we propose a novel integrated sensing and communications (ISAC) scheme to perform bridge micro-deformation monitoring (BMDM) in complex environments. We first provide an excitation-bridge coupling model to represent the micro-deformation process of the bridge. Next, we design a novel frame structure for BMDM applications, and construct the OFDM echo channel model for basic scene of BMDM, including micro-deformation, dynamic objects, and static environment. Then, we develop a phasor statistical analysis method based on average cancellation algorithm to suppress the interference of dynamic objects, as well as a circle fitting method based on least squares algorithm to remove the interference of static environment near the monitoring area. Furthermore, we extract the micro-deformation feature vector from the OFDM echo signals after inverse discrete fourier transform (IDFT), and derive vertical micro-deformation value with the time-frequency phase resources. Simulation results demonstrate the effectiveness of the proposed BMDM scheme and its robustness against both dynamic interferences and static interferences.

Reconfigurable distributed antenna and reflecting surface (RDARS) has been recently proposed as a promising technology. This architecture enables each element to perform flexibly either in the reflection mode, like the traditional passive reconfigurable intelligent surface (RIS), or in the connection mode, akin to the distributed antenna system (DAS). This dual capability allows RDARS to harness both reflection gain and distribution gain. In this paper, we investigate a dynamic RDARS-aided multiple-input multiple-output communication system, where the optimal configuration of the elements operating in connection mode can provide additional selection gain. Considering the theoretical and practical significances, we address the achievable rate maximization problem by jointly optimizing the mode selection, transmit power allocation and passive beamforming under both perfect and imperfect channel state information (CSI) cases. Due to the involvement of the mode selection design of RDARS, the problem is more challenging than those of the traditional RIS-aided systems with fixed reflection operation. For perfect CSI case, by investigating the inherent properties of the objective function, we propose a greedy-based alternating optimization (AO) algorithm with low-complexity and then extend the proposed algorithm to the general multi-user multi-RDARS scenario. Additionally, we find interesting insights about the mode selection of RDARS in a special scenario with a single-antenna user. The result shows that the RDARS elements leading to the largest distribution gain should be selected to operate in connection mode for the rate maximization. For imperfect CSI case, we develop an efficient alternative direction method of multipliers-based AO algorithm. Numerical results show that RDARS-assisted system outperforms the passive-RIS assisted system and DAS under both perfect and imperfect CSI scenarios with promising reflection, distribution and selection gains.

Distributed antenna system (DAS) has been extensively applied in millimeter-wave (mmWave) communications due to its geographically dispersed and cooperating antennas. Incorporating reconfigurable intelligent surfaces (RISs) into DAS is a promising and effective approach to reducing hardware costs and energy consumption while simultaneously maintaining the benefits of distributed gains. Recently, an innovative architecture named reconfigurable distributed antenna and reflecting surface (RDARS) has attracted significant attention as each element can be flexibly switched between the connection and the reflection modes. This dynamic mode switching offers substantial gains by providing an additional degree of freedom (DoF) in system design. In this paper, we investigate the weighted sum rate (WSR) optimization problem in the RDARS-aided downlink mmWave multi-user system and propose a penalty item-based weighted minimum mean square error (PWM) algorithm to jointly optimize the passive phase coefficients, the mode switching, and the active beamforming for the base station and the RDARS elements in the connection mode. Numerical results demonstrate the superiority of the RDARS structure in improving WSR and verify the effectiveness of the proposed PWM algorithm.

Reconfigurable distributed antennas and reflecting surface (RDARS) has been recently proposed as a promising technology. This architecture enables each element to operate either in the reflection mode, like the conventional passive reconfigurable intelligent surface (RIS), or in the connection mode, akin to the distributed antenna system (DAS). This dual capability allows RDARS to harness both reflection gain and distribution gain. In this paper, to further unleash the potential of the RDARSaided multiple-input multiple-output (MIMO) communication system in terms of additional selection gain, we formulate an achievable rate maximization problem by jointly designing the mode selection matrix, the power allocation matrix and the reflection coefficient matrix. Due to the mode selection design of RDARS, this problem is more challenging than those of the conventional RIS-aided systems with fixed elements locations. To tackle it, by investigating the beneficial properties of the objective function, we propose a greedy-based alternating optimization (AO) algorithm with low-complexity. Numerical results clearly illustrate the reflection gain, distribution gain and selection gain of RDARS and show that RDARS-assisted system can achieve superior performance than the passive-RIS assisted system and DAS.

In order to fully explore the potential of intelligent reflecting surfaces (IRSs) in millimeter-wave (mmWave) communication systems and maximize system sum-rate performance within a user-centric framework, this paper investigates the joint optimization problem of user multiple association, transmit beamforming, and cooperative IRS passive beamforming. Meanwhile, the impact of IRS location on user association is also studied. Due to the deep coupling of multiple variables, the modeled problem is a complex non-convex optimization problem. To address it, an efficient alternating iterative optimization algorithm based on the Lagrangian dual decomposition and fractional programming techniques is proposed. Simulation results show that compared with traditional methods, the proposed algorithm significantly improves the system sum rate, validating its effectiveness.

This article presents a novel integrated sensing and communication (ISAC) framework that leverages recent advancements in reconfigurable distributed antennas and reflecting surfaces (RDARS). RDARS is a programmable structure composed of numerous elements, each of which can be flexibly configured to operate in either reflection mode, resembling a passive reconfigurable intelligent surface (RIS), or connected mode, functioning as a remote transmit or receive antenna. Our RDARS-aided ISAC framework effectively mitigates the adverse effects of multiplicative fading compared to passive RIS-aided counterparts and reduces costs and energy consumption relative to active RIS-aided systems. Within this framework, we address a radar output signal-to-noise ratio (SNR) maximization problem by jointly optimizing the active transmit beamforming matrix, the reflection and mode selection matrices of RDARS, and the receive filter, while ensuring communication requirements are met. To tackle the inherent nonconvexity and mixed-integer optimization challenges, we propose an efficient penalty-based iterative algorithm with guaranteed convergence based on the majorization-minimization (MM) framework. Additionally, we present some interesting insights about the mode selection of RDARS by considering a RDARS-aided sensing system. Numerical results demonstrate the superior performance of our framework compared to existing structures, attributed to the distribution, reflection, and selection gains provided by the dynamically configured RDARS.

The mixed Analog-to-Digital Converter (ADC) architecture is considered a promising solution in balancing the trade-off between high-resolution and low-resolution quantization over the hardware costs, power consumption, and transmission demands in massive multiple-input multiple-output (MIMO) systems. Meanwhile, the Direction of Arrival (DOA) estimation is a prerequisite for accurate beam processing in MIMO systems. Therefore, evaluating the DOA estimation performance in linear array architectures of mixed-ADC based MIMO systems is crucial. However, local bounds, such as the widely used Cramer-Rao Bound (CRB), only offer rigorous performance analysis of the estimator in the high signal-to-noise ratio (SNR) regime. In this paper, we derive a globally effective and closed-form Ziv-Zakai Bound (ZZB) to assess the DOA estimation performance of mixed-resolution quantization structures. We have also provided the CRB for DOA estimation with mixed-resolution quantization and included the classical MUSIC algorithm as a comparison. Additionally, we have analyzed the impact of different prior information, the numbers of snapshots and sensors, quantization bits and parameter settings on the ZZB. Simulation results show that the ZZB provides globally effective bounds under Gaussian and uniform distributions. In particular, in the low SNRs region, the ZZB offers a tighter and more effective bound than CRB and BCRB.

Precoding in massive multiple-input-multiple-output (mMIMO) systems relies on accurate estimation of downlink channel state information (CSI). However, when the number of antennas is large, obtaining CSI data incurs significant pilot and feedback overhead. In this paper, we propose a transformer based precoding network (TPN) that infers the future precoding matrices by exploring the time-frequency characteristic of historical wireless channels. Next, we design a suitable precoding matrix switching scheme based on channel correlation to further reduce the pilot overhead. Moreover, we leverage the network pruning technique to reduce the computational complexity of the proposed TPN. Simulations demonstrate that the sum-rate can be improved by 15% compared with the traditional zero-order holding method.

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)$\rightarrow $HIRS$\rightarrow $user (BHU); 2) BS$\rightarrow $AIRS$\rightarrow $PIRS$\rightarrow $user (BAPU); 3) BS$\rightarrow $PIRS$\rightarrow $AIRS$\rightarrow $user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

Precise positioning is becoming increasingly crucial across various applications, including rescue operations, intelligent transportation, logistics, and environmental monitoring. However, previous research often assumes that wireless sensor networks are perfectly synchronized or have unlimited communication resources. These assumptions frequently do not align with real-world scenarios, particularly in resource-constrained networks with stringent power and communication limits. In this paper, we propose a target localization and time synchronization algorithm based on the quantization of time-of-arrival (TOA) measurements. This approach accounts for both the quantization process and clock offsets caused by asynchronous clocks between sensor nodes and the target source. To tackle the problem, a variational Bayesian learning method is proposed to estimate the target source location and clock offsets jointly. This algorithm aims to find a feasible variational distribution that approximates the true posterior distribution, thereby conducting the Bayesian estimation over the approximate distributions and obtaining the accurate locations and clock offsets. Additionally, we derive the quantized Bayesian Cramr-Rao Bound (QBCRB) for quantized TOA measurements to evaluate localization and time synchronization performance. Simulation and experimental results demonstrate that the proposed method provides effective estimations and outperforms other comparison algorithms across various scenarios.

Leveraging cameras and LiDARs has been proved as an effective way to achieve beam management without training overhead for millimeter wave (mmWave) communications. However, existing methods place sensors at base station (BS) and/or mobile station (MS), which may induce privacy concerns and augment communications system expenses. In this paper, we propose a novel third-party camera aided mmWave beam management framework and self-build an RFSoC prototype for validation. Specifically, we design third-party camera aided beam alignment and blockage prediction algorithms that could work with random third-party perspective. Then, we leverage the proposed beam alignment to design initial access and beam recovery, and utilize the proposed blockage prediction to realize failure prediction. Hand-off from mmWave to sub-6G communications is conducted once the blockage is predicted. To validate the proposed framework, we develop an RFSoC prototype from scratch independently. The real-world real-time experimental results show that the beam alignment achieves over 98% in top-5 accuracy and reduces the time consumption to below$1/50$of that incurred by exhaustive beam sweeping. Meanwhile, the prototype maintains 410 MHz dynamic mmWave communications, and can seamlessly switch to sub-6G communications before a mmWave blockage happens.

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

In this paper, we propose an environment sensing-aided beam prediction model for smart factory that can be transferred from given environments to a new environment. In particular, we first design a pre-training model that predicts the optimal beam by sensing the present environmental information. When encountering a new environment, it generally requires collecting a large amount of new training data to retrain the model, whose cost severely impedes the application of the designed pre-training model. Therefore, we next design a transfer learning strategy that fine-tunes the pre-trained model by limited labeled data of the new environment. Simulation results show that when the pre-trained model is fine-tuned by 30% of labeled data from the new environment, the Top-10 beam prediction accuracy reaches 94%. Moreover, compared with the way to completely re-training the prediction model, the amount of training data and the time cost of the proposed transfer learning strategy reduce 70% and 75% respectively.

Artificial intelligence (AI) has emerged as a promising tool in channel state information (CSI) feedback tasks. Although current research primarily focuses on improving feedback accuracy through innovative AI approaches, the reliability of these systems in real-world scenarios often goes overlooked. Specifically, a closer examination of the feedback accuracy of individual CSI samples reveals significant variations, underscoring the imperative need for performance monitoring of AI-based CSI feedback. Building upon this observation, we introduce a pragmatic framework for AI-based CSI feedback. This process involves assessing feedback accuracy (i.e., conducting performance monitoring) on the user side before transmitting the CSI codeword. In particular, this method utilizes a lightweight proxy decoder, trained via knowledge distillation, to emulate the mapping function of the original decoder at the base station. The goal is to generate, at the user end, CSI identical to that produced at the base station by the original, more powerful decoder, thus enable precise prediction of feedback accuracy. Simulation results demonstrate that our proposed performance monitoring method can precisely predict feedback accuracy with low complexity and accurately detect low-quality feedback samples with a detection rate of nearly 95%, ensuring reliable transmission.

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termedflexible reflector(FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multi-FR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement.

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

Ultra-reliable low-latency communication is the key technology for smart factories and autonomous vehicles. However, traditional beam training approaches in millimeter-wave communications generally cause significant latency and communication overhead, especially in the case of multi-user communications. To tackle this problem, we propose a novel Vision-aided Multi-user Beam Tracking (VA-MUBT) framework for mmWave massive MIMO system, which leverages deep learning based visual object detection and multiple objects tracking algorithm to enable fast beam tracking of multi-user. In addition, a prototype is constructed to evaluate the proposed VA-MUBT framework and the experimental results based on this prototype show that the accuracy of 3-time beam search can reach near 90% with only 8% overhead of the exhaustive beam search method. Hence, the proposed VA-MUBT demonstrates the superiority in achieving fast multi-user beam tracking and significantly reducing the communication overhead.

