Integrated sensing and communication (ISAC) enables reliable transmission by sensing targets in scattering environment. However, a single base station (BS) struggles to accurately detect three-dimension (3D) moving targets. Meanwhile, asynchronicity caused by spatially separated transceivers introduces timing offsets (TOs) and carrier frequency offsets (CFOs), which impair the accuracy of sensing parameter. To address this, this paper studies an orthogonal frequency division multiplexing (OFDM) networked ISAC system, where multiple base stations (BSs) cooperatively sense multiple unmanned aerial vehicles (UAV) through a joint active and passive sensing framework. The sensing signals received from the BSs are transmitted through a backhaul-limited link to a fusion center (FC) for sensing parameter estimation. In this context, we propose a novel cooperative sensing scheme for UAV parameter estimation based on quantized signals. The joint active and passive sensing problem is formulated as a problem with imperfect parameters (such as TO and CFO). An efficient algorithm is proposed to solve the resulting problem. Simulation results indicate that the proposed cooperative sensing design achieves higher estimation accuracy than other baselines. The results also confirm the performance advantage of multi-BS cooperative sensing over conventional single-BS sensing.

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

This paper considers a joint scattering environment sensing and data recovery problem in an uplink integrated sensing and communication (ISAC) system. To facilitate joint scatterers localization and multi-user (MU) channel estimation, we introduce a three-dimensional (3D) location-domain sparse channel model to capture the joint sparsity of the MU channel (i.e., different user channels share partially overlapped scatterers). Then the joint problem is formulated as a bilinear structured sparse recovery problem with a dynamic position grid and imperfect parameters (such as time offset and user position errors). We propose an expectation maximization based turbo bilinear subspace variational Bayesian inference (EM-Turbo-BiSVBI) algorithm to solve the problem effectively, where the E-step performs Bayesian estimation of the the location-domain sparse MU channel by exploiting the joint sparsity, and the M-step refines the dynamic position grid and learns the imperfect factors via gradient update. Two methods are introduced to greatly reduce the complexity with almost no sacrifice on the performance and convergence speed: 1) a subspace constrained bilinear variational Bayesian inference (VBI) method is proposed to avoid any high-dimensional matrix inverse; 2) the multiple signal classification (MUSIC) and subspace constrained VBI methods are combined to obtain a coarse estimation result to reduce the search range. Simulations verify the advantages of the proposed scheme over baseline schemes.

In millimeter-wave ad hoc networks, the challenges of directional neighbor discovery (ND), combined with the emerging integration of reinforcement learning (RL), necessitate algorithms that are both efficient and RL-compatible under practical constraints. To address this, we propose the Constrained Random Algorithm (CoRA), a novel directional ND scheme achieving high discovery efficiency, robustness to node density variations, broad compatibility with enhancement techniques, and strong support for RL-based optimization. Specifically, CoRA constructs constrained beam direction sets using minimal constraint units—spatially symmetric direction pairs—and executes ND by sequentially scanning these sets with randomized beam selection. We analyze CoRA and show that it provides a unified analytical formulation that includes Pure Random Algorithm (PRA) and Scan-Based Algorithm (SBA) as limiting cases, achieving up to 80% lower time-slot overhead than PRA in sparse networks and up to 60% lower than SBA in dense networks, with strong performance even under minimal hardware and without enhancement techniques. To illustrate compatibility, we propose two variants: GCoRA employing gossip-based neighbor information exchange and SCoRA with a selective reply mechanism, both showing additional performance gains. Furthermore, we propose QCoRA by integrating Q-learning and benchmarking it against QPRA. QCoRA achieves faster convergence, higher cumulative rewards, and enhanced exploration. Finally, we extend QCoRA to deep RL by proposing DDQCoRA, which leverages Double DQN to further reduce discovery latency. Extensive simulations confirm the effectiveness and adaptability of the proposed CoRA family.

The deployment of dual-functional communication and sensing base stations (BSs) in cellular networks will transform these networks into extensive sensing systems, enabling various emerging applications such as autonomous driving and smart cities. However, to fully realize these benefits, optimizing transmission and managing interference among different BSs is crucial. In this paper, we consider a cell-free integrated sensing and communication (ISAC) system, where multiple BSs, each equipped with multiple antennas, collaboratively communicate with multiple single-antenna users while jointly estimating the target’s location through signals reflected by the target and received at all BSs. In this context, fully utilizing all links necessitates coordinated transmit beamforming design among BSs to effectively balance communication and sensing performance. To address this challenge, we first characterize the sensing performance by deriving the Cramér-Rao lower bound (CRLB) for target location estimation. We then formulate an optimization problem to design the transmit beamforming vectors at each BS, minimizing the sensing CRLB while meeting communication quality of service constraint for each user. Due to the highly non-convex nature of this problem, we apply a series of transformations to convert it into a more tractable form and develop an iterative algorithm to solve it. Numerical results validate the effectiveness of the proposed design, highlighting its advantages in balancing the trade-off between sensing and communication compared to three benchmark designs.

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

With the advancement of wireless communications toward higher user densities and increasingly complex environments, near-field communication has emerged as a critical research focus. Supporting multi-user access in this regime with manageable complexity remains a key challenge, particularly due to the coupling between the spherical wavefront effect and the spatial non-stationarity (SnS) property, which complicates the exploitation of spatial correlation among user channels. To address this, we propose a unified multi-user channel model that incorporates joint support to capture the structured sparsity shared across users. Additionally, we introduce a two-dimensional (2D) Markov prior to model both local sparsity pattern continuity across adjacent grid points and joint burst sparsity in the shared support structure. Based on this, we develop a spatial information-sharing-aided framework that alternately estimates model parameters. Specifically, an inverse-free variational Bayesian inference (IF-VBI) algorithm is employed in the channel estimation module to avoid high-dimensional matrix inversion while enabling information exchange among users via joint support. In the common grid update module, joint updates across users are performed to achieve a full spatial-domain optimum, whereas in the joint visible region (VR) matrix detection module, the user-sharing structure is exploited to decouple and efficiently solve the VR matrix. Simulation results validate the effectiveness of the proposed approach, demonstrating improved estimation accuracy and computational efficiency in multi-user near-field extremely large-scale multiple-input-multiple-output (XL-MIMO) systems.

The growing number of users leads to an increase in pilot overhead, while the constraint of limited pilot resources poses a challenge in supporting all users with orthogonal pilots. By exploiting the spatial characteristics of the multi-user (MU) environment, it is possible to reduce pilot costs and improve the channel estimation performance. In practical scenarios, nearby users often share the same scatterers, whereas users situated farther apart tend to exhibit orthogonal channels. This paper proposes a two-timescale approach for joint MU channel estimation and localization, modeling the MU channel in the 3-D location-domain. In the long-timescale phase, the positions of the scatterers are estimated through a scatterer association algorithm based on density-based spatial clustering of applications with noise (DBSCAN). This enables the base station to group users via a graph-coloring-based user grouping algorithm, achieving spatial division multiplexing of pilots and reducing overhead. In the short timescale phase, a low-complexity scattering environment aware location-domain turbo channel estimation (SEA-LD-TurboCE) algorithm integrates overlapping scatterer information to achieve precise MU channel estimation and localization under spatially reused pilots. Simulation results verify the superior channel estimation and localization performance of our proposed scheme over the baselines.

This paper investigates the energy-efficient power scheduling (EEPS) problem in extended reality (XR) transmission with hard-latency constraints. Highly dynamic wireless channels and low packet dropout requirements render this a challenging non-convex stochastic constrained sequential decision problem, further complicated by XR’s multi-timeslot large-packet transmissions and non-stationary traffic. Traditional resource scheduling techniques are limited to simple and known traffic/channel models, while existing constrained reinforcement learning (CRL) algorithms lack theoretical guarantees for satisfying non-convex stochastic constraints and struggle to adapt to rapidly changing traffic dynamics. To address this, we propose a Context-aware Constrained Reinforcement Learning (CACRL) algorithm, consisting of a CRL module and a context inference (CI) module. The CRL module uses a policy network for EEPS decision-making and optimizes it through a novel constrained stochastic successive convex approximation (CSSCA) method, which effectively handles the original problem by solving a sequence of convex surrogate problems. The CI module integrates context-aware meta-learning and reward-reshaping mechanisms to infer varying traffic dynamics and transform sparse packet dropout signals caused by multi-timeslot transmissions into dense ones, guiding the CRL module to quickly converge under XR traffic. Theoretical analyses provide insights into the CACRL, while simulations demonstrate it outperforms advanced baselines in both power conservation and meeting packet dropout constraints.

This work considers a spatially non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationarity, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and develop a novel dynamic alternating maximum a posteriori (DA-MAP) method to solve the problem. DA-MAP contains four core modules: channel estimation module, VR detection module, grid update module, and temporal processing module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computationally intensive matrix inverse in each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulation results demonstrate that the proposed method achieves significant improvements in channel tracking performance with low computational overhead.

Integrated sensing and communication (ISAC) has drawn great attention for its capacity to simultaneously support wireless communication and environmental sensing. However, in dynamic ISAC systems, environmental mobility poses critical challenges in both dynamic channel estimation and continuous sensing parameter tracking across multiple time slots under severe Doppler effect. To address these challenges with a unified framework, we propose a novel base station-user cooperative sensing approach for joint dynamic channel estimation and sensing parameter tracking, including target/scatterer locations and actual velocities, which is formulated as a maximum a posterior (MAP) problem. In cases where some moving radar targets also serve as communication scatterers, the spatial overlap induces an underlying partially common sparsity between the location-domain sensing and communication channels. Based on this, we develop a two-dimensional Markov Model (2D-MM) based on dynamic location grids to capture the spatio-temporal correlations and partially common sparsity, thereby enhancing both sensing and communication performance. To alleviate the high-complexity matrix inverse in the E-step of sparse Bayesian inference, we propose a dynamic subspace-constrained variational Bayesian inference (D-SCVBI) algorithm with the aid of prior information obtained from the two-dimensional discrete Fourier transform (2D-DFT) localization and state evolution model. Finally, simulations show that the proposed D-SCVBI algorithm attains remarkable performance gains over various baselines.

With the increase of carrier frequencies and the expansion of large antenna array sizes, wireless communication systems are progressively entering the near-field region. Characterized by spherical wave characteristics, near-field channels exhibit more complex spatial structures and higher parameter dimensions. To mitigate these issues, we propose a near-field channel tracking framework based on a Markov model-based prior. Specifically, by leveraging the spherical wave propagation characteristics, we construct a three-dimensional polar-domain sparse representation, i.e., azimuth, elevation, and distance dimensions, enabling high-resolution channel modeling. Incorporating a Markov model-based prior, we can effectively track variations in spherical wave channels. For channel estimation, we develop a Turbo subspace-constrained variational Bayesian inference (Turbo-SC-VBI) algorithm that avoids high-dimensional matrix inversion, significantly reducing computational complexity. Numerical results demonstrate that the proposed method achieves superior channel tracking performance, with lower pilot overhead and acceptable computational complexity.

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

Positioning and sensing, as critical enablers for emerging applications, can be further empowered by multi-band fusion technology. In this paper, a two-stage framework utilizing position-domain joint stochastic particle variational Bayesian inference (PD-JSPVBI) is proposed to address the key challenges in time-of-arrival (TOA)-based direct position determination. Unlike existing works focusing on multi-band delay estimation or single-band direct positioning, our unified framework enables joint target localization directly from multi-station, multiband signals. The algorithm integrates prior information and resolves spatial coordinate coupling via a novel two-dimensional particle-based variational approximation, significantly improving estimation efficiency. Additionally, a joint estimation mechanism is introduced to synchronously optimize positioning parameters (e.g., target coordinates) and non-ideal factors (e.g., timing synchronization errors, random initial phases) within a unified variational framework. Simulation results validate the proposed algorithm's superiority, outperforming conventional cascaded architectures by eliminating error propagation and leveraging multi-band coherence. The proposed solution offers a promising pathway for high-precision direct positioning systems.

In fifth generation (5G) new radio (NR) time-division duplex (TDD) systems, the base station (BS) can estimate downlink channels based on the received uplink pilots due to the channel reciprocity. As the number of users increases, determining how to accommodate more users in the uplink channel estimation stage with limited pilot resources has emerged as a challenging problem. This paper proposes a non-orthogonal pilot reuse scheme based on Zadoff-Chu (ZC) pilot sequence partitioning, where a single ZC sequence is partitioned into two subsequences to support two users, thus doubling the number of users in the uplink channel estimation stage. Additionally, an efficient sequence partitioning optimization algorithm is first employed to suppress the energy leakage caused by the non-orthogonal pilot reuse, and then a successive interference cancellation (SIC)-based multi-user Turbo channel estimation algorithm is proposed to mitigate the residual interference. Through the optimization of sequence partitioning and the utilization of the SIC-based multi-user Turbo channel estimation, more users can be accommodated with minimal channel estimation performance loss, as verified by the simulation results.

We investigate a joint scattering environment sensing, channel estimation, and data recovery problem in an uplink integrated sensing and communication (ISAC) system. Based on a three-dimensional (3D) location-domain sparse channel model, the joint problem is formulated as a bilinear sparse recovery problem with a dynamic position grid and imperfect parameters. We propose an expectation maximization based bilinear subspace variational Bayesian inference (EM-BiSVBI) algorithm to solve the problem effectively, where the E-step performs Bayesian estimation of the the location-domain sparse channel and transmitted data, and the M-step refines the dynamic position grid and learns the imperfect factors via gradient update. In particular, the BiSVBI algorithm in the E-step avoids the high-dimensional matrix inverse by a subspace constrained approach while ensuring convergence to a stationary solution of the Kullback-Leibler divergence minimization problem. Simulations verify the advantages of the proposed method over baselines.

In this paper, we consider uplink channel estimation for massive multi-input multi-output (MIMO) systems with partially connected hybrid beamforming (PC-HBF) structures. Existing beam design are usually based on ideal assumptions and require transmitting pilots across multiple timeslots, making them unsuitable for practical PC-HBF systems. To overcome these drawbacks, we propose a novel beam design to achieve accurate and real-time channel estimation. Firstly, we introduce a group-wise narrow beam design in the vertical dimension to suppress interference and improve vertical angle estimation accuracy, which divides the columns of the uniform planar array into groups and the vertical angle interval into sub-intervals. In this way, each group is assigned with a narrow beam to cover one vertical angle sub-interval, and the set of narrow beams is designed based on the filter theory. Secondly, we optimize the antenna grouping pattern in the horizontal dimension to balance interference suppression and resolution capability, leading to a better horizontal angle estimation performance. Simulation results demonstrate that the proposed scheme achieves notable channel estimation performance gains over baseline methods.

This paper analyzes the impact of beam alignment errors (BAE) on directional ad hoc networks and proposes a new angular error model that derives angular errors from positional errors, overcoming limitations of existing models that assume fixed angular error variance. We derive the probability density function (PDF) of antenna gain for both flat-top and Gaussian antenna models and analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability in Poisson networks. Numerical results show that the new model more accurately captures the effects of BAE on SINR coverage and main lobe alignment probability. The proposed model provides a more realistic understanding of beam misalignment effects in practical network scenarios.

In time division duplex (TDD) massive MIMO systems, the multiple antenna BS suffers severe hardware impairments such as power amplifier nonlinearity, crosstalk and in-phase/quadrature (IQ) imbalance, which significantly degrade the uplink channel estimation performance, especially for hybrid analog-digital beamforming (HBF) systems. Though polynomial and neural network (NN) have been proposed to compensate for the impairments, there are few works consider online joint channel and hardware impairments estimation at the receiver based on pilot signals, which is necessary when impairments varies slowly over time, leading to model mismatch. In this work, we model the impairments using a ResNet and propose a two-timescale Bayesian framework for joint short-term channel tracking and long-term hardware impairments estimation, where the parameters both in the channel model and the ResNet follow a time-evolving Markov prior, while the transition probabilities are set at different timescales. To obtain the marginal posterior distributions of the parameters, we propose a message passingbased two-timescale Bayesian deep learning (MP-TTBDL) algorithm: the messages w.r.t. the channel are computed via turbo orthogonal approximate message passing (Turbo-OAMP), while the messages within the ResNet are computed via deep approximate message passing (DAMP). Such a two-timescale joint estimation scheme can better track the fast-varying wireless channel and slow-varying impairments. Simulations show that the proposed scheme outperforms various baseline schemes under practical scenarios when the impairments and the channel vary at different timescales.

Achieving higher spectrum efficiency has been a critical performance target for the forthcoming 6G era. Non-orthogonal multiple access (NOMA) is regarded as an effective technique to improve the spectrum efficiency a nd t he cluster-free NOMA is a novel generalized downlink NOMA transmission framework. However, there still lacks an efficient joint beamforming and SIC optimization algorithm for cluster-free NOMA. In this paper, we propose a novel channel correlation based two-loop greedy (CC-TLG) algorithm to obtain a near-optimal solution with dramatically reduced complexity. CC-TLG maximizes the WSR of users based on the user channel correlation coefficients, where the outer loop add users one by one based on the WSR in a greedy manner, and the inner loop generates a near-optimal SIC operation based on both the channel correlation coefficients and WSR in a greedy way. Finally, simulations demonstrates the effectiveness of the proposed CC-TLG algorithm.

In this paper, we consider joint channel extrapolation and scattering environment sensing for multi-user timedivision duplexing (TDD) massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Unlike conventional angle-delay domain channel modeling techniques, we adopt a location domain channel modeling approach to leverage the spatial overlap of scatterers across different users. By exploiting this, we propose a novel two-stage joint multi-user channel extrapolation and scattering environment sensing algorithm. In the coarse estimation stage, a low complexity Spatial and Temporal Multiple Signal Classification (ST-MUSIC) algorithm is utilized to perform independent channel extrapolation and scatterer localization for each user. In the refined estimation stage, an aggregated location grid constructed from the coarse estimation result is used to enable a sparse representation of the location domain channels of all users. And by combining the inverse-free variational Bayesian inference (IF-VBI) and the expectation maximization (EM) algorithm, an EM-IF-VBI algorithm is designed to jointly refine the location domain channel coefficients and aggregated location grid of all users, which can exploit the spatial overlap of scatterers across different users to simultaneously achieve more accurate channel extrapolation and scattering environment sensing. Simulation results show that our proposed method significantly outperforms existing baseline methods.

In this paper, we propose an over-the-air MIMO autoencoder-based simultaneous multiple access (OA-MIMO-AEbased SMA) scheme for applications that require high-efficiency transmission but can tolerate certain errors. Unlike existing nonorthogonal multiple access schemes, the proposed SMA scheme conducts simultaneous transmission to exploit the superposition nature of the wireless multiple access channel and transform it into an equivalent wireless linear processing layer (WLPL) of the MIMO autoencoder. The receiver aggregates the signals from different transmitters by computing their weighted sum via OFDM over-the-air computation (AirComp), which can fully exploit the correlations among the transmitted data to significantly reduce the communication cost. An alternating algorithm for OFDM over-the-air computation is proposed for accurate data fusion. Benefiting from the MIMO autoencoder, the receiver then recovers each transmitter's data from their fusion version in parallel, and the proposed SMA scheme can be optimized using the end-to-end method. Simulation results demonstrate that the proposed OA-MIMO-AE-based SMA scheme achieves accurate transmission with a lower communication cost than existing schemes such as OFDMA and NOMA.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems enable extraordinarily high data rates and necessitate precise channel state information (CSI) for effective beamforming. To explore the structural sparsity feature, we propose an advanced low-rank matrix factorization technique for channel estimation. Our algorithm treats channel estimation as a problem of low-rank matrix completion by exploiting the simultaneous sparsity and low-rank nature of the channel. First, we decompose the channel matrix into two low-rank factor matrices, refining them through projected gradient descent to satisfy the constraints of low-rankness and sparsity simultaneously. For improved results, we introduce a deep unfolding network, named Deep Unfolding Low-Rank Factorization Net for Channel Estimation (DULRF-CE), which integrates the low-rank matrix factorization model into the neural network design. This network includes a module capable of nonlinear sparse transformations and a denoising module, giving it the ability to learn a more accurate low-rank decomposition and a sparser channel representation. Our experimental results show that the DULRF-CE algorithm provides a significant advance, offering up to 4.3 dB improvement in average normalized mean square error performance over existing matrix-completion-based channel estimation techniques in various measurement scenarios.

Pilot pattern optimization in orthogonal frequency division multiplexing (OFDM) systems has been widely investigated due to its positive impact on channel estimation. In this paper, we consider the problem of multi-user pilot pattern optimization in OFDM systems. In particular, the goal is to enhance channel extrapolation performance for 5G NR systems by optimizing multi-user pilot patterns in frequency-domain. We formulate a novel pilot pattern optimization problem with the objective of minimizing the maximum integrated side-lobe level (ISL) among all users, subject to a statistical resolution limit (SRL) constraint. Unlike existing literature that only utilizes ISL for controlling side-lobe levels of the ambiguity function, we also leverage ISL to mitigate multi-user interference in code-domain. Additionally, the introduced SRL constraint ensures sufficient delay resolution of the system to resolve multipath, thereby improving channel extrapolation performance. Then, we employ the estimation of distribution algorithm (EDA) to solve the formulated problem in an offline manner. Simulation results demonstrate that the optimized pilot pattern yields significant performance gains in channel extrapolation over the conventional pilot patterns.