In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) to support the large-capacity and reliable transmission of the high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communication links between RSU and different vehicles are blocked or connected. The 3D detection technique is firstly used to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method. The joint statistical distribution of the residual transmission distance and the residual multi-hop latency is used to optimize the total multi-hop latency. Simulation results show that the proposed vision based link state identification method can significantly outperform the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

Smart factory is a new intelligent platform requiring high throughput and millimeter wave (mmWave) technology has become an enabler for high speed communications in Industry 4.0. However, the sensitivity of mmWave signals to blockage poses serious challenges to the reliability of wireless networks in these frequency ranges. In this paper, we propose a vision-aided reference signal receiving power prediction (RSRP) framework for smart factory to avoid communications interruption caused by unexpected blockage. In particular, we design a feature extraction method to obtain communications-related features in environmental images. Then, we construct a joint image-channel dataset based on Blender and Wireless Insite software. Simulations show that the root mean square error (RMSE) of RSRP prediction 400 ms ahead reaches 2.88 dB. RSRP prediction can assist base station (BS) handover to avoid communications interruption. Hence, the proposed study provides a promising direction for enabling ultra-reliable communications under mmWave and even Terahertz bands in smart factory of Industry 4.0.

In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) based integrated sensing and communications (ISAC) systems. Specifically, we design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry targets information in different directions, based on which the targets' angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different OFDM symbols to estimate the distance and velocity of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed as You Only Listen Once (YOLO). Compared with the traditional ISAC method that requires time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results confirm the effectiveness of the proposed scheme.

This paper analyzes the block error rate (BLER) of hybrid automatic repeat request with incremental redundancy (HARQ-IR) assisted short packet communications. The small packet size leads to the occurrence of the time correlation among HARQ rounds. To accurately capture the effect of time correlation, a general correlated Nakagami-m fading channel model is first developed. However, the channel correlation, multiple transmissions, and finite blocklength information theory extremely challenge the analysis of BLER. To confront this, the average BLER is derived in a compact form by capitalizing on linearization approximation and conditional Laplace transform. To reveal more insights, the asymptotic BLER in high signal-to-noise ratio is obtained in a simple form. It is disclosed that full time diversity can be achieved by the proposed HARQ-IR-aided scheme. This justifies the feasibility of using retransmissions to offer reliable short packet communications. The numerical results finally corroborate the validity of our analysis.

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. We design multiple communications beams that can communicate with users while one rotating sensing beam can scan entire space, and then we propose the supporting beam-forming design and power allocation strategies for such design. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel that includes both static environment and dynamic targets. When base station receives echo signals, we first provide a practical clutter filtering method to filter out static environmental clutter. Then dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

This article examines the application of hybrid automatic repeat request with incremental redundancy (HARQ-IR) to reliable mission-critical Internet of Things (IoT) communications, which frequently use short packets to meet low latency of mission. We first analyze the average block error rate (BLER) of HARQ-IR-aided short packet communications. The finite-blocklength information theory and the correlated decoding events preclude the analysis of BLER. To overcome the issue, the recursive formulation of the average BLER motivates us to calculate its value through trapezoidal approximation and Gauss-Laguerre quadrature. Besides, dynamic programming is applied to implement Gauss-Laguerre quadrature to avoid redundant calculations. Moreover, the asymptotic analysis is performed to derive a simple expression for the asymptotic average BLER at high-signal-to-noise ratio (SNR). Then, we study the maximization of long-term average throughput (LTAT) via power allocation meanwhile ensuring power and BLER constraints. To tackle the fractional and nonconvex problem, the asymptotic BLER is employed to convert the original problem into a convex one through geometric programming (GP). Unfortunately, since there is a large approximation error at low SNR, the GP-based solution underestimates the LTAT performance in the circumstance. Alternatively, we develop a deep reinforcement learning (DRL)-based framework to learn the optimal power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process problem, which is solved by integrating deep deterministic policy gradient(DDPG) and subgradient method. The numerical results demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden.

Dual-function radar-communication (DFRC) can alleviate spectrum congestion and competition with the spectrum-sharing architecture for next-generation wireless networks. In this article, we consider the problem of robust beamforming in millimeter wave (mmWave) DFRC systems. Unlike most existing works which assume that the angle-of-arrival (AoA)/angle-of-departure (AoD) or channel state information (CSI) is perfectly known, we consider the case of imperfect CSI resulting from the movement of users/targets or the beam misalignment errors. Our object is to maximize the achievable ergodic rate for communication under the constrained worst-case sensing signal-clutter-noise ratio (SCNR) for radar. By integrating the two-phase-shifter structure into our proposed robust hybrid beamforming architecture, it substantially improves the system performance and increases the design flexibility at the cost of doubling the number of phase shifters. With the two-phase-shifter structure, our proposed robust multibeam technology coherently combines sensing subbeams and communication subbeams with a widebeam radiation pattern for alleviating the effect of AoA/AoD uncertainty. Our proposed robust multibeam beamformer can shape the transmit waveform flexibly with very low complexity, which is amiable for practical implementation. Theoretical and numerical results validate the effectiveness and robustness of our proposed method in mmWave DFRC systems.

Smart factory is a new digital and intelligent platform requiring high throughput and ultra-reliable low-latency communications (URLLC). Industrial communications at sub-6 GHz faces spectrum congestion and bandwidth limitations, which seriously jeopardize the high data rate requirement of smart factory. Recently, millimeter wave (mmWave) and Terahertz technologies have become enablers for high speed communications and intelligent manufacturing in Industry 4.0 and beyond. However, the sensitivity of mmWave signals to blockage and the overhead of large-scale antenna beam sweeping pose serious challenges to the reliability and the latency of wireless networks in these frequency ranges. In this paper, we propose a vision-aided URLLC framework for smart factory that does not incur any overhead from channel training and beam sweeping. In particular, we design a feature extraction method to obtain communications-related features in environmental images for blockage prediction, reference signal receiving power (RSRP) prediction, and beam selection. Then, we construct a joint image-channel dataset covering images, annotations, blockage, and wireless channels based on Blender and Wireless Insite software. Simulations show that the accuracy of blockage prediction 400 ms ahead reaches 99.9%, the root mean square error (RMSE) of RSRP prediction 400 ms ahead reaches 2.78 dB, and the Top-5 accuracy of beam selection reaches 91.8%. Blockage and RSRP prediction can assist base station (BS) handover to avoid communications interruption, while beam selection can eliminate the overhead of channel training and beam sweeping. Hence, the proposed study provides a promising direction for enabling URLLC under mmWave and even Terahertz bands in smart factory of Industry 4.0.

Distributed Antenna Systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of fully digital or hybrid active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure coverage and transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of “Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)” is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform theconnected modeas remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform thereflection mode. As a result, RDARS encompasses both DAS and RIS as special cases, offering flexible control over the trade-off betweendistribution gainandreflection gainto enhance performance. To unveil the potential of such architecture, the ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis proves that the RDARS can achieve a higher achievable rate than both DAS and fully passive RIS with the passive beamforming gain provided by elements actingreflection modewhile combating the “multiplicative fading” suffered by RISs through theconnected modeperformed at the RDARS. Simulation results also demonstrate the superiority of the RDARS architecture over DAS and passive RIS-aided systems and its flexible trade-off between performance and cost. To further validate the feasibility and effectiveness, an RDARS prototype with 256 elements is built for real experiments. Experimental results show that the RDARS-aided system with only one element operating inconnected modecan achieve an additional 21% and 170% throughput improvement over DAS and RIS-aided systems, respectively.

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cramér-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.

Compared with traditional half-duplex wireless systems, the application of emerging full-duplex (FD) technology can potentially double the system capacity theoretically. However, conventional techniques for suppressing self-interference (SI) adopted in FD systems require exceedingly high power consumption and expensive hardware. In this paper, we consider employing an intelligent reflecting surface (IRS) in the proximity of an FD base station (BS) to mitigate SI for simultaneously receiving data from uplink users and transmitting information to downlink users. The objective considered is to maximize the system weighted sum-rate by jointly optimizing the IRS phase shifts, the BS transmit beamformers, and the transmit power of the uplink users. To visualize the role of the IRS in SI cancellation, we first study a simple scenario with one downlink user and one uplink user. To address the formulated non-convex problem, a low-complexity algorithm based on successive convex approximation is proposed. For the more general case considering multiple downlink and uplink users, an efficient alternating optimization algorithm based on element-wise optimization is proposed. Numerical results demonstrate that the FD system with the proposed schemes can achieve a larger gain over the half-duplex system, and the IRS is able to achieve a balance between suppressing SI and providing beamforming gain.

To harness the benefits of both reconfigurable intelligent surface (RIS) and dynamic metasurface antenna (DMA), we consider the hybrid RIS and DMA assisted multiple-input multiple-output (MIMO) multiple-access channel (MAC) over spatially correlated Rician fading, in which multiple multi-antenna users send the transmitted signals to the DMA-based base station (BS) with the assistance of a RIS. The objective is to maximize the achievable ergodic sum-rate by jointly designing the transmit covariance matrix of users, the phase shift matrix of RIS, and the DMA weight matrix at BS only with statistical channel state information. By capitalizing on large random matrix theory, a closed-form asymptotic ergodic sum-rate is first obtained. Then, we propose a modified water-filling algorithm to design the optimal transmit covariance matrix under the power consumption and specific absorption rate constraints. Next, we design the phase shift matrix of RIS via the projected gradient ascent algorithm, subject to the non-convex unit-modular constraint. To find the constrained DMA weight matrix, we further resort to the optimal solution of the unconstrained DMA problem and adopt the alternating optimization method. The proposed algorithm is numerically shown to improve the sum-rate compared to the baseline schemes, verifying the effectiveness of the proposed schemes.

Carrier sense multiple access (CSMA) provides a feasible way to support machine type communications (MTC). However, in CSMA how to guarantee the diverse delay QoS efficiently is unsolved because the relationship between the back-off factor and delay QoS is unexploited. In this paper, we propose a new CSMA-type scheme named QoS-aware Local-Information-driven Multiple Access (LIMA) to handle this problem. The LIMA is formulated as a throughput maximization problem subject to the statistical delay QoS including the delay bound and delay bound violation probability. The diverse statistical delay QoS constraints are decomposed constraint into a series of the parameter adjusting period (PAP)-progressive QoS constraints in the time scale. Based on the effective capacity, the relationship between the back-off factor (defined as the average of back-off duration) and statistical delay QoS is studied. Then, each PAP-progressive QoS constraint is explicitly expressed in a simple form. Finally, a distributed algorithm is developed to find the optimal back-off factors based on local information. The global optimality of the proposed solution is theoretically proved. The salient feature of LIMA is that it can satisfy the diverse delay QoS requirements in a distributed way without signaling overhead for traffic information of other devises and scheduling information from the coordinator/access point (AP).

A variable-length cross-packet hybrid automatic repeat request (VL-XP-HARQ) is proposed to boost the spectral efficiency (SE) and the energy efficiency (EE) of communications. The SE is firstly derived in terms of the outage probabilities, with which the SE is proved to be upper bounded by the ergodic capacity (EC). Moreover, to facilitate the maximization of the SE, the asymptotic outage probability is obtained at high signal-to-noise ratio (SNR), with which the SE is maximized by properly choosing the number of new information bits while guaranteeing outage requirement. By applying Dinkelbach’s transform, the fractional objective function is transformed into a subtraction form, which can be decomposed into multiple sub-problems through alternating optimization. By noticing that the asymptotic outage probability is a convex function, each sub-problem can be easily relaxed to a convex problem by adopting successive convex approximation (SCA). Besides, the EE of VL-XP-HARQ is also investigated. An upper bound of the EE is found and proved to be attainable. Furthermore, by aiming at maximizing the EE via power allocation while confining outage within a certain constraint, the methods to the maximization of SE are invoked to solve the similar fractional problem. Finally, numerical results are presented for verification.

With the emerging environment-aware applications, ubiquitous sensing is expected to play a key role in future networks. In this paper, we study a 3-dimensional (3D) multi-target localization system where multiple intelligent reflecting surfaces (IRSs) are applied to create virtual line-of-sight (LoS) links that bypass the base station (BS) and targets. To fully unveil the fundamental limit of IRS for sensing, we first study a single-target-single-IRS case and propose a novel two-stage localization protocol by controlling the on/off state of IRS. To be specific, in the IRS-off stage, we derive the Cramér-Rao bound (CRB) of the azimuth/elevation direction-of-arrival (DoA) of the BS-target link and design a DoA estimator based on the MUSIC algorithm. In the IRS-on stage, the CRB of the azimuth/elevation DoA of the IRS-target link is derived and a simple DoA estimator based on the on-grid IRS beam scanning method is proposed. Particularly, the impact of echo signals reflected by IRS from different paths on sensing performance is analyzed and we show that only the signal passing through the BS-IRS-target link is required while that of the BS-target link can be neglected provided that the number of BS antennas is sufficiently large and the dedicated sensing beam at the BS is aligned with the departure transmit array response from the BS to the IRS. Moreover, we prove that the single-beam of the IRS is not capable of sensing, but it can be achieved with multi-beam. Based on the two obtained DoAs, the 3D single-target location is constructed. We then extend to the multi-target-multi-IRS case and propose an IRS-adaptive sensing protocol by controlling the on/off state of multiple IRSs, and a multi-target localization algorithm is developed. Simulation results demonstrate the effectiveness of our scheme and show that sub-meter-level positioning accuracy can be achieved.