In the emerging extended reality (XR) applications, downlink transmission struggles with tricky packet dropout issues caused by large-sized data packets and hard-latency constraints, usually requiring significant reliance on transmission power resources for support. This paper proposes an accelerated constrained reinforcement learning (ACRL)-based energy-efficient power scheduling (EEPS) algorithm for XR transmission to ensure a satisfactory packet dropout rate for each user with minimal power consumption. Based on a actorcritic framework, the proposed method employs a policy network to make energy-efficient power scheduling decisions online, with the critic module evaluating it through interactions with the environment, and the actor module optimizing it using the constrained stochastic successive convex approximation (CSSCA) method, which is particularly suitable for addressing non-convex stochastic constraints related to packet dropout rates. Moreover, we integrate a transfer learning method, policy reuse, into the actor module and apply a signal-reshaping mechanism to transform sparse delayed packet dropout signals into dense signals. Both techniques considerably accelerate the convergence speed of traditional constrained reinforcement learning (CRL) algorithms in XR scenarios. Simulation results demonstrate that the proposed ACRL-EEPS algorithm outperforms advanced baselines in both power conservation and meeting packet dropout constraints.

We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where nearfield propagation and spatial non-stationary effects exist. In this case, each scatterer can only see a subset of antennas, i.e., it has a certain VR over the antennas. A novel alternating maximum a posteriori (MAP) framework is developed for high-accuracy VR detection and channel estimation, which consists of three basic modules: a channel estimation module, a VR detection module, and a grid update module. Specifically, the first module is a low-complexity inverse-free variational Bayesian inference (IF-VBI) algorithm that avoids the matrix inverse via minimizing a relaxed Kullback-Leibler (KL) divergence. The second module is an expectation propagation (EP) algorithm that can recover binary VRs. And the third module refines polar-domain grid parameters via gradient ascent. Simulations demonstrate the superiority of the proposed algorithm in both VR detection and channel estimation.

In the forthcoming 6G era, extend reality (XR) has been regarded as an emerging application for ultra-reliable and low latency communications (URLLC) with new traffic characteristics and more stringent requirements. In addition to the quasi-periodical traffic in XR, burst traffic with both large frame size and random arrivals in some real world low latency communication scenarios has become the leading cause of network congestion or even collapse, and there still lacks an efficient algorithm for the resource scheduling problem under burst traffic with hard latency constraints. We propose a policy reuse reinforcement learning framework for resource scheduling with hard latency constraints (PRRL-RSHLC), which maximizes the hard-latency constrained effective throughout (HLC-ET) of users. The proposed algorithm reuses polices from both old policies learned under other similar environments and domain-knowledge-based (DK) policies constructed using expert knowledge to improve the performance. Simulations show that PRRL-RSHLC can achieve superior performance with faster convergence speed compared to baseline aIgorithms.

This paper considers an intelligent unmanned cluster system, where edge nodes need to communicate with a cluster head to collaborate on certain tasks, posing higher requirements for accurate channel estimation and localization of edge nodes. To achieve this, we propose a joint channel estimation and cooperative localization scheme by fusing the pilot signals and location prior information (LPI). In particular, we propose a mixed location-delay domain channel model, where the dominant channel paths are modeled using the nodes' locations, while the others are modeled using a delay-domain sparse channel prior. In this way, LPI is directly exploited to achieve high-accuracy channel estimation with limited pilot overhead. In turn, the more accurate channel estimation results are exploited to achieve cooperative localization among nodes. By combining the turbo approach and the expectation-maximization (EM) method, we propose a LPI-aided EM-based turbo compressive sensing (EM-Turbo-CS) algorithm that updates the location-delay domain channel coefficients and location/delay parameters alternately to achieve high-precision channel estimation and node localization with limited pilot overhead. Finally, the feasibility and superiority of the proposed scheme are verified through simulations.

A novel hard-latency guaranteed cluster-free multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) framework is proposed to deal with burst traffics that commonly occur in real-world scenarios. The hard-latency constrained effective throughput (HLC-ET) maximization problem is formulated, which jointly optimizes the beamforming and cluster-free success interference cancellation (SIC) operations. To address the resultant problem, a two-stage reinforcement learning (RL)-based algorithm is developed to capture system uncertainty, where the large-dimension optimization is decoupled into two stages to reduce the action space and fasten convergence of RL. In the long-term stage, we aim to maximize the HLC-ET, and a hybrid RL algorithm with policy reuse is adoped to control the priority weights to construct the weighted sum rate (WSR) function of users. In the short-term stage, a branch-and-bound (BB) based algorithm is further developed to obtain the optimal solution of the WSR maximization problem. The BB-based algorithm is proved to guarantee the convergence to an ϵ-optimal solution of the WSR maximization problem within a finite number of steps. To accelerate computation in the short-term stage, a channel correlation based two-loop greedy (CC-TLG) algorithm is proposed to significantly reduce the complexity with almost no performance loss compared to the BB-based algorithm. Finally, simulations demonstrate the advantages of the proposed two-stage RL based joint beamforming and SIC optimization (TSRL-JBSO) algorithm over conventional RL-based and non-RL based algorithms.

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results.

Pilot pattern optimization in orthogonal frequency division multiplexing (OFDM) systems has been widely investigated due to its positive impact on channel estimation. In this paper, we consider the problem of multi-user pilot pattern optimization for OFDM systems. In particular, the goal is to enhance channel extrapolation performance for 5G NR systems by optimizing multi-user pilot patterns in frequency-domain. We formulate a novel pilot pattern optimization problem with the objective of minimizing the maximum integrated side-lobe level (ISL) among all users, subject to a statistical resolution limit (SRL) constraint. Unlike existing literature that only utilizes ISL for controlling side-lobe levels of the ambiguity function, we also leverage ISL to mitigate multi-user interference in code-domain multiplexing. Additionally, the introduced SRL constraint ensures sufficient delay resolution of the system to resolve multipath, thereby improving channel extrapolation performance. Then, we employ the estimation of distribution algorithm (EDA) to solve the formulated problem in an offline manner. Finally, we extend the formulated multi-user pilot pattern optimization problem to a multiband scenario, in which multiband gains can be exploited to improve channel extrapolation performance. Simulation results demonstrate that the optimized pilot pattern yields significant performance gains in channel extrapolation over the conventional pilot patterns.

For many practical applications in wireless communications, we need to recover a structured sparse signal from a linear observation model with dynamic grid parameters in the sensing matrix. Conventional expectation maximization (EM)-based compressed sensing (CS) methods, such as turbo compressed sensing (Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have double-loop iterations, where the inner loop (E-step) obtains a Bayesian estimation of sparse signals and the outer loop (M-step) obtains a point estimation of dynamic grid parameters. This leads to a slow convergence rate. Furthermore, each iteration of the E-step involves a complicated matrix inverse in general. To overcome these drawbacks, we first propose a successive linear approximation VBI (SLA-VBI) algorithm that can provide Bayesian estimation of both sparse signals and dynamic grid parameters. Besides, we simplify the matrix inverse operation based on the majorization-minimization (MM) algorithmic framework. In addition, we extend our proposed algorithm from an independent sparse prior to more complicated structured sparse priors, which can exploit structured sparsity in specific applications to further enhance the performance. Finally, we apply our proposed algorithm to solve two practical application problems in wireless communications and verify that the proposed algorithm can achieve faster convergence, lower complexity, and better performance compared to the state-of-the-art EM-based methods.

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a backhaul-limited multiple access channel (MAC) for cooperative localization of the target. Different from schemes on only information domain or signal domain cooperation, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the backhaul resource allocation, together with a greedy strategy for node selection. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to outperform the baseline schemes significantly.

This paper presents an innovative downlink transmission power scheduling (TPS) scheme for the emerging delay-sensitive applications in the 6G era, focusing on enhancing the transmission efficiency while ensuring a high quality of service (QoS) for each user. Specifically, we first adopt the hard-delay constrained effective throughput and users’ packet dropout rates as performance metrics and formulate the TPS problem as a constrained Markov Decision Process (CMDP), which appropriately characterizes the requirements of delay-sensitive applications. Then, we propose a novel constrained deep Actor-Critic-based TPS (CDAC-TPS) algorithm, which can dynamically make TPS decisions by a policy network in its Actor module and evaluate the current policy by Q-networks in its Critic module without any prior information of the environment. In particular, the CDAC-TPS adopts a constrained stochastic successive convex approximation (CSSCA) method to optimize the policy network, which can better handle the stochastic non-convex objective and constraints in the TPS problem, while most of the existing policy optimization methods for CMDP are only suitable for simple convex constraints. Finally, simulation results demonstrate that the proposed TPS scheme outperforms baselines in both transmission efficiency and QoS guarantee.

The integration of radar sensing into communication networks for supporting various applications is an emerging area of interest in the field of integrated sensing and communication. In this paper, we propose a two-stage sensing-assisted channel extrapolation scheme for time-division duplex (TDD) massive MIMO-OFDM systems, utilizing radar-sensed scattering environment information (SEI) to enhance channel extrapolation performance. In Stage 1, user transmits uplink pilots (UPs) on a bandwidth part (BWP) that occupies only a fraction of the system bandwidth, for the base station (BS) to estimate the angle parameters through the proposed space-time multiple signal classification (ST-MUSIC) algorithm. However, it is difficult to achieve accurate channel extrapolation on the other BWPs because the delay resolution of a single BWP is limited. In Stage 2, the BS transmits full-band downlink pilots (DPs) aligned with the angles estimated in stage 1, and then performs joint scattering environment sensing and channel extrapolation based on the received UPs and reflected DPs. The EM-Turbo-CS algorithm is proposed for Stage 2 to exploit the SEI extracted from the full-band reflected DPs to dramatically improve the delay estimation accuracy, resulting in high-accuracy channel extrapolation. Meanwhile, a coarse estimation using the ST-MUSIC algorithm on the received signals helps reduce the complexity of the EM-Turbo-CS algorithm. Simulation results demonstrate that the proposed solution significantly improves the channel extrapolation performance.

Future wireless systems are supposed to provide high-resolution sensing services via communication signals. Under this background, multiband sensing has recently become a promising technology due to that it can improve the sensing performance by jointly utilizing multiple non-contiguous frequency bands at a low cost. However, few studies have investigated the fundamental limits of multiband sensing, especially in the presence of phase distortion factors. In this paper, we investigate the fundamental limits of multiband sensing in terms of time delay. We derive a closed-form expression of the Cramér-Ran bound (CRB) for the delay separation to reveal useful insights. Additionally, a metric called the statistical resolution limit (SRL) is employed to investigate the fundamental limits of delay resolution. The fundamental limits of delay estimation error are also investigated based on the CRB and Ziv-Zakai bound (ZZB). Based on the above derived fundamental limits, numerical results are presented to provide key insights on the performance limits of the multiband sensing.

Multiband fusion enhances delay estimation by jointly utilizing signals from multiple noncontiguous frequency bands. However, in the multiband signal model, there are many local optimums in the associated likelihood function due to the existence of high-frequency component and phase distortion factors, posing challenges for high-accuracy parameter estimation. To address this, we propose a two-stage scheme equipped with different signal models derived from the original model, where the first-stage coarse estimation is performed using a weighted root MUSIC algorithm to narrow down the search range for the subsequent stage, and the second-stage refined estimation utilizes a Bayesian approach to avoid convergence to bad suboptimal solutions. Specifically, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm in the refined stage. Unlike conventional particle-based VBI (PVBI) that optimizes only particle probability and incurs exponential per-iteration complexity with particle count, our more flexible SPVBI algorithm optimizes both the position and probability of each particle. Additionally, it utilizes block SSCA to significantly improve sampling efficiency by averaging over iterations, making it suitable for high-dimensional problems. Extensive simulations demonstrate the superiority of our proposed algorithm over various baseline methods.

Multiband technology has recently received incremental attention for its ability to jointly utilize multiple noncontiguous frequency bands to achieve high-resolution delay estimation. In multiband scenarios, numerous signal processing algorithms for delay estimation have been proposed, while research on the fundamental limits remains under explored. In this article, we focus on the analysis of fundamental limits and the optimization of multiband delay estimation in orthogonal frequency division multiplexing (OFDM) systems. We derive a closed-form expression of the Cramer-Rao bound (CRB) for the delay separation to reveal useful insights. Additionally, a metric called statistical resolution limit (SRL) that provides a resolution performance bound is employed to research the fundamental limits of delay resolution. The fundamental limits of the delay estimation error are also investigated using the performance bounds CRB and Ziv-Zakai bound (ZZB). Based on these derived performance bounds, numerical results have been presented to analyse the effect of frequency band apertures and phase distortions on the fundamental limits of the multiband delay estimation error. Inspired by the analysis of fundamental limits, we formulate an optimization problem to find the optimal system configuration in multiband systems with the objective of minimizing the delay SRL. To solve this nonconvex constrained problem, we propose an efficient alternating optimization (AO)-based algorithm that iteratively optimizes the variables using the successive convex approximation (SCA) and 1-D search. Simulation results demonstrate the effectiveness of the proposed algorithm and give useful insights for the multiband system design.

Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and leveraging prior information, but poses a long-standing challenge on computing intractable posterior distributions. Most existing variational methods depend on specific distribution assumptions for obtaining closed-form solutions, and are difficult to apply in practical scenarios. Given these challenges, firstly, we propose a parallel stochastic particle VBI (PSPVBI) algorithm. Due to innovations like particle approximation, added updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posterior distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding this algorithm. Specifically, superior hyperparameters are learned to dramatically reduce iterations. In this PSPVBI-induced deep-unfolding network, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the learnable PSPVBI (LPSPVBI) to solve two important positioning/sensing problems over wireless networks. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.

We focus on visible light communication-based simultaneous position and orientation detection (SPAO) for user devices (UDs) using photodiodes, which is challenging due to scattering interference and small-scale fading. To address this challenge, a novel SPAO approach is proposed, which can jointly estimate UD location parameters and scattering channel states. As such, the disturbance of diffuse scattering and random fading on SPAO will be alleviated via scattering channel equalization. In addition, SPAO is non-convex in nature, and hence brute-force application of conventional optimization methods will lead to a poor SPAO solution. To address this issue, we devise a majorization minimization (MM)-based SPAO algorithm, where hidden convex structure of the non-convex SPAO problem is exploited, which renders an efficient closed-form iteration rule for joint SPAO and diffuse channel estimation. Due to the cross-layer cooperation between “VLC” and “ranging”, a robust SPAO solution against diffuse scattering and small-scale fading is achieved. It is corroborated by our simulations that the proposed MM-based SPAO algorithm achieves a large performance gain over state-of-the-art baseline methods.

In this paper, we present a novel hybrid precoding scheme, named group-and-codebook based two-timescale hybrid precoding (GC-THP), for reducing complexity in millimeter wave massive MIMO systems. The scheme clusters users into groups based on their statistical similarity and selects analog beams from a codebook with orthogonal beams. The base station time-shares among multiple analog precoders, each serving a subset of user groups, for fairness. At the slow timescale, the analog precoders, together with their corresponding group scheduling vectors and time-sharing factors, are jointly optimized, while at the fast timescale, short-term user scheduling and digital precoding are performed for a given analog precoder and its associated group scheduling vector. The proposed scheme employs beam-domain channel statistics with reduced dimension to optimize the proportional fairness utility. By applying user grouping and codebook based analog precoding, the proposed scheme significantly reduces complexity while retaining high performance levels. Moreover, the implementation complexity is tunable by adjusting the codebook size and the number of user groups, allowing for flexible performance-complexity tradeoffs. Simulation results confirm a superior performance-complexity tradeoff for the proposed GC-THP in comparison to the existing hybrid precoding schemes.

Recently, channel extrapolation has been widely investigated in frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. However, in time-division duplex (TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation problem also arises due to the hopping uplink pilot pattern, which has not been fully researched yet. This paper addresses this gap by formulating a channel extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating imperfection factors. A novel two-stage two-dimensional (2D) channel extrapolation scheme in frequency-time domain is proposed, designed to mitigate the effects of imperfection factors and ensure high-accuracy channel estimation. Specifically, in the channel estimation stage, we propose a novel multi-band multi-timeslot based high-resolution parameter estimation algorithm to achieve 2D channel extrapolation in the presence of imperfection factors. Then, to avoid repeated multi-timeslot channel estimation, a channel tracking stage is designed during the subsequent time instants, where a sparse Markov channel model is formulated to capture the dynamic sparsity of massive MIMO-OFDM channels under the influence of imperfection factors. Next, an expectation-maximization (EM) based compressive channel tracking algorithm is designed to estimate unknown imperfection and channel parameters by exploiting the high-resolution prior information of the delay/angle parameters from previous timeslots. Simulation results underscore the superior performance of our proposed channel extrapolation scheme over baselines.

Recently, massive multiple input multiple output (MIMO) channel tracking in the fifth generation (5G) new radio (NR) systems has attracted intensive interest. By exploiting the dynamic sparsity of massive MIMO channels, it is possible to design a high-accuracy channel tracking scheme. However, the existing channel estimation/tracking algorithms often ignore the practical imperfections in real systems, such as the channel aging effect due to the hopping Sounding Reference Signal (SRS) pattern, the time offset, phase noise, and multi-user SRS interference. In this paper, we propose a robust multi-user uplink channel tracking scheme, which is compatible with 5G NR systems and robust against various system imperfections. Specifically, we propose a sparse Markov channel model to capture the dynamic sparsity of massive MIMO-OFDM channels under the consideration of imperfect factors. Then, we propose a robust multi-user channel tracking scheme, which iterates between two components until convergence. Particularly, in thechannel estimation component, we employ the Turbo-CS method to exploit the channel dynamic sparsity to perform efficient multi-user channel estimation under non-orthogonal SRSs, where a multi-stage successive interference cancellation (MSIC) scheme is proposed to mitigate the multi-user SRS interference. Then, in theimperfection parameter estimation component, we further estimate the unknown imperfection parameters based on the updated Bayesian channel estimates from thechannel estimation component. Simulation results verify the superior channel tracking performance of our proposed scheme over the baselines.

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.

Most existing downlink channel estimation methods rely on channel sparsity in the angular domain to reduce pilot overhead for massive multiple-input multiple-output (MIMO) systems. Compared with channel estimation methods based on predefined basis or offline dictionary learning, in this paper, we design an online two-timescale joint uplink/downlink dictionary learning and channel estimation (TTS-JDLCE) algorithm for dynamic scenarios, where the channel sparsity and angle reciprocity between uplink and downlink transmissions are both exploited to reduce the pilot overhead. The downlink channel estimation is constructed as a TTS stochastic optimization problem with a constraint coupled by the long-term dictionary and short-term sparse channel representations. Treating the dictionary as a learnable parameter, the proposed algorithm can capture dynamic spatial information for enhancing performance. By introducing a relaxed TTS primal-dual decomposition (PDD) framework, the original problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems. Besides, the deep unfolding technique is employed to extract gradient information from short-term problems, which circumvents the non-closed form and non-convexity of long-term subproblem by constructing a convex surrogate problem. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gains over various baselines.

In integrated sensing and communication (ISAC) systems, communication signals are exploited to achieve high-accuracy sensing. Multiband Wi-Fi sensing, which jointly utilizes Wi-Fi signals from multiple non-contiguous frequency bands to improve the sensing performance, has recently emerged as a promising technology for ISAC. However, the multi-dimensional non-convex likelihood function associated with the multiband WiFi sensing contains many local optimums due to the existence of high frequency components and phase distortion factors in the signal model, making it difficult to exploit the multiband gain for high-accuracy parameter estimation. To address this, we divide the target parameter estimation into two stages equipped with different signal models derived from the original model, where the first-stage coarse estimation is used to narrow down the search range for the next stage, and the second-stage refined estimation is based on the Bayesian approach to avoid the convergence to a bad local optimum of the likelihood function. Specifically, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm in the refined stage. Unlike the conventional particle-based VBI (PVBI) in which only particle probability is optimized and the per-iteration computational complexity increases exponentially with particle count, the proposed SPVBI optimizes both the position and probability of each particle, and it adopts the block SSCA to significantly improve the sampling efficiency by averaging over iterations. As such, the proposed SPVBI can achieve a better performance than the conventional PVBI with a much lower complexity. Finally, simulations verify the advantage of the proposed algorithm over various baseline algorithms.

Integrated sensing and communication (ISAC) is a promising technology for future 6G networks that enables the joint utilization of hardware and spectrum resources for sensing and communication systems. However, the co-sharing of resources leads to a fundamental tradeoff between sensing and communication performance, which is not well understood in multiple-access ISAC scenarios with perfect or imperfect channel state information at the receiver (CSIR). In this paper, we address this challenge by considering a state-dependent multiple access channel model that accounts for correlated sensing and channel states, as well as imperfect CSIR. We propose an achievable scheme that combines message cooperation and joint compression via distributed Wyner-Ziv coding at each user, resulting in unified cooperative communication and sensing. Our scheme always achieves a communication-rate-distortion region which includes that achieved by state-of-the-art coding scheme. In addition, a numerical example is provided to demonstrate strict inclusion. It is found that the compressed information not only enhances communication (especially in scenarios with imperfect CSIR) but also improves sensing performance.