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparse angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms.

Using communications signals for dynamic target sensing is an important component of integrated sensing and communications (ISAC). In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) Terahertz band communications systems. Specifically, we construct a wideband channel model of the echo signals, and design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry target information in different directions, based on which the targets’ angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different orthogonal frequency division multiplexing (OFDM) symbols to estimate the distances and velocities of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed asYou Only Listen Once(YOLO). Compared with the traditional ISAC methods that require time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. To implement communications function and sensing function simultaneously, we design multiple communications beams that can communicate with the users as well as one sensing beam that can rotate and scan the entire space. To minimize the interference of sensing beam on existing communications systems, we divide the service area intosensing beam for sensing (S4S) sectorandcommunications beam for sensing (C4S) sector, and provide beamforming design and power allocation optimization strategies for each type sector. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel model that includes both static environment and dynamic targets. When base station receives the echo signals, it first filters out the interference from static environmental clutter and extracts the effective dynamic target echoes. Then a complete and practical dynamic target sensing scheme is designed to detect the presence of dynamic targets and to estimate their angles, distances, and velocities. In particular, dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results demonstrate the effectiveness of the proposed scheme and its superiority over the existing methods that ignore environmental clutter.

In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive multiple-input multiple-output (MIMO) system with imperfect channel state information (CSI) and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. The rate expression is general and covers that of the distributed antenna systems (DAS) and reconfigurable intelligent surface (RIS) aided systems as special cases. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems.

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution for green communications in 6G. However, its further extensive use has been greatly limited due to its fully passive characteristics. Considering the appealing distribution gains of distributed antenna systems (DAS), a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two special cases and maintains the advantages of distributed antennas while reducing the hardware cost by replacing some active antennas with low-cost passive reflecting surfaces. In this paper, we present a RDARS-aided uplink multi-user communication system and investigate the system transmission reliability with the newly proposed architecture. Specifically, in addition to the distribution gain and the reflection gain provided by the connection and reflection modes, respectively, we also consider the dynamic mode switching of each element which introduces an additional degree of freedom (DoF) and thus results in a selection gain. As such, we aim to minimize the total sum mean-square-error (MSE) of all data streams by jointly optimizing the receive beamforming matrix, the reflection phase shifts and the channel-aware placement of elements in the connection mode. To tackle this nonconvex problem with intractable binary and cardinality constraints, we propose an inexact block coordinate descent (BCD) based penalty dual decomposition (PDD) algorithm with the guaranteed convergence. Since the PDD algorithm usually suffers from high computational complexity, a low-complexity greedy-search-based alternating optimization (AO) algorithm is developed to yield a semi-closed-form solution with acceptable performance. Numerical results demonstrate the superiority of the proposed architecture compared to the conventional fully passive RIS or DAS. Furthermore, some insights about the practical implementation of RDARS are provided.

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method.

Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers and leads to extremely high CSI feedback overhead. Deep learning-based methods have emerged for compressing CSI but these methods generally require substantial collected samples and thus pose practical challenges. Moreover, existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. To address these issues, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. An incorporation-extrapolation scheme for eigenvector-based CSI feedback is proposed to reduce the feedback overhead. Then, to alleviate the necessity of extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation (KDDA) method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Experimental results based on the DeepMIMO dataset demonstrate that the proposed IEFSF significantly reduces CSI feedback overhead by 64 times compared with existing methods while maintaining higher feedback accuracy using only several hundred collected samples.

In this paper, we propose a novel framework for optical flow estimation that achieves a good balance between performance and efficiency. Our approach involves disentangling global motion learning from local flow estimation, treating global matching and local refinement as separate stages. We offer two key insights: First, the multi-scale 4D cost-volume based recurrent flow decoder is computationally expensive and unnecessary for handling small displacement. With the separation, we can utilize lightweight methods for both parts and maintain similar performance. Second, a dense and robust global matching is essential for both flow initialization as well as stable and fast convergence for the refinement stage. Towards this end, we introduce EMD-Flow, a framework that explicitly separates global motion estimation from the recurrent refinement stage. We propose two novel modules: Multi-scale Motion Aggregation (MMA) and Confidence-induced Flow Propagation (CFP). These modules leverage cross-scale matching prior and self-contained confidence maps to handle the ambiguities of dense matching in a global manner, generating a dense initial flow. Additionally, a lightweight decoding module is followed to handle small displacements, resulting in an efficient yet robust flow estimation framework. We further conduct comprehensive experiments on standard optical flow benchmarks with the proposed framework, and the experimental results demonstrate its superior balance between performance and runtime. Code is available at https://github.com/gddcx/EMD-Flow.

To alleviate the blockage effect and explore the potential of reconfigurable intelligent surface (RIS) assisted communication, we investigate the cooperative double-RIS assisted multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. To improve system performance, we jointly optimize the transmit beamforming matrix at the base station and the phase shift matrices at RISs to maximize the system sum rate, which is an intractable non-convex problem. To solve the problem, we propose an efficient alternating optimization algorithm based on the techniques of weighted minimum mean square error (WMMSE), Lagrange multiplier and majorization-minimization (MM). Simulation results validate the effectiveness of the proposed algorithm, as well as the superiority of double-RIS in improving system performance.

Signals over millimeter wave (mmWave) bands suffer from severe path loss and are easily blocked by obstacles, which greatly degrades the link quality and reliability of mmWave communications. One of the promising ways to overcome this challenge is multi-connectivity, which enables a user to associate with multiple small cells simultaneously. In this paper, we investigate the association problem of a given user to multiple mmWave base stations (mBSs), which we termed user-centric association. In particular, we consider an intelligent reflecting surface (IRS)-aided ultra-dense mmWave communication system, in which multiple distributed IRSs are deployed to expand the coverage of mmWave signals to blind spots. The user-centric association problem is formulated to maximize the user achievable sum-rate with respect to the mBS-user association, auxiliary IRS selection, and power allocation in a combinatorial manner. The original optimization problem is a mixed-integer nonlinear programming problem, which is NP-hard. To solve it, we first relax the formulated problem into a continuous one, then decouple it into three subproblems by utilizing decomposition technique, and finally propose an alternating iteration based algorithm to obtain the optimal solution. Numerical simulations show that the user sum-rate can be greatly improved by the joint optimization scheme.

Mission critical communications require ultra-high reliability guarantees. It is a natural idea to combine massive multiple-input multiple-output (MIMO) and repetition transmission to further improve the reliability. In this paper, we combine the irregular repetition scheme with the massive MIMO with grant-free access. By introducing random transmission patterns for replicas, the collision problem of grant-free access is mitigated. Considering finite block-length coding, as well as sporadic and periodic arrivals of mission critical traffic, the reliability is thoroughly analyzed and derived in closed form. The theoretical analysis is finally verified through computer simulations. It is observed that the reliability of periodic traffic is superior to the sporadic one in large MIMO systems.

In view of the nonideality of communication links in the Internet of Things (IoT) originating from transceiver hardware impairments, in this article, we introduce a general framework for hardware impairments-aware multiantenna transceiver design, which considers different availabilities of CSI at the transmitter (CSIT) and the receiver (CSIR). The well-known Kronecker model is applied to characterize stochastic channel state information (CSI) errors. For each case, we aim to minimize the (average) total mean square error (MSE) of all data streams subject to the practical per-antenna power constraints. To address the nonconvexity of the formulated problem, we propose an efficient majorization–minimization (MM)-based iterative algorithm to transform the original problem into a series of convex subproblems with semiclosed-form optimal solutions. For low-complexity implementation, we also develop an alternative scheme for directly finding a high-quality suboptimal solution by considering both worst case hardware impairments and worst case CSI errors. In particular, since an explicit expression of the average total MSE for the perfect CSIR and imperfect CSIT case is hard to derive, we instead optimize its effective upper and lower bounds. The prospective applications of our work in the two currently popular multiple-input–multiple-output (MIMO) IoT scenarios are then discussed. Furthermore, we fundamentally reveal the MSE floor effect caused by both hardware distortion and CSI imperfection in the high-SNR regime. Numerical results illustrate the excellent average total MSE and average bit error rate (BER) performance of our proposed algorithms over the adopted benchmark schemes.

In wireless sensor networks, unmanned aerial vehicles (UAVs) can be employed to collect data from sensor nodes (SNs) efficiently. In this article, we consider a dual-UAV-enabled (long-distance) data collection system, where one UAV is dispatched to collect data from distributed SNs, while the other UAV is employed to relay data from the data-collection UAV to a fusion center (FC) that locates far from the SNs. To shorten the time duration for the FC to collect all data, we propose to minimize the completion time of the data collection task by jointly optimizing the transmit power and bandwidth of all SNs and the UAVs, as well as the three-dimensional trajectories of the two UAVs. Instead of assuming the simplified line-of-sight UAV-ground channel model as in most existing works, we model the channels between the UAVs and SNs as well as that between the UAVs and FC by applying the practically more accurate elevation-angle-dependent Rician fading channel model. The resulting optimization problem is nonconvex and thus is difficult to solve in general. Nevertheless, we propose an algorithm to solve it efficiently by using the techniques of block coordinate descent, slack variable substitution, and successive convex approximation. Simulation results show that our proposed algorithm can achieve higher communication efficiency than other benchmark schemes and greatly reduce the task completion time for data collection.

An off-grid direction-of-arrival (DOA) estimation method based on block sparse representation is proposed to localize the strictly noncircular (NC) sources utilizing an extended transformed nested array (ETNA). This novel off-grid DOA estimation algorithm effectively promotes spatial distribution information mining. Furthermore, it is conducive to providing stable signal recovery, which refines the DOA estimation precision with interpolation over a coarse grid. We then combine the above algorithm with the designed ETNA to improve the detection performance. The ETNA is an optimal displacement on the existing TNA, which enlarges the degree of freedom (DOF) and lengthens the maximum contiguous segment from the derived virtual array. Simulation results demonstrate its superiority in estimation performance and DOF.

Integrated sensing and communications (ISAC) has attracted tremendous attention for the future 6G wireless communications systems. To improve the transmission rates and sensing accuracy, massive multi-input multi-output (MIMO) technique is leveraged with large transmission bandwidth in millimeter wave (mmWave)/terahertz (THz) band. However, the growing size of antenna array and transmission bandwidth results in the beam-squint effect, which hampers the performance of communications. Moreover, the time overhead of the traditional sensing algorithm is prohibitively high for practical systems. In this paper, instead of alleviating the beam-squint effect, we take advantage of joint beam-squint and beam-split effect and propose a novel integrated sensing and communications scheme for massive MIMO system. Specifically, with the beam-squint effect, the base station (BS) utilizes the true-time-delay (TTD) lines to steer the beams of different OFDM subcarriers towards distributive directions simultaneously. Different users then feedback their respective subcarrier frequency with the maximum array gain to BS, based on which BS could calculate the directions of the users. Moreover, by selecting sub-array with the inter-antenna spacing larger than half-wavelength, the beam-split effect can be introduced and exploited to expand the sensing range. The proposed sensing method operates over frequency-domain, and the intended sensing range is covered by all the subcarriers simultaneously, which significantly reduces the time overhead compared to the conventional sensing scheme. Simulation results have demonstrated the effectiveness as well as the superior performance of the proposed ISAC scheme.

In this paper, reconfigurable intelligent surface (RIS) and hybrid automatic repeat request with incremental redundancy (HARQ-IR) are amalgamated to lower power expenditure, shorten latency and strengthen reliability of the internet of things (IoT) communications. By considering Rician fading channels and multiple RISs, the outage probability of single-input single-output (SISO) HARQ-IR-RIS aided IoT networks is derived in closed-form, with which the asymptotic outage analysis is carried out. The asymptotic results are further extended to single-input multiple-output (SIMO)/multiple-input multiple-output (MIMO) HARQ-IR-RIS aided IoT networks by using random matrix theory. Thereafter, the asymptotic expressions are invoked to reduce the design complexity of the phase shifts, transmit powers, and rate, which aims at minimizing the age of information (AoI) while ensuring the power and outage constraints. Particularly, the optimal phase shifts are firstly determined. The alternating optimization technique is then applied to solve the transmit rate and powers, which are iteratively updated based on majorization-minimization (MM) principle and geometric programming (GP) approximation, respectively. The numerical results consequently corroborate our theoretical analysis. More interestingly, the HARQ-IR-aided scheme with fixed power is found to provide a comparable performance as the proposed scheme with variable power especially for numerous reflecting elements at RIS.