Integrated sensing and communication (ISAC) has emerged as a key technology for future communication systems. In this paper, we provide a general framework to reveal the fundamental tradeoff between sensing and communication in OFDM systems, where a unified ISAC waveform is exploited to perform both tasks. In particular, we define the Capacity-Bayesian Cramer Rao Bound (BCRB) region in the asymptotical case when the number of subcarriers is large. Specifically, we show that the asymptotical optimal input distribution that achieves the Pareto boundary point of the Capacity-BCRB region is Gaussian and the entire Pareto boundary can be obtained by solving a convex power allocation problem. Moreover, we characterize the structure of the sensing-optimal power allocation in the asymptotical case. Finally, numerical simulations are conducted to verify the theoretical analysis and provide useful insights.

In this paper, we consider a cooperative radar sensing system, where a radar transmitter sends a sensing signal to track a target, several radar receivers receive the echo signal from the target and send local processing results to a fusion center (FC) for cooperative localization. Different from existing researches on information domain or signal domain cooperation, we propose a novel hybrid information-signal domain cooperative radar sensing (HISCRS) framework. In the proposed HISCRS framework, the FC not only collects the extracted location information such as the time delays from the radar receivers, but also obtains a fused version of the echo signals from different radar receivers via a deep over-the-air computation (AirComp) scheme, which can fully exploit the correlations among the echo signals to significantly reduce the communication cost. Specifically, information domain features, i.e., the time delays, are used to obtain a coarse location of the target and signal domain features, i.e., the fused echo signals, are used to generate a location offset to further improve the localization accuracy. Simulations results show that the proposed HISCRS framework is able to achieve a notable gain over the existing cooperative sensing schemes with reduced communication cost.

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a partially common sparsity. By exploiting this, we propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously. Specifically, the base station (BS) first transmits downlink pilots to sense the targets in the scattering environment. Then the user transmits uplink pilots to estimate the communication channel. Finally, joint scattering environment sensing and channel estimation are performed at the BS based on the reflected downlink pilot signal and received uplink pilot signal. A message passing based algorithm is designed by combining the turbo approach and the expectation maximization method. The advantages of our proposed scheme are verified in the simulations.

In massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detection (AD) and channel estimation (CE) problems in massive-connected communication systems with low-resolution analog-to-digital converters. To further exploit the sparse structure in transmission, we propose a maximum posterior probability (MAP) estimation problem based on both sporadic activity and sparse channels for joint AD and CE. Moreover, a majorization-minimization-based method is proposed for solving the MAP problem. Finally, various numerical experiments verify that the proposed scheme outperforms state-of-the-art methods.

The paper presents a novel hybrid precoding scheme, named group-and-codebook based two-timescale hybrid precoding (GC-THP), for millimeter wave (mmWave) massive MIMO Systems. This scheme aims to reduce complexity by clustering users into groups based on their statistical similarity and selecting analog beams from a codebook containing orthogonal beams. To achieve fairness, the base station (BS) time-shares among multiple analog precoders, each serving a subset of user groups. At the slow timescale, the analog precoders, together with their corresponding group scheduling vectors and time-sharing factors, are jointly optimized, while at the fast timescale, short-term user scheduling and digital precoding are performed for a given analog precoder and its associated group scheduling vector. The proposed scheme uses beam-domain channel statistics with reduced dimension to maximize the proportional fairness (PF) utility. By applying user grouping and codebook based analog precoding, the proposed scheme significantly reduces complexity while retaining high performance levels. Furthermore, the implementation complexity is tunable by adjusting the codebook size and the number of user groups, allowing for flexible performance-complexity trade-offs. Simulation results confirm a superior performance-complexity trade-off for the GC-THP than existing hybrid precoding schemes.

Multi-user MIMO (MU-MIMO) enables a base station (BS) to transmit data streams to multiple users simultaneously on the same resource block (RB). In 5G specifications, a resource scheduling algorithm needs to consider both resource allocation in the spatial domain (i.e., MU-MIMO user scheduling on each RB) and frequency domain (i.e., RB allocation to each user). In addition, the scheduler must meet the real-time requirement. This paper presents a random sampling based joint spatial-frequency domain message passing (RS-JSFD-MP) algorithm, which is a novel radio resource scheduler that can meet the real-time requirement under the consideration of the finite buffer traffic scenario. The key idea of RS-JSFD-MP is to transform the scheduling problem into a graph model and design the corresponding low complexity message passing algorithm based on the random sampling approach. Experimental results show that RS-JSFD-MP can achieve better scheduling performance than existing greedy baseline algorithm, and can also obtain lower complexity by choosing an appropriate number of random samples. Moreover, RS-JSFD-MP facilitates parallel and distributed implementation, which helps to accelerate computation time to meet the real-time requirement on the resource scheduling algorithm.

Federated learning (FL) is an edge learning framework that has received significant attention recently. However, the cost of communication has become a major challenge for FL as the number of edge devices grows and the complexity of training models increases. Besides, data samples across all edge devices are usually not independent and identically distributed (non-IID), posing additional challenges to the convergence and model accuracy of FL. Therefore, we propose a novel personalized FL framework based on deep coded over-the-air computation, named DipFL. In this framework, we design a deep AirComp aggregation (DACA) module for n-to-1 information aggregation. Besides, a joint source-channel coding (JSCC) module is designed based on the variational auto-encoder (VAE) model, which not only encodes the transmitted data, but also reduces the bias of local samples by introducing certain regularisation terms. In addition, we propose a personalized mix module that allows local models to be more personalized by mixing the global model and the local models. Simulation results confirm that the proposed DipFL framework is able to significantly reduce the amount of transmitted data, while improving FL performance especially at low signal-to-noise regimes.

This paper introduces a cooperative sensing framework designed for integrated sensing and communication cellular networks. The framework comprises one base station (BS) functioning as the sensing transmitter, while several nearby BSs act as sensing receivers. The primary objective is to facilitate cooperative target localization by enabling each receiver to share specific information with a fusion center (FC) over a limited capacity backhaul link. To achieve this goal, we propose an advanced cooperative sensing design that enhances the communication process between the receivers and the FC. Each receiver independently estimates the time delay and the reflecting coefficient associated with the reflected path from the target. Subsequently, each receiver transmits the estimated values and the received signal samples centered around the estimated time delay to the FC. To efficiently quantize the signal samples, a Karhunen-Loève Transform coding scheme is employed. Furthermore, an optimization problem is formulated to allocate backhaul resources for quantizing different samples, improving target localization. Numerical results validate the effectiveness of our proposed advanced design and demonstrate its superiority over a baseline design, where only the locally estimated values are transmitted from each receiver to the FC.

Cooperative localization is a promising technique in wireless networks, and neighbor selection (NS) is essential to limit the degree of cooperation and reduce the amount of data to be exchanged. However, the existing NS algorithms may suffer from major performance loss when applied to networks with limited resources (e.g., bandwidth, time and energy). In this paper, we establish a general optimization framework for the NS problem to minimize the localization error under strict resource constraints. Based on the squared position error bound (SPEB) criterion, we formulate two distributed NS problems under implicit and explicit energy constraints, respectively, to balance the energy consumption of the network, where implicit energy constraints mean that specific energy profiles of the nodes’ neighbors are unavailable while explicit energy constraints mean the opposite. Moreover, we propose to jointly optimize the NS and power allocation in the explicit case to further improve the localization performance. The resulting problems are challenging to solve due to the nonlinear objective functions and discrete optimization variables. We first transform them into more tractable forms and then develop novel algorithms based on the penalty dual decomposition method to solve the transformed problems efficiently. Simulation results show that the proposed algorithms can significantly outperform benchmark algorithms. In particular, the proposed algorithm almost achieves the performance lower bound in the implicit case.

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

For intelligent reflecting surface (IRS)-aided wireless communications, channel estimation is essential and usually requires excessive channel training overhead when the number of IRS reflecting elements is large. The acquisition of accurate channel state information (CSI) becomes more challenging when the channel is not quasi-static due to the mobility of the transmitter and/or receiver. In this work, we study an IRS-aided wireless communication system with a practical channel model that characterizes the time-varying propagation property and propose an innovative two-stage transmission protocol. In the first stage, we send pilot symbols and track the direct/reflected channels based on the received signal, and then data signals are transmitted. In the second stage, instead of sending pilot symbols first, we directly predict the direct/reflected channels and all the time slots are used for data transmission. Based on the proposed transmission protocol, we propose a two-stage channel tracking and prediction (2SCTP) scheme to obtain the direct and reflected channels with low channel training overhead, which is achieved by exploiting the temporal correlation of the time-varying channels. Specifically, we first consider a special case where the IRS-access point (AP) channel is assumed to be static, for which a Kalman filter (KF)-based algorithm and a long short-term memory (LSTM)-based neural network are proposed for channel tracking and prediction, respectively. Then, for the more general case where the IRS-AP, user-IRS and user-AP channels are all assumed to be time-varying, we present a generalized KF (GKF)-based channel tracking algorithm, where proper approximations are employed to handle the underlying non-Gaussian random variables. Numerical simulations are provided to verify the effectiveness of our proposed transmission protocol and channel tracking/prediction algorithms as compared to existing ones.

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

In this paper, we design a two-timescale approach for joint uplink/downlink (UL/DL) dictionary learning and channel estimation (TTS-JDLCE) for frequency division multiplexing (FDD) massive multiple-input multiple-output (MIMO) systems in dynamic scenarios. With channel sparsity and angle reciprocity between UL and DL transmissions, the joint UL/DL dictionary is regarded as a learnable parameter to capture dynamic spatial information at a slower timescale than instantaneous downlink channel estimation. By introducing the primal-dual decomposition (PDD) framework, the original non-convex two-timescale stochastic optimization problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems, and then solved in a divide-and-conquer manner, which can converge to the stationary solutions of the original problem over time. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gain over various baselines.

In this paper, we consider a reinforcement learning (RL) based multi-user downlink communication system. An actor-critic based deep channel prediction (CP) algorithm is proposed at the base station (BS) where the actor network directly outputs the predicted CSI without channel reciprocity. Different from the existing methods which either require the perfect channel state information (CSI), or estimate outdated CSI and set strict constraints on pilot sequences, the proposed algorithm has no such premised knowledge requirements or constraints. Deep-Q learning and policy gradient methods are adopted to update the parameters of the proposed prediction network, with the objective of maximizing the overall transmission sum rate. Numerical simulation results and the complexity analysis verify that the proposed CP algorithm could beat the existing traditional and learning based methods in terms of sum rate over different channel models and different numbers of users and antennas.

The rapidly developing energy harvesting (EH) technology is a promising solution to the durability issue in the battery-powered Internet of Things (IoT) systems. In this article, underlaid device-to-device (D2D) transmission powered by radio signals harvested from cellular systems is studied. By considering the dilemmas among EH, D2D transmission opportunity, and interference management, we propose two transmission policies: 1) Policy 1 requires that the available power in the battery should be no less than the D2D transmission power and 2) Policy 2 not only sets the constraint on available power but also introduces theguard zonerule to protect D2D transmissions from severe interference. The employment of aguard zonein this situation is technically challenging since the original distribution of energy arrival will thereby be changed. We derive expressions in closed or semiclosed forms for the considered D2D transmission performance metrics with the stochastic geometry framework and Poisson hole process. With numerical simulation results, the influences of varying network parameters on D2D performances are illustrated. The results show that by introducing a guard zone, the D2D successful transmission rate can be increased by 41.2%. All the developed D2D frameworks and the summarized useful remarks are used to provide meaningful design insights and guidelines for the deployment strategies of EH-based D2D wireless networks.

Great concerns on the Internet of Things (IoT) security are raised as IoT becomes an emerging paradigm to achieve ubiquitous connectivity. This article studies low-complexity secure transceiver and feedback design with the assistance of a jammer for physical-layer security in multiantenna IoT systems. We consider the general setting where a legitimate multiantenna controller broadcasts confidential messages to multiple multiantenna IoT devices in the presence of a passive external multiantenna eavesdropper. Moreover, there is only quantized downlink channel state information (CSI) at the controller and the jammer through feedback channels. We introduce several secure transceivers for different system setups, all of which employ block-diagonal precoding at the controller and null-space beamforming at the jammer but are with the different receivers at each IoT device. Considering the practical setup of IoT and for the tractability of analysis, we study the secrecy performance of the transceiver with an arbitrarily selected receive matrix independent of the channels of all devices. We derive an approximate lower bound on the ergodic secrecy rate (ESR) of each devicewithoutassuming any asymptotes for system parameters. The obtained result can also be viewed as a lower bound on the ESR performance of any other transceivers. We also optimize this bound to find an adaptive feedback bit allocation to the two feedback channels of each legitimate device. Numerical results are shown to illustrate the obtained analytical ESR lower bound, the feedback bit allocation algorithm, and significant ESR performance gain that results from the proposed feedback bit allocation.

In this paper, reinforcement learning (RL) based end-to-end channel prediction (CP) and beamforming (BF) algorithms are proposed for multi-user downlink system. Different from the previous methods which either require perfect channel state information (CSI), or estimate outdated CSI and set constraints on pilot sequences, the proposed algorithms have no such premised assumptions or constraints. Firstly, RL is considered in channel prediction and the actor-critic aided CP algorithm is proposed at the base station (BS). With the received pilot signals and partial feedback information, the actor network at BS directly outputs the predicted downlink CSI without channel reciprocity. After obtaining the CSI, BS generates the beamforming matrix using zero-forcing (ZF). Secondly, we further develop a deep RL based two-layer architecture for joint CP and BF design. The first layer predicts the downlink CSI with the similar actor network as in the CP algorithm. Then, by importing the outputs of the first layer as inputs, the second layer is the actor-critic based beamforming layer, which can autonomously learn the beamforming policy with the objective of maximizing the transmission sum rate. Since the learning state and action spaces in the considered CP and BF problems are continuous, we employ the actor-critic method to deal with the continuous outputs. Empirical numerical simulations and the complexity analysis verify that the proposed end-to-end algorithms could always converge to stable states under different channel statistics and scenarios, and can beat the existing traditional and learning based benchmarks, in terms of transmission sum rate.

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

In massive multiple-input multiple-output (MIMO) systems employing hybrid analog-digital precoder, there are two commonly used antenna structures that have their own pros and cons, namely, the fully-connected antenna structure (FCAS) and the partially-connected antenna structure (PCAS). The FCAS achieves better spectrum efficiency (SE) even with reduced radio frequency (RF) chains, but the hardware cost and power consumption are still grievous. Contrarily, the PCAS has less hardware cost and power consumption, but suffers from severe performance loss. In this paper, by combining the advantages of both structures, we first propose a sparse adaptive antenna structure (SAAS) for the implementation of the hybrid precoder, which can jointly control the on/off state of all phase shifters (PS) and RF chains through a switch network. Then, a sparse hybrid precoding (SHP) optimization problem based on the proposed SAAS is established aiming at minimizing the total power consumption under individual average data rate requirements. To tackle the challenging non-smooth non-convex stochastic optimization (NSO) emerged with the SHP design and reduce the power consumption of PSs and RF chains, we propose a sparse smooth approximation based an online algorithm to find a stationary point of the NSO problem and establish its convergence. Simulations verify that the proposed antenna structure and algorithm achieve a better balance between power consumption and system throughput than the existing schemes.

We investigate the downlink transmission for multi-user multi-input multi-out (MU-MIMO) system, in which the regularized zero forcing (RZF) precoder is adopted and the power allocation and regularization factor are optimized. Our aim is to find a power allocation and regularization factor control policy that can minimize the long-term average power consumption subject to long-term delay constraint for each user. The induced optimization problem is formulated as a constrained Markov decision process (CMDP), which is efficiently solved by the proposed constrained deep reinforcement learning algorithm, called successive convex approximation policy optimization (SCAPO). The SCAPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problems with convex surrogate functions. At each iteration, the SCAPO merely needs to estimate the first-order information and solve a convex surrogate problem that can be efficiently parallel tackled. Moreover, the SCAPO enables to reuse old experiences from previous updates, thereby significantly reducing the implementation cost. Numerical results have shown that the novel SCAPO can achieve the state-of-the-art performance over advanced baselines.

This article investigates the analog gradient aggregation (AGA) solution to overcome the communication bottleneck for wireless federated learning applications by exploiting the idea of analog over-the-air transmission. Despite the various advantages, this special transmission solution also brings new challenges to both transceiver design and learning algorithm design due to the nonstationary local gradients and the time-varying wireless channels in different communication rounds. To address these issues, we propose a novel design of both the transceiver and learning algorithm for the AGA solution. In particular, the parameters in the transceiver are optimized with the consideration of the nonstationarity in the local gradients based on a simple feedback variable. Moreover, a novel learning rate design is proposed for the stochastic gradient descent algorithm, which is adaptive to the quality of the gradient estimation. Theoretical analyses are provided on the convergence rate of the proposed AGA solution. Finally, the effectiveness of the proposed solution is confirmed by two separate experiments based on linear regression and the shallow neural network. The simulation results verify that the proposed solution outperforms various state-of-the-art baseline schemes with a much faster convergence speed.

Mobile-edge computing (MEC) is considered as a promising technology to reduce the energy consumption (EC) and task accomplishment latency of smart mobile user equipments (UEs) by offloading computation-intensive tasks to the nearby MEC servers. However, the Quality of Experience (QoE) for computation highly depends on the wireless channel conditions when computation tasks are offloaded to MEC servers. In this article, by considering the imperfect channel-state information (CSI), we study the joint offloading decision, transmit power, and computation resources to minimize the weighted sum of EC of all UEs while guaranteeing the probabilistic constraint in multiuser MEC-enabled Internet-of-Things (IoT) networks. This formulated optimization problem is a stochastic mixed-integer nonconvex problem and challenging to solve. To deal with it, we develop a low-complexity two-stage algorithm. In the first stage, we solve the relaxed version of the original problem to obtain offloading priorities of all UEs. In the second stage, we solve an iterative optimization problem to obtain a suboptimal offloading decision. As both stages include solving a series of nonconvex stochastic problems, we present a constrained stochastic successive convex approximation-based algorithm to obtain a near-optimal solution with low complexity. The numerical results demonstrate that the proposed algorithm provides comparable performance to existing approaches.

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

In this paper, we consider a mmWave massive multiple-input multiple-output (MIMO) communication system with one static base station (BS) serving a fast-moving user, both equipped with a very large array. The transmitted signal arrives at the user through multiple paths, each with a different angle-of-arrival (AoA) and hence Doppler frequency offset (DFO), thus resulting in a fast time-varying multipath fading MIMO channel. In order to mitigate the Doppler-induced channel aging for reduced pilot overhead, we propose a new angular-domain selective channel tracking and Doppler compensation scheme at the user side. Specifically, we formulate the joint estimation of partial angular-domain channel and DFO parameters as a dynamic compressive sensing (CS) problem. Then we propose a Doppler-aware-dynamic variational Bayesian inference (DD-VBI) algorithm to solve this problem efficiently. Finally, we propose a practical DFO compensation scheme which selects the dominant paths of the fast time-varying channel for DFO compensation and thereby converts it into a slow time-varying effective channel. Compared with the existing methods, the proposed scheme can enjoy the huge array gain provided by the massive MIMO and also balance the tradeoff between the CSI signaling overhead and spatial multiplexing gain. Simulation results verify the advantages of the proposed scheme over various baseline schemes.

In massive MIMO systems, the effective channel state information (CSI) is an essential prerequisite for the beamforming (BF) design. While there is a handful of previously proposed compressive sensing (CS)-based channel estimation algorithms in the literature, the large BF gain provided by the massive MIMO array has not been fully exploited in the channel estimation stage. In order to obtain higher BF gain and better estimation performance with less pilot overhead, we study the joint design of the transmitting and receiving hybrid pilot BF as well as the associated channel tracking scheme. Specifically, a Markov prior is used to model the temporal correlation in massive MIMO channels over different time slots. Then, the hybrid pilot BF is optimized by maximizing the mutual information between the channel measurements and the corresponding downlink sparse channels with the Markov prior. Following, we derive an efficient channel tracking algorithm called Turbo Bayesian Inference (Turbo-BI) to solve the resulting CS problem and generate the channel prior information required to calculate the mutual information for the optimization of hybrid pilot BF in the next time slot. The proposed Turbo-BI can exploit both the sparsity and the temporal correlation of massive MIMO channels to enhance the estimation performance. Finally, simulations show that our proposed algorithm can achieve significant gain over the existing state-of-the-art baselines.

Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral/energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming (i.e., IRS phase shifts) is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming (i.e., transmit precoding vectors at the access point (AP)) is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the AP, subject to the users' individual quality of service (QoS) constraints on the achievable long-term average rate. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term passive beamforming problem and a family of short-term active beamforming problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. We show that both the long-term and short-term problems can be efficiently solved and the proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes.