Massive multiple-input multiple-output (MIMO) assisted grant-free access is a compelling approach to support uplink ultra-reliable low-latency communications (URLLCs). It is a natural idea to combine massive MIMO and repetition transmission to further improve the reliability. However, excessive repetitions may increase the collision probability in multiuser scenarios and thus limit the reliability improvement. In this paper, we propose a novel semi-probabilistic repetition (SPRe) scheme for the massive multiuser MIMO (MU-MIMO) systems with grant-free access. By introducing a transmission probability for the replicas, the SPRe is more flexible than the popular repetition scheme, i.e.,$K$-repetition, and covers the$K$-repetition as a special case. Considering practical MU-MIMO systems with finite block-length coding, channel estimation errors, pilot collisions and the sporadic arrivals of URLLC traffic, the reliability of the proposed SPRe is thoroughly analyzed and derived in closed form. Through further asymptotic analysis, the optimal transmission probability to maximize the reliability of MU-MIMO is derived for the high-SNR regime and low-load scenarios. Moreover, the diversity gain of the proposed SPRe is also analyzed and it is proved that the optimal SPRe is superior to the$K$-repetition in terms of reliability. The analysis and superiority of the SPRe are finally verified through computer simulations.

In this paper, we study an intelligent reflecting surface (IRS)-aided radar-communication (Radcom) system, where the IRS is leveraged to help Radcom base station (BS) transmit the joint of communication signals and radar signals for serving communication users and tracking targets simultaneously. The objective of this paper is to minimize the total transmit power at the Radcom BS by jointly optimizing the active beamformers, including communication beamformers and radar beamformers, at the Radcom BS and the phase shifts at the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) required by communication users, the minimum SINR required by the radar, and the cross-correlation pattern design. In particular, we consider two cases, namely, case I and case II, based on the presence or absence of the radar cross-correlation design and the interference introduced by the IRS on the Radcom BS. For case I where the cross-correlation design and the interference are not considered, we prove that the dedicated radar signals are not needed, which significantly reduces implementation complexity and simplifies algorithm design. Then, a penalty-based algorithm is proposed to solve the resulting non-convex optimization problem. Whereas for case II considering the cross-correlation design and the interference, we unveil that the dedicated radar signals are needed in general to enhance the system performance. Since the resulting optimization problem is more challenging to solve as compared with the case I, the semidefinite relaxation (SDR) based alternating optimization (AO) algorithm is proposed. Particularly, instead of relying on the Gaussian randomization technique to obtain an approximate solution by reconstructing rank-one solution, the tightness is achieved by our proposed reconstruction strategy. Simulation results demonstrate the effectiveness of proposed algorithms and also show the superiority of the proposed scheme over various benchmark schemes.

Driven by unmanned aerial vehicle (UAV)’s advantages of flexible observation and enhanced communication capability, it is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements. In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication (IPSAC) mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a two-layer penalty-based algorithm to efficiently solve the considered problem. To draw more important insights, the optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints in our considered UAV-enabled ISAC system. Based on this, we propose an efficient algorithm for solving the problem with location constraints. Numerical results validate the effectiveness of our proposed designs and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.

Reconfigurable intelligent surface (RIS) has been anticipated to be a novel cost-effective technology to improve the performance of future wireless systems. In this paper, we investigate a practical RIS-aided multiple-input-multiple-output (MIMO) system in the presence of transceiver hardware impairments, RIS phase noise and imperfect channel state information (CSI). Joint design of the MIMO transceiver and RIS reflection matrix to minimize the total average mean-square-error (MSE) of all data streams is particularly considered. This joint design problem is non-convex and challenging to solve due to the newly considered practical imperfections. To tackle the issue, we first analyze the total average MSE by incorporating the impacts of the above system imperfections. Then, in order to handle the tightly coupled optimization variables and non-convex NP-hard constraints, an efficient iterative algorithm based on alternating optimization (AO) framework is proposed with guaranteed convergence, where each subproblem admits a closed-form optimal solution by leveraging the majorization-minimization (MM) technique. Moreover, via exploiting the special structure of the unit-modulus constraints, we propose a modified Riemannian gradient ascent (RGA) algorithm for the discrete RIS phase shift optimization. Furthermore, the optimality of the proposed algorithm is validated under line-of-sight (LoS) channel conditions, and the irreducible MSE floor effect induced by imperfections of both hardware and CSI is also revealed in the high signal-to-noise ratio (SNR) regime. Numerical results show the superior MSE performance of our proposed algorithm over the adopted benchmark schemes, and demonstrate that increasing the number of RIS elements is not always beneficial under the above system imperfections.

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.

Massive multiple-input multiple-output (MIMO) systems have suffered from extremely high hardware complexity and cost because of the introduction of a tremendous number of antennas. Recently, one way to alleviate this is by considering the unequal uplink and downlink data transmission requirements and employing an asymmetrical transceiver. Such asymmetrical transceiver architecture, however, also brings out channel dimension inconsistency between the uplink and downlink. Thus, to well achieve the large array gain and fully exploit the potentials of asymmetrical transceiver-based massive MIMO systems, accurately recovering the full-dimensional downlink channel state information (CSI) based on the obtained small-dimensional uplink CSI is necessary. Nevertheless, the CSI at different antennas plays a different role in the CSI recovery due to the spatial correlation. Therefore, investigating appropriate antenna selection for asymmetrical transceiver-based massive MIMO systems is valuable and essential. To address this, we first formulate the antenna selection problem to minimize the mean-square recovery error of the full-dimensional downlink CSI in this paper. Then, two receive antenna selection algorithms are proposed by exploiting the low-rank property of the spatial correlation matrices under single-user scenarios. We also extend these algorithms to multi-user scenarios, and semi-closed-form optimal selection coefficients are derived. Numerical results demonstrate that, with the aid of the proposed antenna selection algorithms, the full-dimensional downlink CSI can be well recovered, which thus paves the way for asymmetrical transceiver-based massive MIMO systems to achieve their excellent downlink transmission performance with a much lower overall system hardware complexity and cost.

This paper investigates a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MIMO) system by considering only the statistical channel state information (CSI) at the base station (BS). We aim to maximize its sum-rate via the joint optimization of precoding matrix at the BS and phase shifts vector at the RIS. However, the multi-user MIMO transmissions and the spatial correlations make the optimization cumbersome. For tractability, an asymptotic sum-rate is derived under a large number of the reflecting elements. By adopting the asymptotic sum-rate as the objective function, optimal designs of the transmit precoding matrix and the phase shifts vector can be decoupled and solved individually. More specifically, a high-quality suboptimal solution of the transmit precoding matrix and phase shifts vectors can be obtained by utilizing the water-filling algorithm and the projected gradient ascent (PGA) algorithm, respectively. Comparing to the case of the instantaneous CSI assumed at the BS, the proposed algorithm based on the statistical CSI can achieve comparable performance but with much lower channel estimation overhead, information feedback overhead, and computational complexity, which is more affordable and appealing for practical applications. Moreover, the impact of spatial correlation on the asymptotic sum-rate is examined by using majorization theory.

In this article, we present a hybrid channel estimation algorithm for uniform planar array (UPA)-assisted millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems by exploiting the benefits from both the compressed sensing (CS) and the sparse Bayesian learning (SBL). Compared with existing studies, the distribution characteristics and correlations between propagation paths in the elevation (e)- and azimuth (a)-angle domains are considered to enhance the estimation performance. Specifically, we first redefine the e-angles and the a-angles to simplify the system model. Then, a novel autoregressive (AR)-Gaussian channel prior is proposed to capture both the sparsity and the clustering properties of mmWave massive MIMO IoT channels. After that, we provide a channel approximation method to overcome the channel uncertainty by exploiting the structure of the AR-Gaussian channel prior. The hybrid beamforming (HBF) architecture with limited radio-frequency (RF) chains in mmWave IoT systems is also considered. Finally, we propose a hybrid channel estimation algorithm, which consists of two stages. Based on the different distribution characteristics in different angle domains, the CS-based channel estimation is performed for e-angles on stage one, while the SBL-based channel estimation is applied for a-angles on stage two. Numerical results reveal that compared with the existing CS- and SBL-only methods, the proposed hybrid channel estimation algorithm exhibits better performance in terms of computational complexity, sparsity robustness, and estimation accuracy.

Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wireless networks. In this paper, we present a reinforcement learning (RL) based random linear network coding scheme for drone-aided cellular systems to address eavesdropping. In this scheme, the network coding policy, including the encoded packet number, the packet and power allocation, is chosen based on the measured jamming power, previous transmission performance and BS channel states. A virtual model generates simulated experiences to update Q-values besides real experiences for faster policy optimization. We also propose a deep RL version and design a hierarchical architecture to further accelerate the policy exploration and improve the anti-eavesdropping performance, in terms of the intercept probability, the latency, the outage probability and the energy consumption. We analyze the computational complexity, drone deployment, secure coverage area and the performance bound of the proposed schemes, which are verified via simulation results.

Vehicle-to-vehicle (V2V) communications under dense urban environments usually experience severe keyhole fading effect especially for multi-input multi-output (MIMO) channels, which degrades the capacity and outage performance due to the rank deficiency. To avoid these, the integration of MIMO and hybrid automatic repeat request (HARQ) is proposed to assist V2V communications in this paper. By using the methods of integral transforms, the outage probabilities are derived in closed-form for different HARQ-assisted schemes, including Type I-HARQ, HARQ with chase combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). With the results, meaningful insights are gained by conducting the asymptotic outage analysis. Specifically, it is revealed that full time diversity order can be achieved, while full spatial diversity order is unreachable as compared to MIMO-HARQ systems without keyhole effect. Moreover, we prove that the asymptotic outage probability is a monotonically increasing and convex function of the transmission rate. More importantly, although HARQ-IR performs better than HARQ-CC owing to its higher coding complexity, this advantage becomes negligible in the large-scale array regime. Finally, the numerical results are verified by Monte-Carlo simulations along with some in-depth discussions.

Intelligent reflecting surface (IRS) is an emerging cost-efficient technology to enhance communication performance by implementing a large number of passive reflecting elements with tunable phases in wireless systems. In this paper, we propose a general framework for the IRS-aided MIMO system designs under both single-user and multi-user setups, in which the diverse performance metrics including weighted mutual information and weighted MSE, and the realistic multiple weighted power constraint are taken into consideration. Leveraging the alternating optimization approach, the optimal IRS phase shifts are obtained in semi-closed forms. Specifically, based on the matrix-monotonic optimization theory, it is found that optimizing IRS phase shifts is essentially equivalent to tuning the eigenvalues and the corresponding eigenvectors of the MSE matrix. Then the proposed general framework is extended to a multi-user system by introducing a majorization-minimization (MM)-based method for IRS phase shift optimization. Simulation results show that our proposed optimal design brings significant enhancement on the chosen performance metric compared to the traditional MIMO systems without the IRS, and also significantly outperforms various benchmark designs in both single-user and multi-user systems.

Hybrid energy supply composed of batteries and radio frequency (RF) signals has been anticipated to be a prominent solution for balancing the reliability and self-sustainability of future IoT networks. The newly emerging reconfigurable intelligent surface (RIS) is also capable of greatly enhancing spectral and energy efficiencies. In this paper, by considering an asynchronous transmission protocol among all energy receivers (ERs) and assuming the perfect self-interference cancellation (SIC) at the hybrid access point (HAP), we aim to maximize sum throughput in the RIS-aided hybrid powered communication networks (HPCNs) by jointly optimizing the transmit covariance matrices of the HAP and all ERs, the RIS reflection matrix and the downlink/uplink (DL/UL) time allocation. Generally, this optimization problem is intractable to solve due to strongly coupled variables and nonconvex unit-modulus constraints. To draw more insights into this joint design, we firstly carry out feasibility analysis on this problem, and then develop a 2-block alternating optimization algorithm, which consists of the semi-closed-form solution based iterative algorithm for deriving the optimal MIMO transceivers together with the DL/UL time allocation and the alternating direction method of multipliers (ADMM) based algorithm for the RIS design. To avoid the potential high complexity of alternating optimization, we also propose a two-stage scheme, where the RIS design is independent of the others and aims to create favorable DL/UL channels. The extension of our proposed algorithms to the practical imperfect SIC case is then discussed. Numerical results illustrate the superior performance of our proposed algorithms over the baselines in terms of the achievable sum throughput, and their time effectiveness in solving large-scale problems.

We thoroughly investigate the outage performance of reconfigurable intelligent surface (RIS) aided multi-input multi-output (MIMO) communications by exploiting statistical channel state information (CSI). Kornecker channel model is adopted to characterize the impact of spatial correlations among MIMO antennas and reconfigurable reflectors. Mellin transform and random matrix theory are then utilized to derive the outage probability, with which we further conduct the asymptotic outage analysis to obtain insightful findings. In particular, the asymptotic analysis reveals that the number of reflecting elements at the RIS should not be smaller than the total number of MIMO transmit and receive antennas to get rid of the rank deficiency of the cascaded MIMO channels. Moreover, the asymptotic outage probability is a monotonically increasing and convex function with respect to the transmission rate. The numerical outcomes not only corroborate our analytical results, but also demonstrate the negative impact of the spatial correlation and the benefit of increasing the number of reconfigurable reflectors. Finally, we apply the asymptotic results to optimally devise the phase shifts with a low computational complexity.

The optimal design of the energy-efficient multiple-input multiple-output (MIMO) aided uplink ultra-reliable low-latency communications (URLLC) system is an important but unsolved problem. For such a system, we propose a novel absorbing-Markov-chain-based analysis framework to shed light on the puzzling relationship between the delay and reliability, as well as to quantify the system energy efficiency. We derive the transition probabilities of the absorbing Markov chain considering the Rayleigh fading, the channel estimation error, the zero-forcing multi-user-detection (ZF-MUD), the grant-free access, the ACK-enabled retransmissions within the delay bound and the interactions among these technical ingredients. Then, the delay-constrained reliability and the system energy efficiency are derived based on the absorbing Markov chain formulated. Finally, we study the optimal number of user equipments (UEs) and the optimal number of receiving antennas that maximize the system energy efficiency, while satisfying the reliability and latency requirements of URLLC simultaneously. Simulation results demonstrate the accuracy of our theoretical analysis and the effectiveness of massive MIMO in supporting large-scale URLLC systems.

Millimeter wave (mmWave) communication is one of the key technologies in 5G and beyond systems to address the tremendous growth in mobile data traffic owing to the abundant spectrum resources. Ultra-dense network deployment is a promising solution to combat the limited coverage, high propagation loss and attenuation of mmWave signals. This study investigates the beam management, with focus on beam configuration of mmWave base stations, in the ultra-dense mmWave network. To fulfill adaptive and intelligent beam management while protecting user privacy, we employ a double deep Q-network under a federated learning to tackle the beam management problem which is formulated to maximize the long-term system throughput. Simulation results demonstrate the performance gain of our proposed scheme.

Reconfigurable intelligent surface (RIS) has recently attracted a spurt of interest due to its innate advantages over massive MIMO on power consumption. In this paper, we study the outage performance of multi-RIS system with the help of hybrid automatic repeat request (HARQ) to improve the RIS system reliability, where the fading channels are modeled by Rician fading and the phase shift setting only depends on the line-of-sight (LoS) component. Both the exact and asymptotic outage probabilities under Type-I HARQ and HARQ with chase combining (HARQ-CC) schemes are derived. Particularly, the tractable asymptotic results empower us to derive meaningful insights for HARQ-aided multi-RIS systems. On the one hand, we find that both the Type-I and the HARQ-CC schemes can achieve full diversity that is equal to the maximal number of HARQ rounds. On the other hand, the closed-form expression of the optimal phase shift setting with respect to outage probability minimization is obtained. The optimal solution indicates that the reflecting link direction should be consistent with the direct link LoS component. Finally, the analytical results are validated by Monte-Carlo simulations.

Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer from nonlinear distortions of the power amplifier (PA) at the transmitters, which degrades the channel estimation and detection performances of the receivers. To mitigate the clipping distortions at the receivers end, we leverage deep learning (DL) and devise a DL based receiver which is aided by the traditional least square (LS) channel estimation and the zero-forcing (ZF) equalization models. Moreover, a data driven DL based receiver without explicit channel estimation is proposed and combined with the model aided DL based receiver to further improve the performance. Simulation results showcase that the proposed model aided DL based receiver has superior performance of bit error rate and has robustness over different levels of clipping distortions.

Secure localization and velocity estimation are of great importance in Internet-of-Things (IoT) applications and are particularly challenging in the presence of malicious attacks. The problem becomes even more challenging in practical scenarios in which attack information is unknown and anchor node location uncertainties occur due to node mobility and falsification of malicious nodes. This challenging problem is investigated in this article. With reasonable assumptions on the attack model and uncertainties, the secure localization and velocity estimation problem is formulated as an intractable maximum a posterior (MAP) problem. A variational-message-passing (VMP)-based algorithm is proposed to approximate the true posterior distribution iteratively and find the closed-form estimates of the location and velocity securely. The identification of malicious nodes is also achieved in the meantime. The convergence of the proposed VMP-based algorithm is also discussed. Numerical simulations are finally conducted and the results show the VMP-based joint localization and velocity estimation algorithm can approach the Bayesian Cramer Rao bound and is superior to other secure algorithms.

In order to combat the challenges of scarce spectrum bandwidth and energy supply in the Internet-of-Things networks, this article investigates a wireless-powered massive multiple-input-multiple-output (MIMO) underlay cognitive radio (CR) system, where the secondary user (SU) harvests the energy from the primary network through a time-switching (TS) strategy. Moreover, the detection technique of maximum-ratio combining (MRC) is applied at base stations (BSs) due to its low complexity. Thereon, the achievable rate of the secondary network is thoroughly analyzed by accounting for spatial correlations at both users and BSs, which significantly differentiates the analysis from the prior literature. With the analytical results, the impacts of the number of antennas and the spatial correlations are quantified. In particular, the negative impact of the spatial correlation on the achievable rate is theoretically proved based on the majorization theory. Furthermore, the TS factor and/or power allocation coefficients are optimally designed based on the statistical channel state information (CSI) only to maximize the achievable rate while ensuring the maximum endurable interference constraint. It is found that the optimal power allocation matrix is a variant of water-filling solution with two water levels associated with sum power constraint and sum weighted power constraint, respectively. The weighting matrix is aligned with the transmit spatial correlation in the SU. The joint design of TS factor and power allocation coefficients is also shown to reach a superior performance over the algorithms-based solely on TS factor or power allocation coefficients optimizations. Finally, the analytical results are validated by conducting simulations.

Reconfigurable intelligent surface (RIS) stands out as a promising technology by enhancing the electromagnetic wave propagation environment with its passive reflecting elements. In this paper, we focus on the ergodic sum-rate analysis and maximization of the RIS-assisted uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) under Rician fading by exploiting full statistical channel state information (CSI). The spatial correlations at the base station, the users, and the RIS are also considered. By using the replica method originated in statistical physics, the closed-form asymptotic ergodic sum-rate of the system is first derived in the large-system regime on the account of the unique channel structure of the RIS-assisted MIMO-MAC system. Then, based on the derived asymptotic ergodic sum-rate, we propose an alternating optimization (AO) algorithm to jointly design the transmit covariance matrix of users and the phase-shifting matrix of RIS with full statistical CSI. Simulation results are also demonstrated to verify the accuracy of the derived asymptotic ergodic sum-rate and the superiority of the proposed AO algorithm. The results reveal that the derived closed-form asymptotic ergodic sum-rate matches very well with the Monte Carlo results even for a small number of antennas, and the proposed AO algorithm can achieve up to 10 bps/Hz gain at high signal-to-noise (SNR) regime, which is therefore valuable for future RIS-assisted MIMO-MAC system designs.

Multi-user millimeter wave (mmWave) massive multi-input multi-output (MIMO) is a promising technology for the next generation mobile communication systems. However, there are still unsolved problems before such commercial MIMO networks are rolled out. One main issue is the hardware cost and power consumption which grow significantly as the number of radio frequency (RF) components increases. To tackle this issue, we consider to deploy one-bit analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at the base station (BS), and study uplink (UL)/downlink (DL) channel estimation and DL precoding techniques for the associated MIMO systems with one-bit ADCs/DACs. Specifically, we first formulate the UL channel estimation as an one-bit compressed sensing problem, and then devise an efficient gridless generalized approximate message passing-based (GL-GAMP) algorithm to handle it. Additionally, we develop an exhaustive search based proximal gradient descent method (PGM) for DL channel estimation. Note that with slight modifications, we show that PGM can also be applied to solve the DL precoding problem. Simulation results showcase that our methods have advantages over the state-of-the-art techniques and are able to offer good trade-offs between accuracy and computational complexity, which ultimately indicates their superiority in the application of mmWave MIMO systems with one-bit ADCs/DACs.

Reconfigurable intelligent surface (RIS) is an emerging technology to enhance wireless communication in terms of energy cost and system performance by equipping a considerable quantity of nearly passive reflecting elements. This study focuses on a downlink RIS-assisted multiple-input multiple-output (MIMO) wireless communication system that comprises three communication links of Rician channel, including base station (BS) to RIS, RIS to user, and BS to user. The objective is to design an optimal transmit covariance matrix at BS and diagonal phase-shifting matrix at RIS to maximize the achievable ergodic rate by exploiting the statistical channel state information at BS. Therefore, a large-system approximation of the achievable ergodic rate is derived using the replica method in large dimension random matrix theory. This large-system approximation enables the identification of asymptotic-optimal transmit covariance and diagonal phase-shifting matrices using an alternating optimization algorithm. Simulation results show that the large-system results are consistent with the achievable ergodic rate calculated by Monte-Carlo averaging. The results verify that the proposed algorithm can significantly enhance the RIS-assisted MIMO system performance.

To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input-multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to demultiplex the input data streams. Only statistical channel state information (CSI) is available at the base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering the Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of the Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with a higher transmission rate under benign channel conditions.

Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors' positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target's location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithms.

In this work, we propose a novel non-orthogonal pilot design framework to tackle the pilot contamination problem in multi-cell massive multiple input multiple output (MIMO) systems. Assuming the minimum mean square error (MMSE) estimators are adopted at the base stations (BSs), we design pilot signals under power constraints by minimizing the total mean square errors (MSEs) of the MMSE estimators of all BSs. The pilot design problem is difficult to solve due to the non-convex objective function. A linearized alternating direction method of multipliers (L-ADMM) algorithm is introduced to solve the above non-convex optimization problem. The L-ADMM algorithm could approximate the objective function in a linear form, which makes the original problem solvable. In addition, a new non-orthogonal pilot design problem that maximizes the total spectral efficiency of all users in the cellular network is established. We show that the proposed L-ADMM-based pilot design method can be directly extended to solve it. Finally, numerical simulations validate that the proposed pilot design framework can achieve higher channel estimation accuracy and uplink achievable sum rate compared to the state-of-the-art approaches with less computational complexity.

The new paradigm of wirelessly powered two-tier heterogeneous networks (HetNets) is considered in this paper. Specifically, the femtocell base station (FBS) is powered by a power beacon (PB) and transmits confidential information to a legitimate femtocell user (FU) in the presence of a potential eavesdropper (EVE) and a macro base station (MBS). In this scenario, we investigate the secrecy beamforming design under three different levels of FBS-EVE channel state information (CSI), namely, the perfect, imperfect and completely unknown FBS-EVE CSI. Firstly, given the perfect global CSI at the FBS, the PB energy covariance matrix, the FBS information covariance matrix and the time splitting factor are jointly optimized aiming for perfect secrecy rate maximization. Upon assuming the imperfect FBS-EVE CSI, the worst-case and outage-constrained SRM problems corresponding to deterministic and statistical CSI errors are investigated, respectively. Furthermore, considering the more realistic case of unknown FBS-EVE CSI, the artificial noise (AN) aided secrecy beamforming design is studied. Our analysis reveals that for all above cases both the optimal PB energy and FBS information secrecy beamformings are of rank-1. Moreover, for all considered cases of FBS-EVE CSI, the closed-form PB energy beamforming solutions are available when the cross-tier interference constraint is inactive. Numerical simulation results demonstrate the secrecy performance advantages of all proposed secrecy beamforming designs compared to the adopted baseline algorithms.

In this paper, we propose a superimposed training based two-phase robust channel estimation scheme for the unmanned aerial vehicle (UAV) assisted cellular communication system, in which various unitarily-invariant channel statistics errors are considered. Specifically, in the first phase, mobile station (MS) estimates the UAV-MS channel via the UAV training sequence, of which the robust design can be solved based on convex-concave theory. While in the second phase, the superimposed training scheme is considered at the ground base station (GBS) to improve spectrum efficiency. Then the robust GBS training sequence, the information signal power and the UAV amplifying factor are jointly optimized for the partially cascaded GBS-UAV-MS channel estimation subject to GBS and UAV transmit power constraints as well as the required information signal strength at the MS. To tackle this NP-hard problem, the optimal structures of involved variables are firstly derived, based on which the robust superimposed training design is simplified and proved to be quasi-convex in the UAV amplifying factor. Particularly, for Spectral norm and Nuclear norm bounded errors, the optimal training sequence can be obtained via convex-concave theory and Golden section searchWhile for Frobenius norm bounded error, a tractable upper-bounding scheme is proposed for the robust superimposed training design. Furthermore, we extend our work into the more general probabilistic path loss scenario of UAV-ground channels, and analyze the impacts of the probabilistic path loss and RicianK-factor on channel estimation performance. Numerical results illustrate the excellent performance of the proposed superimposed training based two-phase channel estimation scheme.

In this paper, we study the resource allocation design for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long- packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and a significant decoding error rate. Thus conventional resource allocation design based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we employ the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Then, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to various practical constraints. Since the problem formulated belongs to a non-convex mixed integer non-linear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. In addition, we analyze the complexity of the proposed algorithm theoretically. Finally, simulation results show that the proposed optimal algorithm outperforms the suboptimal baseline schemes significantly.

The combination between non-orthogonal multiple access (NOMA) and hybrid automatic repeat request (HARQ) is capable of realizing ultra-reliability, high throughput and massive concurrent connections particularly for emerging communication systems. This paper focuses on characterizing the asymptotic scaling law of the outage probability of the HARQ with chase combining (HARQ-CC)-aided downlink NOMA scheme with respect to the transmit power, i.e., diversity order. The diversity order of the two- user HARQ-CC-aided NOMA system is derived in closed-form, where an integration domain partition trick is developed to obtain the upper and lower bounds of the outage probability. The analytical results show that the diversity order is a decreasing step function of transmission rate given the ratio between the transmit powers allocated to the two users. Moreover, full time diversity can only be achieved under a sufficiently low transmission rate. Additionally, the users' diversity orders follow a descending order according to their respective average channel gains. Monte Carlo simulations finally confirm the analysis.