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

In this article, we propose a sparse hybrid combining (SHC) scheme for the uplink transmission of beamspace massive multiple-input multiple-output (MIMO) system with low-resolution analog to digital converters (LADCs), to alleviate the performance bottleneck caused by the multi-user interference and quantization noise, with reduced hardware cost and power consumption. To this end, we formulate the optimization of the proposed SHC scheme as a system energy efficiency maximization problem under some practical constraints. The resulting problem contains the highly coupled nonconvex objective function, as well as the discrete binary constraints. By exploiting some fractional programming (FP) techniques and introducing auxiliary variables, we first recast the original challenging problem into a more tractable yet equivalent form. We then develop an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) method to find its local stationary solutions. Finally, simulation results verify the effectiveness of the proposed SHC scheme by numerical examples in terms of the achieved system energy efficiency.

Symbiotic radio (SR) is emerging as a spectrum-and energy-efficient communication paradigm for future passive Internet of Things (IoT), where some single-antenna backscatter devices, referred to as Tags, are parasitic in an active primary transmission. The primary transceiver is designed to assist both direct-link (DL) and backscatter-link (BL) communication. In multi-Tags SR systems, the transceiver designs become much more complicated due to the presence of DL and inter-Tag interference, which further poses new challenges to the availability and reliability of DL and BL transmission. To overcome these challenges, we formulate the stochastic optimization of transceiver design as the general network utility maximization problem (GUMP). The resultant problem is a stochastic multiple-ratio fractional nonconvex problem, and consequently challenging to solve. By leveraging some fractional programming techniques, we tailor a surrogate function with the specific structure and subsequently develop a batch stochastic parallel decomposition (BSPD) algorithm, which is shown to converge to stationary solutions of the GNUMP. The simulation results verify the effectiveness of the proposed algorithm by numerical examples in terms of the achieved system throughput.

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

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

In many applications in wireless communications, we need to recover a structured sparse signal from a linear measurement model with uncertain sensing matrix. There are two challenges of designing an algorithm framework for this problem. How to choose a flexible yet tractable sparse prior to capture different structured sparsities in specific applications? How to handle a sensing matrix with uncertain parameters and possibly correlated entries? As will be explained in the introduction, existing common methods in compressive sensing (CS), such as approximate message passing (AMP) and variational Bayesian inference (VBI), may not work well. To better address this problem, we propose a novel Turbo-VBI algorithm framework, in which a three-layer hierarchical structured (3LHS) sparse prior model is proposed to capture various structured sparsities that may occur in practice. By combining the message passing and VBI approaches via the turbo framework, the proposed Turbo-VBI algorithm is able to fully exploit the structured sparsity (as captured by the 3LHS sparse prior) for robust recovery of structured sparse signals under an uncertain sensing matrix. Finally, we apply the Turbo-VBI framework to solve two application problems in wireless communications and demonstrate its significant gain over the state-of-art CS algorithms.

We propose a novel randomized channel sparsifying hybrid precoding (RCSHP) design to reduce the signaling overhead of channel estimation and the hardware cost and power consumption at the base station (BS), in order to fully harvest benefits of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. RCSHP allows time-sharing among multiple analog precoders, each serving a compatible user group. The analog precoder is adapted to the channel statistics to properly sparsify the channel for the associated user group, such that the resulting effective channel (product of channel and analog precoder) not only has enough spatial degrees of freedom (DoF) to serve this group of users, but also can be accurately estimated under the limited pilot budget. The digital precoder is adapted to the effective channel based on the duality theory to facilitate the power allocation and exploit the spatial multiplexing gain. We formulate the joint optimization of the time-sharing factors and the associated sets of analog precoders and power allocations as a general utility optimization problem, which considers the impact of effective channel estimation error on the system performance. Then we propose an efficient stochastic successive convex approximation algorithm to provably obtain Karush-Kuhn-Tucker (KKT) points of this problem.

Amplitude phase shift keying (APSK) aided differential spatial modulation (APSK-DSM) is a multiple-input-multiple-output wireless transmission technique which not only has the desirable features of differential spatial modulation (DSM) but also has a higher spectrum efficiency than DSM. However, the error propagation problem and the violation of the quasi-static channel assumption in conventional APSK-DSM design will cause significant performance loss, especially in undesirable channel conditions. Besides, the maximum-likelihood detection based on the traditional block-by-block search has a complexity that grows exponentially with the block size, which becomes intractable in systems with large transmit antenna numbers. To overcome those drawbacks, we propose a novel transmission scheme named the absolute amplitude differential phase spatial modulation (AADP-SM) in this paper. AADP-SM is able to alleviate the error propagation problem and achieve a near-coherent performance by invoking multiple previous blocks in the current detection. By taking into account the channel fading rate, AADP-SM is also more robust in fast-fading channels than APSK-DSM. A novel non-coherent detection algorithm is proposed for AADP-SM to reduce the exponential detection complexity to a polynomial order. Finally, we show through simulation that AADP-SM is tolerable to imperfect information about the channel statistics.

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

In this paper, visible light-based positioning (VLP) is studied. VLP is greatly challenging because (i) it is essentially a non-convex optimization problem since the visible-light received signal strength (RSS) is nonlinear with the user equipment (UE) position; and (ii) in addition to the UE location, the visible light RSS also depends on the UE orientation and small-scale channel gains, which are unknown in practice. This complicates the VLP problem due to the enlarged searching space. To address these challenges, we propose a location-domain grid sampling scheme. Specifically, the location-domain grid sampling can potentially partition the location space into small cells, and hence the non-convexity challenge of RSS-based VLP is mitigated. In addition, using the location-domain grid sampling, we transform VLP into a sparse recovery problem. A novel group sparse learning (GSL) algorithm with self-adaptive location-domain grids is proposed to achieve an efficient RSS-based VLP solution, via exploring the inherent sparse structure. The convergence of our GSL algorithm is established. Thanks to the adaptivity of dynamic location-domain grids, the required number of location-domain grids can be significantly reduced, compared with conventional fixed-grid-based GSL solutions. Moreover, the proposed GSL-based VLP method jointly learns the UE location, orientation and channel gain, thus achieving a robust RSS-based VLP solution against parameter uncertainties. Finally, our simulation result verifies the large performance gain of the proposed RSS-based VLP solution over state-of-the-art VLP baselines, thanks to our self-adaptive grid sampling and problem-specific group sparse learning.

Massive connectivity is regarded as a key requirement for future networks to support new communication paradigms, where the human-type communications coexist with machine-type communications. Owing to the limited coherence time but the huge number of potential devices, it is impossible to allocate mutually orthogonal pilot sequence for all potential devices, which may impose severe interference on the device activity detection and channel estimation. Existing nonorthogonal pilot design methods for conventional cellular network are not suitable for the massive connectivity regime. To overcome this challenge, we first formulate the pilot sequences design as an optimization problem to minimize the average mean square error (MSE) of channel estimation under the individual power constraint. The proposed optimization problem is nonconvex and highly coupled. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop a distributed algorithm based on the matrix fractional programming (FP) and the alternating direction method of multipliers (ADMM) methods. Simulations validates that the proposed scheme not only achieves significant gains in channel estimation over state-of-the-art baseline schemes, but also improves the device activity detection performance.

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

In this paper, joint estimation for channel and Inphase/Quadrature imbalance (IQI) is investigated in the downlink Frequency Division Duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. First, exploiting the sparsity of massive MIMO channels and the timescale separation of channels and IQI, we derive a two-timescale sparse maximum a posterior (MAP) formulation for the joint estimation, where the IQI parameter is the long- term variable and the sparse channel is the short- term variable. Then we propose a two-timescale online joint sparse estimation (TOJSE) algorithm to solve the problem, which can converge to the stationary solutions of the original two-timescale non-convex stochastic optimization problem over time. Finally, simulations show that our proposed TOJSE algorithm can achieve significant gain over various baselines.

Millimeter wave (mmWave) systems have potential advantages for high accuracy localization due to their large bandwidth and highly directional and sparse channel. The existing schemes on mmWave system localization only explore the channel's angular sparsity and restrict the representation of angular parameters to fixed grids. In this paper, we propose a joint sparse channel model with adjustable grids of angles and delays of mmWave paths, which not only enables exploiting the joint sparsity in angular and delay domain, but also mitigates the quantization error due to fixed grids. We formulate the estimation of channel, angles and delays as a sparse Bayesian learning problem and propose to solve it using an in-exact block minorization-maximization algorithm and then determine the user location. We verify the performance of the algorithm by simulations.

Many important application scenarios in the future fifth generation (5G) systems, such as indoor navigation and autonomous driving, rely on accurate localization of users. In this paper, we propose a sparse-Bayesian-inference (SBI) based direct location algorithm for massive MIMO systems, which can exploit the sparse and high-resolution nature of angle of arrival (AoA) and any available statistical location information (SLI), to significantly improve the user localization accuracy. The existing common methods in SBI, such as approximate message passing (AMP) an variational Bayesian inference (VBI), may not work well for the massive MIMO localization problem due to their respective drawbacks. To overcome these drawbacks, we first propose a novel three-layer hierarchical structured (3LHS) sparse prior model to incorporate both the structured sparsity of the massive MIMO channel and the SLI into the SBI-based localization formulation. Then we propose a structured VBI algorithm called 3LHS-VBI to solve the resulting SBI-based localization problem. Finally, simulations verify the superior performance of the proposed location algorithm.

The spectral efficiency of small-cell wireless networks is limited by the backhaul capacity of the base stations (BSs) as well as the severe interference from the neighboring BSs. One promising approach to improve the spectral efficiency of small-cell wireless networks is cache-aided cooperative transmission, where caching at the BSs can alleviate the high-speed backhaul capacity requirement and cooperative transmission can enhance the signal-to-interference-plus-noise ratio. A key issue is that the cached content may not be located at the nearest BS, which means that to access such content, a user needs to overcome strong interference from the nearby BSs. We propose an interference-aware dual-mode caching and user-centric open-loop cooperative transmission scheme that embraces spatial caching diversity and user-centric open-loop cooperative transmission, and alleviate the interference issue in the system. Based on the proposed scheme, we derive a tractable expression of the average successful transmission probability (STP) in terms of key system parameters. We then design a low-complexity algorithm to optimize the average STP with respect to the bandwidth and the cache storage capacity allocation. Simulations show that the proposed scheme achieves a higher average STP than the existing caching schemes.

Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing-based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings.