Two-way massive MIMO amplify-and-forward relaying systems with non-ideal transceivers are investigated in this paper. To be general, multiple-antenna nodes and antenna correlation at both the user equipments (UEs) and the relay are considered, which differentiates the analysis from the prior ones. The achievable rate is analyzed and derived deterministically in closed-form. Joint scaling of the transmission powers and hardware impairments is then particularly investigated. Feasible scaling speeds for the transmission powers and hardware impairments are discovered when the number of relay antennas grows large. It is shown that down scaling of the transmission powers at the UEs and the relay and up scaling of the hardware impairment at the relay with the number of relay antennas are tolerable without reducing the expected rate. However, UE hardware impairment is a key limiting factor to the achievable rate and is not allowed to scale up with the number of relay antennas in order to achieve a non-vanishing rate. Moreover, ceiling effect on the achievable rate is still observable and the ceiling rate varies among different scaling cases. More interestingly, scalings of the UEs transmission power and the relay hardware impairment are found to be offsettable, which means that the relay hardware cost and the UE transmission power are tradable. It is found that the best tradeoff is achieved in the medium scalings of both the relay hardware impairment and UE transmission power. Numerical results are provided to verify the analysis and the tradability between the relay hardware cost and the UE transmission power. The analytical results thus provide solid foundation for flexible system designs under various cost and energy constraints.

Considering the widespread use of effective capacity in cross-layer design and the extensive existence of renewal service processes in communication networks, this paper thoroughly investigates the effective capacity for renewal processes. Exact expressions of the effective capacity at a given quality of service (QoS) exponent are derived for the renewal processes with either constant or variable reward. The simple expressions reveal meaningful insights, such as the monotonicity and bounds of the effective capacity. The analytical results are then applied to evaluate the cross-layer throughput for diverse hybrid automatic repeat request (HARQ) systems, including fixed-rate HARQ (FR-HARQ, e.g., Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR)), variable-rate HARQ (VR-HARQ), and cross-packet HARQ (XP-HARQ). Furthermore, aiming at maximizing the effective capacity via the optimal rate selection, it is disclosed that the VR-HARQ and XP-HARQ attain almost the same performance, and both of them perform better than FR-HARQ. In contrast, most of the prior outcomes approximate the effective capacity with the lack of insightful discussions that hampers the optimal design of HARQ systems. Finally, the numerical results corroborate the analytical ones.

In this paper, we design an uplink/downlink channel estimation method for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems and investigate the impact of beam squint effect that accompanies large array configuration. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the grid mismatch problem. Hence, the proposed method achieves good channel estimation accuracy and handles the wideband direction of arrival (DOA) estimation problem for mmWave massive MIMO communications, where beam squint effect was previously ignored by many existing literatures. The Cramér-Rao bound for unknown parameters is derived to make the proposed study complete. More importantly, a much simplified downlink channel estimation scheme is designed with the aid of angle-delay reciprocity, which significantly reduces training and feedback overhead. The simulation results are provided to demonstrate the superior performance of the proposed method over existing ones.

This paper investigates the performance of Multipleinput multiple-output non-orthogonal multiple access (MIMONOMA) systems with randomly deployed users, where the randomly deployed NOMA users follow Poisson point process (PPP), the spatial correlation between MIMO channels are characterized by using Kronecker model, and the composite channel model is used to capture large-scale fading as well as small-scale fading. The spatial randomness of users' distribution, the spatial correlation among antennas and large-scale fading will severally impact the system performance, but they are seldom considered in prior literature for MIMO-NOMA systems, and the consideration of all these impact factors challenges the analysis. Based on zeroforcing (ZF) detection, the exact expressions for both the average outage probability and the average goodput are derived in closedform. Moreover, the asymptotic analyses are conducted for both high signal-to-noise ratio (SNR) (/small cell radius) and low SNR (/large cell radius) to gain more insightful results. In particular, the diversity order is given by δ = Nr- M + 1, the average outage probability of k-th nearest user to the base station follows a scaling law of O (Dα(Nr-M+1)+2k), the average goodput scales as O(D2) and O(D-2) as D → 0 and D → ∞, respectively, where Nr, M, α and D stand for the number of receive antennas, the total number of data streams, the path loss exponent and the cell radius, respectively. The analytical results are finally validated through the numerical analysis.

This paper develops an interference aware design for cooperative hybrid automatic repeat request (HARQ)-assisted non-orthogonal multiple access (NOMA) scheme for large-scale device-to-device (D2D) networks. Specifically, interference aware rate selection and power allocation are considered to maximize long term average throughput (LTAT) and area spectral efficiency. The design framework is based on stochastic geometry that jointly accounts for the spatial interference correlation at the NOMA receivers as well as the temporal interference correlation across HARQ transmissions. It is found that ignoring the effect of the aggregate interference, or overlooking the spatial and temporal correlation in interference, highly overestimates the NOMA performance and produces misleading design insights. An interference oblivious selection for the power and/or transmission rates leads to violating the network outage constraints. To this end, the results demonstrate the effectiveness of NOMA transmission and manifest the importance of the cooperative HARQ to combat the negative effect of the network aggregate interference. For instance, comparing to the non-cooperative HARQ-assisted NOMA, the proposed scheme can yield an outage probability reduction by 21%. Furthermore, an interference aware optimal design that maximizes the LTAT given outage constraints leads to 17% throughput improvement over HARQ-assisted orthogonal multiple access scheme.

This paper investigates the impact of antenna correlation on full duplex massive multiple-input multiple-output (MIMO) two-way relaying systems. To be general, all nodes in the system are equipped with multiple antennas and arbitrary antenna correlation is considered. Deterministic equivalent of the achievable sum-rate is first given, which enables the derivation of the asymptotic sum-rate when the number of relay antennas is large. The impact of antenna correlation on the asymptotic sum-rate is then thoroughly analyzed. The results reveal that with a large number of relay antennas, the asymptotic sum-rate is independent of the antenna correlation at the relay's transmitter side, however it is Schur-concave with respect to the eigenvalue vectors of the involved antenna correlation matrices at both the users' transceiver sides and the relay's receiver side. In other words, the antenna correlations at both the users' transceiver sides and the relay's receiver side have detrimental effects on the sum-rate. Moreover, the antenna correlation at the users is more significant to the asymptotic sum-rate than that at the relay. The analysis is general and can be easily reduced to consider half-duplex and/or one-way massive MIMO relaying systems. Numerical results are finally provided to validate the analysis. It is also shown that a larger number of user antennas may not always lead to a higher sum-rate, and thus proper selection of the number of user antennas is necessary to maximize the sum-rate.

Sparse code multiple access (SCMA) is a promising non-orthogonal air-interface technology for its ability to support massive connections. In this paper, we design the multiuser codebook and the advanced decoding from the perspective of the theoretical capacity and the system feasibility. First, different from the lattice-based constellation in point-to-point channels, we propose a novel codebook for maximizing the constellation constrained capacity. We optimize a series of 1-D superimposed constellations to construct multi-dimensional codewords. An effective dimensional permutation switching algorithm is proposed to further obtain the capacity gain. Consequently, it shows that the performance of the proposed codebook approaches the Shannon limit and achieves significant gains over the other existing ones. Furthermore, we provide a symbol-based extrinsic information transfer tool to analyze the convergence of SCMA iterative detection, where the complex codewords are considered in modeling the a priori probabilities instead of assuming the binary inputs in previous literature. Finally, to approach the capacity, we develop a low-density parity-check code-based SCMA receiver. Most importantly, by utilizing the EXIT charts, we propose an iterative joint detection and decoding scheme with only partial inner iterations, which exhibits significant performance gain over the traditional one with separate detection and decoding.

Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local meansurements/observations are scattered over a wide geographical area. However, the convergence of Gaussian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular on the convergence of the information matrix. We show analytically that the exchanged message information matrix converges for arbitrary positive semidefinite initial value, and its distance to the unique positive definite limit matrix decreases exponentially fast.

In this paper, we investigate wireless information and power transfer in full-duplex (FD) two-way massive MIMO amplify-and-forward (AF) relaying network. In the considered network, the relay is an energy demander and applies time switching scheme to harvest energy from the users for amplifying and forwarding the users' signals. Different from prior studies, MIMO users and antenna correlation are considered. Achievable sum-rate is analyzed and the impact of antenna correlations is discovered. It is revealed from the analysis that the transmission power at the MIMO users can be cut down proportionally to 1/Mrand/or the transmission power at the relay can be scaled down proportionally to 1/Mr2to maintain a constant sum-rate, when massive antennas are equipped at the relay. Here Mrdenotes the number of antennas at the relay. Moreover, the optimal time switching ratio for energy harvesting at the relay is found to maximize the sum-rate. Finally, the analytical results are validated by computer simulations.

The authors take the quality of sensing data into consideration and design a reputation‐aware incentive mechanism (RAIM) with the properties of truthfulness and individual rationality while maximising the weighted social welfare of the whole system. In addition, in order to reduce the computational complexity of RAIM and improve the system feasibility, the authors propose a heuristic algorithm RAIM‐H, with the computational complexity of . Simulation results show the nice performance of the proposed mechanisms RAIM and RAIM‐H in terms of the weighted social welfare and the average reputation. Specifically, RAIM can improve the weighted social welfare by 8.65 and 48.16% compared with trustworthy sensing for crowd management (TSCM) and random selection, respectively, with the number of smartphone users . Meanwhile, RAIM‐H approaches to the maximum very well and can improve the weighted social welfare by 6.15% and 75% compared with TSCM and random selection, respectively, with the number of smartphone users .

In this paper, the performance of full-duplex (FD) two-way massive multiple-input multiple-output (MIMO) relay systems is analyzed. One popular linear relaying scheme, i.e., maximum ratio combining/maximum ratio transmission relaying, is particularly investigated. Different from prior analyses, multi-pair of MIMO users and antenna correlation at both the relay and the users are considered. Asymptotic sum-rate under a general case is first derived. Four special power scaling cases are then discussed and the corresponding asymptotic sum-rates are derived in simple forms with clear insights. The analytical results clearly quantify the impacts of self-loop interference, intra-group interference and antenna correlation, and discover the power scaling laws under various cases. It is found that the transmission powers at both the MIMO users and the relay can be scaled down inversely proportional to the number of antennas at the relay while maintaining a desirable sum-rate when the number of antennas at the relay grows large. The FD two-way massive MIMO relay system is finally compared with the half-duplex (HD) counterpart. It is revealed that under two special power scaling cases, the FD system can achieve double sum-rate when compared with the HD system. However, under the other two power scaling cases, self-loop interference and intra-group interference degrade the asymptotic sum-rate and the FD system may perform worse than the HD system. The maximum allowable self-loop interference for the FD system to outperform the HD system is given explicitly. In addition, the impact of antenna correlation at the users and the relay is analyzed. The results show that the antenna correlation at the users has complicate impact on the sum-rate, while the antenna correlation at the relay may not affect the sum-rate under certain power scaling cases.

In this paper, outage performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) is analyzed. Unlike prior analyses, time-correlated Nakagami-m fading channel is considered. The outage analysis thus involves the probability distribution analysis of a product of multiple correlated shifted Gamma random variables and is more challenging than prior analyses. Based on the findings of the conditional independence of the received signal-to-noise ratios, the outage probability is exactly derived by using conditional Mellin transform. Specifically, the outage probability of HARQ-IR under time-correlated Nakagami-m fading channels can be written as a weighted sum of outage probabilities of HARQ-IR over independent Nakagami fading channels, where the weightings are determined by a negative multinomial distribution. This result enables not only an efficient truncation approximation of the outage probability with uniform convergence but also asymptotic outage analysis to further extract clear insights, which have never been discovered for HARQ-IR even under fast fading channels. The asymptotic outage probability is then derived in a simple form, which clearly quantifies the impacts of transmit powers, channel time correlation, and information transmission rate. It is proved that the asymptotic outage probability is an inverse power function of the product of transmission powers in all HARQ rounds, an increasing function of the channel time correlation coefficients, and a monotonically increasing and convex function of information transmission rate. The simple expression of the asymptotic result enables optimal power allocation and optimal rate selection of HARQ-IR with low complexity. Finally, numerical results are provided to verify our analytical results and justify the application of the asymptotic result for optimal system design.

This paper conducts outage analysis for cooperative hybrid automatic repeat request with incremental redundancy (C-HARQ-IR). A general time-correlated Nakagami fading channel including fast fading and Rayleigh fading as special cases is considered. An efficient truncated inverse moment matching method is developed to derive the outage probability in closed- form. The accuracy of the analytical results is verified by Monte Carlo simulations and the results reveal that C-HARQ-IR protocol can benefit from high fading order and low channel time correlation.

As an efficient way to collect sensing data, participatory sensing has been receiving more and more attentions and its applications cover various areas such as traffic control and management, environmental monitoring, etc. In a participatory sensing system, Service Provider (SP) works as the task promulgator, Smartphone User (SU) works as the task executor and the Platform handles the sensing task allocation procedure. Incentive mechanism plays a key role in stimulating both SPs and SUs to take part in the participatory sensing system. Most of previous works on the incentive mechanism design do not consider the social relationship of SUs, which may degrade the performance of the participatory sensing system. In this paper, we take the social relationship of SUs into consideration, and design a Social-Aware Incentive Mechanism (SAIM) which can achieve high system performance and satisfy the properties of individual rationality, budget balance, the completely truthful for SPs, and partially truthful for SUs. Simulation results show the better performance of the proposed incentive mechanism compared with two other counterparts in terms of social utility and social effect. Specifically, the proposed mechanism can improve the social utility by 12% and 16% compared with McAfee and random selection, respectively, with the number of smartphone users m = 100.

Various types of sensors have been embedded in smartphones such that a mobile user can easily conduct some sensing tasks. The mobile users conducting the sensing task with their sensor-equipped smartphones have their own unique features, thus can be efficiently complementary to stationary sensors which are deployed at specific locations. In this paper, we consider a heterogeneous sensor network composed of stationary sensors and mobile sensors (i.e., mobile users with sensor-equipped smartphones), in which a key question is how the service provider selects the sensors to conduct the sensing task considering the heterogeneity of sensors in terms of location, mobility pattern, energy constraint, and sensing cost. We propose a greedy algorithm GSSA to reduce the computational complexity. Simulation results show the nice performance of the proposed algorithms compared with the optimal sensor selection algorithm using dynamic programming. In specific, the proposed GSSA improves the achieved social welfare by 32.3% and 35.6% with the time period T=20 for high mobility and low mobility patterns, respectively, compared with the random selection.

A full duplex amplify-and-forward relay network is thoroughly investigated in this paper. To be practical and general, loop interference and Nakagami-m fading channels are considered. Outage probability and ergodic capacity are particularly analyzed and exactly derived in close-forms. Asymptotic outage probability and bounds of the ergodic capacity are also derived in simple forms with clear insights. The analytical results unveil the impacts of loop interference and other system parameters in details. Specifically, it is found that the capacity and outage performance are enhanced via the increase of transmit power at the source, the average fading power and fading order associated with the source-to-relay link. However, under high signal-to-noise ratio (SNR), the increases of the transmit power at the relay and the average fading power corresponding to the loop interference would deteriorate the capacity and outage performance. The results also show that the outage probability obeys an inverse-m1law with respect to the ratio between the transmit powers in the source and relay, where m1is the fading order corresponding to the source-to-relay Nakagami fading channel. The accuracy of the analytical results is validated by Monte-Carlo simulations and they thus can serve as solid foundation for system design and optimization.

In this paper, performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) over Rayleigh fading channels is investigated. Different from prior analysis, time correlation in the channels is considered. Under time-correlated fading channels, the mutual information in multiple HARQ transmissions is correlated, making the analysis challenging. By using polynomial fitting technique, probability distribution function of the accumulated mutual information is derived. Three meaningful performance metrics including outage probability, average number of transmissions, and long term average throughput (LTAT) are then derived in closed-forms. Moreover, diversity order of HARQ-IR is also investigated. It is proved that full diversity can be achieved by HARQ-IR, i.e., the diversity order is equal to the number of transmissions, even under time-correlated fading channels. These analytical results are verified by simulations and enable the evaluation of the impact of various system parameters on the performance. Particularly, the results unveil the negative impact of time correlation on the outage and throughput performance. The results also show that although more transmissions would improve the outage performance, they may not be beneficial to the LTAT when time correlation is high. Optimal rate design to maximize the LTAT is finally discussed and significant LTAT improvement is demonstrated.

This paper presents a distributed hybrid power state estimator, with measurements from both the traditional supervisory control and data acquisition (SCADA) system and the newly invented phasor measurement units (PMUs). The proposed distributed algorithm, which jointly estimates the power states and PMU phase errors, only involves local computations and limited information exchange between neighboring areas, thus alleviating the heavy communication burden compared to the centralized approach. Simulation results show that the performance of the proposed algorithm is very close to that of centralized optimal hybrid state estimates without sampling phase error.

In this paper, the performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) in a mobile ad hoc network (MANET) is analyzed. Based on the theory of stochastic geometry, both interference and the randomness in nodes' locations are incorporated into our analysis. The outage probability after the kth retransmission is analyzed and the outage performance of HARQ-IR is compared with that of Type-I HARQ and HARQ with chase combining (HARQ-CC). Our analysis reveals that the performance gains of HARQ-IR over Type-I HARQ and HARQ-CC increase with the number of retransmissions while they decrease with the path loss exponent. The network throughput in terms of transmission capacity is also analyzed. The results indicate that a certain small number of retransmissions is sufficient to achieve the maximal transmission capacity. Meanwhile, the optimal intensity of source nodes to maximize the transmission capacity is shown to be within a specific interval.

In this paper, an opportunistic cooperative HARQ system is analyzed. Different from prior analyses, time correlated fading channels are considered. Based on moment generation function and Laplace transform, the outage probability and throughput in terms of long-term average transmission rate (LATR) of this opportunistic cooperative HARQ system are derived in closed-forms. The accuracy of the analytical results is verified by computer simulations. From the analytical results, the impacts of the time correlation and other system parameters on the performance are investigated and the optimal packet rate selection to maximize the LATR is discussed.

In this paper, the outage performance of an opportunistic amplify-and-forward cooperative downlink cellular system is analyzed. Different from prior works, the randomness of the network topology is taken into account by modeling the user nodes as a homogeneous Poisson point process. Based on this model, outage probability is derived and the impacts of several system parameters are investigated. Under certain conditions, the closed form expression of outage probability is derived. It is found from our results that the diversity order of this opportunistic cooperative system at high signal-to-noise-ratio (SNR) is one. Moreover, optimal power allocation can be found from our results to minimize the outage probability.

In this paper, outage performance of hybrid automatic repeat request (HARQ) technique in interference dominant mobile ad hoc networks (MANETs) is analyzed. Unlike prior analysis, interference and spatial randomness of the nodes (i.e. the randomness in the number of nodes and nodes' locations) are considered. Based on the theory of point processes, the outage probabilities of two popular HARQ techniques, that are type-I HARQ and HARQ with chase combining (HARQ-CC), are derived in closed forms. The outage performance gain of HARQ-CC over type-I HARQ is also discussed and is found to follow the scaling law of Θ (k2(k+1)/α) where α is the path loss exponent and k is the number of retransmissions. It is also demonstrated that both type-I HARQ and HARQ-CC can significantly improve the communication performance even in interference dominant MANETs. Furthermore, it is revealed that in most cases HARQ-CC is superior over type-I HARQ, however, for a dense network type-I HARQ can provide comparable performance with lower complexity than HARQ-CC and is thus more preferable.

In this paper, the uplink coverage probability for heterogeneous cellular systems with range expansion is analyzed and derived in closed-form. Unlike most of the previous analyses of heterogeneous systems, the randomness of not only the number of mobile users but also their locations is taken into account in the analysis based on the theory of stochastic geometry. With the derived analytical results, the impacts of various system parameters on the uplink performance are investigated in detail. The correctness of the analytical results is also verified by simulations. These analytical results can thus serve as a guidance for the design of the heterogeneous systems.

In this paper, we investigate the joint power allocation for dual-hop amplify-and-forward (AF) MIMO-OFDM cognitive radio (CR) networks. The considered AF MIMO-OFDM CR network coexists with a primary radio (PR) network through underlay spectrum sharing. In order to mitigate the interference to the PR network, environmental learning algorithm is adopted to blindly estimate the null space of the PR user, which are orthogonal to the PR communication channels. With necessary channel state information, under independent transmit power constraints as well as the interference constraints, the power allocation of CR source and relay and subcarrier pairing over two hops are optimized jointly to maximize the CR network throughput. Furthermore, the relay node implements an effective subcarrier permutation policy to enhance the performance further at the cost of affordable complexity. Finally, the performance advantages of the proposed algorithm are demonstrated by the simulation results.

In this paper, the performance of hybrid automatic repeat request (HARQ) technique in an ad hoc network is analyzed. Unlike most prior works on the analysis of HARQ, both time-correlated interference and feedback errors are taken into account in the analysis. Based on the theory of point processes, outage probability after the nth retransmission, delay limited throughput and mean transmission time are derived in closed forms. The analytical results are verified by simulations and the impacts of various parameters on the network performance are investigated in detail. It is found that the outage probability obeys the inverse-2/α power law over the number of retransmissions, where α is the path loss exponent. Furthermore, it is demonstrated that the feedback error significantly degrades the delay limited throughput when the ad hoc network becomes dense, while the increment of mean transmission time due to the feedback error is a concave function of the intensity of the network.

In this paper, we consider robust distributed filter-and-forward beamforming design for two-way relaying networks over the frequency selective fading channels, in which two users exchange information with the aid of multiple single-antenna relay nodes. In contrast to the prior works in which the channel state information (CSI) is available, in our work CSI for all relevant links between the users and relay nodes is not perfectly known with stochastic channel errors. Under stochastic channel errors, a robust beamforming design aiming at maximizing the total system signal-to-interference-plus-noise-ratio (SINR) under individual relay power constraints is proposed. Simulation results show that the proposed robust beamformer reduces the sensitivity of the two-way relaying systems to channel estimation errors, and performs better than the algorithm using estimated channels only.

In this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables.

In this paper, joint transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors in both two hops is investigated. Due to the fact that various linear transceiver designs can be transformed to a weighted linear minimum mean-square-error (LMMSE) transceiver design with specific weighting matrices, weighted mean square error (MSE) is chosen as the performance metric. Precoder matrix at source, forwarding matrix at relay and equalizer matrix at destination are jointly designed with channel estimation errors taken care of by Bayesian philosophy. Several existing algorithms are found to be special cases of the proposed solution. The performance advantage of the proposed robust design is demonstrated by the simulation results.

In this paper, linear beamforming design for uplink amplify-and-forward relaying cellular networks, in which multiple mobile terminals rely on one relay station to communicate with the base station, is investigated. In particular, the base station, relay station and mobile terminals are all equipped with multiple antennas. Based on linear minimum mean-square-error (LMMSE) criterion and exploiting a hidden convexity in the problem, the precoder matrices at multiple mobile terminals, forwarding matrix at relay station and equalizer matrix at base station are jointly designed. Furthermore, several existing linear beamforming designs for multi-user (MU) MIMO systems and AF MIMO relaying systems can be considered as special cases of the proposed solution. Simulation results are presented to demonstrate the performance advantage of the proposed algorithm.

Cooperative communications can significantly enhance the performance of wireless systems by exploiting the inherent spatial diversity gains. In this work, a decode-and-forward cooperative cellular system with spatial random users is investigated. Under a realistic channel model including path loss, shadowing and multipath fading, the users are selected as relays depending on their instantaneous SNR so that error propagation caused by detection errors at the relays can be mitigated. The locations of users are modeled as random point processes. Based on the theory of point processes, an analytic approach to system performance is developed to accommodate the random location of users as well as the underlying channel. The locations of the selected relays are investigated and the outage performance averaging over all possible user locations is analyzed. The accuracy of the analytical results is verified by Monte-Carlo simulations. Various issues, such as power allocation, are also investigated using the numerical results.

This paper investigates an opportunistic cooperative system with multiple relays. The locations of the relays are essentially random due to their unpredictable mobility and are thus assumed to form a spatial Poisson process. A general analytical approach to performance analysis is developed to accommodate the randomness of the locations as well as the underlying channels. The outage probability of the system is derived based on the theory of point processes. In particular, two relay selection criteria, namely the best forward channel selection and the best worse channel selection, are used as examples to illustrate the proposed approach. The accuracy of the analytical results is verified by Monte-Carlo simulations with various system configurations.

In this paper, partial data-dependent superimposed training based channel estimation for OFDM systems over doubly selective channels (DSCs) is addressed. Due to the presence of unknown data as interference, we first derive a minimum mean square error (MMSE) channel estimator by treating the effect of unknown data as noise. To further improve the performance, a novel iterative algorithm which jointly estimates channel and suppresses interference from data is proposed via variational inference approach. Simulation results show that the proposed algorithm converges after a few iterations. Furthermore, after convergence, the performance of the proposed channel estimator is very close to that with full training at high SNRs.

In this paper, robust transceiver design based on minimum-mean-square-error (MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is investigated. The channel estimation errors are modeled as Gaussian random variables, and then the effect are incorporated into the robust transceiver based on the Bayesian framework. An iterative algorithm is proposed to jointly design the precoder at the source, the forward matrix at the relay and the equalizer at the destination, and the joint design problem can be efficiently solved by quadratic matrix programming (QMP).

Semi-blind joint CFO, channel estimation and data detection for OFDM systems over doubly selective channels (DSCs) is investigated in this work. A joint iterative algorithm is developed based on the maximum a posteriori expectation-maximization (MAP-EM) algorithm. In addition, a novel algorithm is also proposed to obtain the initial estimates of CFO and channels. Simulation results show that the performance of the proposed CFO and channel estimators approaches to that of the estimators with full training at high SNRs. Moreover, after convergence, the performance of data detection is close to the ideal case with perfect CFO and channel state information.

In this paper, robust joint design of linear relay precoders and destination equalizers for amplify-and-forward (AF) MIMO relay systems under Gaussian channel uncertainties is investigated. After incorporating the channel uncertainties into the robust design based on the Bayesian framework, a closedform solution is derived to minimize the mean-square-error (MSE) of the received signal at the destination. The effectiveness of the proposed robust transceiver is verified by simulations.

Data detection for OFDM systems over unknown doubly selective channels (DSCs) and carrier frequency offset (CFO) is investigated. A semiblind iterative detection algorithm is developed based on the expectation-maximization (EM) algorithm. It iteratively estimates the CFO, channel and recovers the unknown data using only limited number of pilot subcarriers in one OFDM symbol. In addition, efficient initial CFO and channel estimates are also derived based on approximated maximum likelihood (ML) and minimum mean square error (MMSE) criteria respectively. Simulation results show that the proposed data detection algorithm converges in a few iterations and moreover, its performance is close to the ideal case with perfect CFO and channel state information.

This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worst-case based systems.

In this paper, we address the robust linear transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems, where both transmitters and receivers have imperfect channel state information (CSI). With the statistics of channel estimation errors in the two hops being Gaussian, we formulate the robust linear-minimum-mean-square-error (LMMSE) transceiver design problem using the Bayesian framework, and derive a closed-form solution. Simulation results show that the proposed algorithm reduces the sensitivity of the relay system to channel estimation errors, and performs better than the algorithm using estimated channel only.

In this paper, a joint channel estimation and data detection algorithm is proposed for OFDM systems under doubly selective channels (DSCs). After representing the DSC using Karhunen-Loe¿ve basis expansion model (K-L BEM), the proposed algorithm is developed based on the expectation-maximization (EM) algorithm. Basically, it is an iterative algorithm including two steps at each iteration. In the first step, the unknown coefficients in K-L BEM are first integrated out to obtain a function which only depends on data, and meanwhile, a maximum a posteriori (MAP) channel estimator is obtained. In the second step, data are directly detected by a novel approach based on the function obtained in the first step. Moreover, a Bayesian Cramer-Rao Lower Bound (BCRB) which is valid for any channel estimator is also derived to evaluate the performance of the proposed channel estimator. The effectiveness of the proposed algorithm is finally corroborated by simulation results.

In recent literature, an increasing radii algorithm (IRA) is introduced to decode the signals in multiple-input multiple-output (MIMO) systems. It is developed from the idea of sphere decoder (SD) and can achieve near-maximum likelihood (ML) decoding performance with relatively lower complexity than the SD by taking the noise statistics into consideration. In this paper, an improved IRA (IIRA) is proposed to further reduce the complexity. Apart from the noise statistics, channel information is taken into consideration as well. Additionally, a radii update scheme is introduced which enables the search space of the proposed algorithm to be further pruned. As a result, the proposed algorithm achieves a performance close to that of the IRA but with substantial computational savings. The effectiveness of the proposed decoder is verified by simulations.

Cooperative communications is a multiuser framework for wireless network environments. The collaboration among the users can exploit the inherent spatial diversity gains to thereby enhance the system performance significantly. Due to the mobility of users, especially in cellular systems, the exact locations of them are usually unknown a priori and hard/costly to discover. Therefore it is reasonable to model their locations as Bernoulli process. In this work, the uplink of a microcell in a decode-and-forward cooperative cellular system is investigated. The outage probability is derived based on a practical channel model including not only path loss but also shadowing and multipath fading effects. It is shown that considerable performance gains can be obtained.

This paper considers the problem of robust linear transceiver design for a dual-hop amplify-and-forward (AF) MIMO relay system, with Gaussian random channel uncertainties in both hops. By taking the channel uncertainties into account, an iterative algorithm is proposed to minimize the mean-square-error (MSE) of the output signal at the destination. Simulation results show that the proposed algorithm reduces the sensitivity of the AF MIMO relay systems to channel estimation errors, and performs better than the algorithm based on estimated channels only.

In cognitive radio, it is crucial to control the interference from secondary users (SUs) to primary users (PUs). This paper studies the use of transmit beamforming in the cognitive secondary network for enhancing the performance of a SU while controlling the interference to the PUs. In particular, we propose to maximize the service probability of the SU with a number of probability constraints on the interference level at the PUs with the aid of imperfect channel state information (CSI). Modeling the CSI uncertainty as an additive Gaussian noise, it is shown that the optimum can be realized by second-order cone-programming (SOCP) in tandem with a one-dimensional search. Results reveal that the proposed approach provides a technique to tradeoff the performance between the PUs and the SU, making an analytical connection between non-robust and worst-case systems.

In this paper, a MIMO-OFDM system without cyclic prefix (CP) is considered and a two-step signal detection algorithm is proposed. The algorithm is based on some structural properties derived from shifting the received OFDM symbols. The first step cancels inter-carrier interference (ICI) and inter-symbol interference (ISI) with an equalizer designed using second-order statistics of the shifted received OFDM symbols. The second step detects the signals from the equalizer output in which the signals are still corrupted with multi- antenna interference (MAI). In the proposed algorithm, precise knowledge of the channel length is unnecessary and only one pilot OFDM symbol is utilized to estimate the required channel state information, assuming the number of transmit antennas is smaller than the number of subcarriers in one OFDM symbol. Simulation results show that the proposed algorithm achieves comparable performance to algorithms for MIMO-OFDM system with cyclic prefix and it is robust against channel length overestimation.

This letter addresses the problem of symbol error probability (SEP) analysis for maximum likelihood (ML) detection in multiple-input multiple-output (MIMO) multiplexing systems. A new analytical method is presented based on the total probability theorem. The effects of imperfect channel estimation and power allocation scheme are investigated. The accuracy of the proposed method is demonstrated by Monte-Carlo simulations. It is shown that the analytical results match quite well with the simulation ones irrespective of the signal-to noise- ratio (SNR).

Estimation of the channel state information (CSI) in quadratic form (i.e., quadratic channel estimation) in the downlink can be performed at the base station by using the relayed signals from the mobile users, which facilitates optimization with transmitter CSI. In this letter, the condition for the optimal training sequence for quadratic channel estimation in a multiuser multiple-input single-output (MISO) antenna system in the downlink is first obtained. The mean-square-error (MSE) in the CSI estimate is then analyzed. Based on the quadratic CSI estimates, a robust beamforming optimization algorithm to minimize the base station power while achieving individual users' quality-of-service (QoS) constraints, measured by the MSE in data reception, is proposed.

This paper investigates the effects of channel estimation errors on zero-forcing (ZF) vertical Bell laboratories layered space time (V-BLAST) detection. An analytical method is presented to derive the symbol error probability (SEP) of the signals detected at each stage. The effects of imperfect channel estimation on the SEP performance of V-BLAST detection are then studied. It is shown that ZF-VBLAST detection is very sensitive to the channel estimation errors under high signal to noise ratio (SNR). It is also shown that when optimal ordering is adopted, the effects of channel estimation errors are more significant on the latter detection stages.

In (Dong and Ding, 2006), it has been proposed that channel estimates in quadratic form can be obtained at the base station by sending training sequences to the mobiles where the received signals are forwarded back to the base for channel estimation. In this paper, we first examine the optimal training sequence design for such quadratic channel estimation and then analyze the error bound and statistics of the channel estimates in quadratic form. With the analytical results, two problems for a multiple-input single-output (MISO) antenna system in the downlink are constructed and optimally solved: Power minimization with individual users' 1) worst-case signal-to-interference plus noise ratio (SINR) and 2) average mean-square-error (MSE) constraints, through optimal multiuser MISO beamforming and power allocation.

This paper addresses the problem of performance analysis for maximum likelihood (ML) detection in two-input multiple-output multiplexing systems. A novel analytical method is presented to formulate the symbol error probability (SEP). Based on the total probability theory, the SEPs of the two transmitted signals are obtained in closed-form by solving the SEP equations. Both equal and unequal power allocations are investigated. The accuracy of the proposed method is verified by Monte-Carlo simulations. The proposed method can also be extended to systems with more than two inputs.

In this paper, an analytical approach to performance analysis of vertical Bell laboratories layered space time (V-BLAST) detection with optimal ordering for systems with two transmit antennas is presented. The post-detection signal-to-noise-ratio (SNR) at each stage is derived and the symbol error probabilities (SEP) of the signals are then given in closed-form. The analysis takes into account the effects of optimal ordering, imperfect channel estimation and error propagation, which were rarely considered in the literature due to the difficulties in evaluation. The accuracy of the analysis is demonstrated by Monte-Carlo simulations.

This letter addresses the problem of joint estimation of carrier frequency offset (CFO) and channel for OFDM systems in the presence of timing ambiguity. Based on two signal models for quasi-synchronized OFDM systems, two joint CFO and channel estimators are derived from the Maximum Likelihood (ML) criterion. The first estimator obtained is a joint estimator for all unknown parameters, which needs a multi-dimensional search. The second estimator has a reduced complexity, but its performance slightly degrades compared with the first one. Through MSE analyses, we find that when the number of subcarriers is large, the two estimators are equivalent.

This paper addresses the problem of optimal training for joint carrier frequency offset (CFO) and channel estimation in multiuser MIMO OFDM systems, where each user can utilize all available subcarriers. To choose the training sequence with the goal of providing the smallest estimation mean square error (MSE), the Cramer-Rao bound (CRB) is derived in close-form. However, little insight on the sequence selection can be obtained from the CRB directly due to its complicated dependence on training sequence and channel parameters. To proceed, asymptotic CRB, which has a much simpler dependence on the training, is derived. It is found that the optimal training sequences should be individually white and uncorrelated with each other. Simulation results illustrate the merits of the proposed training design.

This paper presents an improved derivative method to provide good symbol synchronization in multi-path channels for orthogonal frequency division multiplexing (OFDM) systems. As the conventional derivative method (Willians et al., 2005), the improved derivative method can reduce the inter-symbol interference (ISI) caused by multi-path channels. Moreover, the proposed method is robust against the frequency offset and the filter length which are very selective to the conventional derivative method. Simulation results confirm that the improved derivative algorithm outperforms the maximum likelihood algorithm (ML) (Van De Veek et al., 1997) and the conventional derivative method.

In this letter, a Rake-based multiuser detection technique, consisting of multiuser single-path signal separation, time-delay estimation, and multipath combining, is proposed for quasi-synchronous spatial-division multiple-access (SDMA) systems. Time diversity is achieved for performance improvement. In addition, only the upper bounds of the channel length and the time delays are required. Simulation results verify the effectiveness of the proposed technique, as well as its robustness against overestimation of the maximum channel length and the maximum time delay

In this paper, signal detection for MIMO-OFDM systems operating on quasi-static frequency selective fading channels with time offsets is considered. A channel equalizer is first proposed to cancel most inter-block and inter-symbol interferences, based on second order statistics of the received signals. The transmitted signals are then detected from the equalizer output with the aid of a few pilot symbols. In most scenarios, the number of pilot symbols required here is less than that required in the existing algorithms. In addition, perfect time synchronization, time offset and channel length estimations are not needed. The algorithm is applicable irrespective of whether the channel length is shorter than, equal to or longer than the cyclic prefix (CP) length. Simulations confirm the effectiveness of the proposed algorithm and verify its robustness against time offsets, namely, different time delays for different users.

In this paper, a time domain equalization algorithm is proposed for single-TX multiple-RX OFDM systems over frequency selective fading channels. The algorithm, which cancels most of the intersymbol interference (ISI), utilizes the orthogonality of the IFFT matrix and the second order statistics of the received signals. The signals are then detected, with the aid of only four pilots, from the equalizer output. The number of pilots required in the proposed algorithm is less than that in existing algorithms and channel length estimation is not needed. In addition, the proposed algorithm is applicable to both the case where the channel length is shorter than or equal to the length of cyclic prefix (CP), and the case where the channel length is longer than the length of cyclic prefix which results in interblock interference (IBI). Simulation results confirm the effectiveness of the proposed algorithm in both cases and indicate that it is more practical as there is no restriction on the channel and CP lengths.

In this paper, signal detection with time delay estimation for quasi-synchronous MIMO systems on multipath channels is considered. The paper first formulates the problem by shifting each user's signals so that previous results on synchronized system for ISI cancellation based on second-order statistics of the received signals can be applied. The first step effectively reduces the quasi-synchronous system on the multipath channels to the quasi-synchronous system on single-path channels. In the second step, an equivalent model is constructed with the delay information embedded in the reduced channel model. A simultaneous time delay estimation and multiuser detection algorithm is then proposed. The proposed technique can easily be implemented in practical situations as perfect synchronization and knowledge of the channel length and the time delays are not required. Simulation results show that the proposed technique can achieve good performance and is not sensitive to overestimation of the maximum channel length and the maximum time delay.

Many known second-order statistics based blind algorithms for MIMO channel estimation are sensitive to channel order overestimations. To overcome this problem, an algorithm is proposed in Gazzah, H et al. (2002) for SIMO system only, and then a simple generalization of it to MIMO system is presented. In this paper, improvements and refinements on the algorithm are given, which makes the method robust to noise and round-off error. The method can give estimations of all channel impulse responses subject to a scalar matrix ambiguity when only an upper bound for all MIMO channel orders is known.