Recently, unmanned aerial vehicles (UAVs) have been utilized to extend the coverage and capacity of terrestrial wireless networks. In such a UAV-aided wireless network, we can exploit the more favorable line-of-sight (LOS) propagation in the UAV-user wireless link to achieve better coverage and higher capacity. Furthermore, the flexible deployment of UAVs makes them ideal for catering to ad hoc demand. Due to the lack of fixed-line backhaul, UAVs act like relays with wireless backhaul links in UAV-aided wireless networks. Unlike the conventional relays in terrestrial wireless networks, the positions of UAV-Rs can be optimized to maximize the system performance. Although the optimal UAV positioning problem has been studied under simple LOS/probabilistic channel models, the joint optimization of UAV positions, user association, and wireless backhaul capacity allocation remains unsolved under realistic channel models, which will be addressed in this paper. The joint optimization problem is a challenging mixed non-convex and combinatorial problem. Combining the block coordinate descent and successive convex approximation (SCA) methods, we propose a sparse parallel block SCA algorithm, which can find a near-optimal solution by exploiting the structures of the propagation model. The simulations verify the significant gain of the proposed solution over existing solutions.

Recently, physical layer (PHY) caching has been proposed to exploit the dynamic side information induced by caches at base stations (BSs) to support coordinated multi-point (CoMP) and achieve high degrees of freedom (DoF) gains. Due to the limited cache storage capacity, the performance of PHY caching depends heavily on the cache content placement algorithm. In the existing algorithms, the cache content placement is adaptive to the long-term popularity distribution in an offline manner. We propose an online PHY caching framework, which adapts the cache content placement to microscopic spatial and temporary popularity variations to fully exploit the benefits of PHY caching. Specifically, the joint optimization of online cache content placement and content delivery is formulated as a mixed-timescale drift minimization problem to increase the CoMP opportunity and reduce the cache content placement cost. We propose a low-complexity algorithm to obtain a throughput-optimal solution. Moreover, we provide a closed-form characterization of the maximum sum DoF in the stability region and study the impact of key system parameters on the stability region. The simulations results show that the proposed online PHY caching framework achieves large gain over existing solutions.

Hybrid beamforming (BF) is a promising solution for massive MIMO with limited RF chains. To reduce the amount of pilot overhead for channel estimation, compressed sensing techniques that exploit channel sparsity have been proposed. One key issue is how to design the RF (analog) training vectors to achieve higher BF gain with fewer pilots. Specifically, narrow-beam RF training requires large pilot overhead for finding strongest paths, and random RF training suffers from low BF gain. We propose to use a mixture of narrow-beam and random RF training vectors, and optimize the fraction of the two sets of RF training vectors based on channel support side information (CSSI) at the BS. We show that this optimized fraction exhibits a phase transition: when the CSSI accuracy exceeds a certain threshold, the maximum number of narrow-beam RF training vectors should be used to focus beams in all directions indicated by the CSSI. Otherwise, only random RF training vectors should be used to explore the unknown channel support. Moreover, we derive closed-form bounds on the channel estimation error. Both the analysis and simulations show that the proposed method can achieve substantial gains over various baseline methods.

Although there has been a wide investigation on channel estimation in frequency-division duplex (FDD) massive multiple-input-multiple-output (MIMO) systems, the effect of imperfect radio frequency (RF) chains have been largely ignored. In this paper, we consider a downlink massive MIMO system with in-phase/quadrature imbalance (IQI) at the base station (BS). Focusing on the joint estimation for channel and IQI, we model the joint estimation problem as a two-timescale non-convex optimization based on maximum a posteriori (MAP) estimate, where the IQI parameter is treated as the long-term variable and the sparse channel vector is short-term. We propose a batch algorithm and a two-timescale online joint sparse estimation (TOJSE) algorithm to solve the problem. The proposed batch algorithm utilizes all the previously received signals to update the current long-term variable, which can achieve better performance but with increasing computational complexity over time. In contrast, the TOJSE algorithm solves the short-term problem related to the current system state and constructs a recursive convex approximation to update the long-term variable in each iteration. Thus, the memory requirements and computational complexity of the TOJSE are remarkably reduced. Moreover, for the low mobility regime, a dynamic TOJSE algorithm is further presented to exploit the temporal correlation of channel support. Finally, the simulations show that our proposed algorithms can achieve significant gain over various baselines.

In this paper, we are interested in the joint estimate of user equipment (UE) position and orientation for visible light communication (VLC) with uncertain emission power. This joint estimation is a non-convex problem with a huge search space. To address this challenge, a novel VLC localization algorithm is proposed, which converges to the stationary solution of the joint estimation problem at an asymptotic quadratic convergence rate due to our problem-specific surrogate function design. A closed-form update rule is obtained via exploiting the hidden convex structure of the non-convex optimization problem. Hence, our algorithm has low complexity compared with the particle swarm-based optimization methods. In addition, the closed-form Cramer-Rao lower bounds (CRLBs) on the estimation errors of the respective UE location, orientation and LED emitting power are derived. Moreover, the effect of critical parameters such as signal-to-noise ratio (SNR), the number of LED transmitters, transmission distance and non-line-of-sight propagation on the VLC localization performance are revealed. The simulation result verifies that the proposed VLC localization algorithm under unknown VLC emitting power can achieve a huge performance gain over the state-of-the-art localization baselines.

In this paper, we aim at providing a unified performance analysis framework for visible light-based positioning (VLP) using received signal strength (RSS), which can be used to gain insights into improving the performance of RSS-based VLP systems. Specifically, we first obtain a closed-form Cramer-Rao lower bound (CRLB) for the user equipment (UE) location and orientation, respectively. Then, we reveal the impact of the signal-to-noise ratio (SNR), transmission distance, prior knowledge and the number of LED sources on the RSS-based VLP performance. Moreover, the impact of the non-line-of-sight (NLOS) propagation on the RSS-based VLP performance is studied. It is shown that the RSS-based VLP performance will hit an error floor caused by the unknown NLOS links. These NLOS-caused UE location and orientation error floors in the high SNR region are analyzed. Finally, the information contribution of each LED source is studied to give an intuitive understanding on the impact of the LED array geometry on the RSS-based VLP performance. The obtained CRLB and the associated VLP performance analysis form a theoretical basis for the design of efficient VLP algorithms and VLP performance optimization strategies (e.g., resource allocation and smart LED source selection).

Recently, a two-timescale hybrid precoding (THP) scheme has been proposed to reduce the implementation cost of massive MIMO systems. In THP, the MIMO precoder consists of a high- dimensional RF precoder adaptive to the channel statistics and a low-dimensional baseband precoder adaptive to the instantaneous effective channel state information (CSI). Since the channel statistics changes at a slow timescale and is approximately the same for different subbands, only a single RF precoder is required to cover all subbands over a long term, which helps to reduce the hardware cost and implementation complexity of RF precoder. Moreover, the CSI signaling overhead can also be reduced. In this paper, we consider power minimization for massive MIMO systems with THP and individual average rate constraints. Due to the two-timescale design and individual average rate constraints, the problem is a challenging non-convex stochastic optimization problem. We propose an online constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point of the power minimization problem. Simulations show that the proposed algorithm achieves significant gains over various baseline algorithms.

Mobile edge computing (MEC) is an emerging paradigm that mobile devices can offload the computation-intensive or latency-critical tasks to the nearby MEC servers, so as to save energy and extend battery life. Unlike the cloud server, MEC server is a small-scale data center deployed at a wireless access point, thus it is highly sensitive to both radio and computing resource. In this paper, we consider an Orthogonal Frequency-Division Multiplexing Access (OFDMA) based multi-user and multi-MEC-server system, where the task offloading strategies and wireless resources allocation are jointly investigated. Aiming at minimizing the total energy consumption, we propose the joint offloading and resource allocation strategy for latency- critical applications. Through the bi-level optimization approach, the original NP-hard problem is decoupled into the lower-level problem seeking for the allocation of power and subcarrier and the upper-level task offloading problem. Simulation results show that the proposed algorithm achieves excellent performance in energy saving and successful offloading probability (SOP) in comparison with conventional schemes.

Due to the demand for higher throughput, there is need for aggregating more carriers to serve one user equipment (UE). Massive carrier aggregation (MCA) may help as it aggregate a large number of potentially non-contiguous carriers spanning a wide bandwidth. However, implementing MCA brings challenges to the design of the receiver and corresponding data recovery algorithms. For example, if we assign a separate receiver chain for each carrier, the number of receiver chains will be large, which imposes a huge cost. If we use a single receiver chain for all non-contiguous carriers, an expensive high rate analog-to-digital converter (ADC) is required to sample the entire span of the carriers. To reduce the cost, we propose a receiver architecture that employs only one receiver chain with a non-uniform ADC whose sampling rate is much smaller than the Nyquist rate, and a low cost power amplifier with small dynamic range. Under such architecture, the received signal suffers from non-linear distortion and interference, and the resulting data recovery is a challenging non-linear compressive sensing problem. We propose an algorithm to jointly mitigate the interference and recover the data, which is proved to have theoretical performance guarantees and verified advantageous over baselines in simulations.

In massive MIMO aided cloud radio access network (C-RAN), plenty of remote radio heads (RRHs), each equipped with a massive MIMO array, are distributed within a specific geographical area and are connected to a centralized baseband unit (BBU) pool through fronthaul links. One major performance bottleneck in the uplink of massive MIMO aided C-RAN is that, the RRHs need to transport a huge amount of data to the BBU for baseband processings. Existing fronthaul compression methods that rely on fully-digital processing are not suitable for the massive MIMO regime due to their high implementation cost. To overcome this challenge, we propose a two-timescale hybrid analog-and-digital spatial compression scheme at RRHs to reduce the fronthaul data, where the analog filter is updated at a slow timescale according to the channel statistics to achieve massive MIMO array gain, and the digital filter is updated at a fast timescale according to the instantaneous effective channel state information (CSI) to achieve spatial multiplexing gain. Such a design can alleviate the performance bottleneck of limited fronthaul with reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters. Simulations verify the advantages of the proposed scheme over state-of-the-art baseline schemes.

In this paper, the identification of multiple leaks in pipes based on transient waves is studied, which is, however, quite challenging due to its nonconvex nature with lots of local optima. Existing approaches need the number of leaks and suffer from a huge computational complexity that is increased exponentially with the number of leaks. To provide a scalable solution, we propose a compressive sensing (CS) framework to solve the multileaks identification. We first exploit the sparseness nature of leak locations through spatial sampling. Then, we formulate the multileak identification as a CS problem, where the spatial sample-dependent components form a basis matrix and the leak sizes are viewed as a sparse signal. We establish the convergence of spatial sampling mismatch and the two-restricted isometry property of basis matrix to justify the proposed CS framework. The proposed CS framework renders a superior-performance solution to multileak identification and its computational complexity is linear with the number of leaks, which is a significant technical improvement over existing approaches. In addition, a closed-form Cramer-Rao lower bound (CRLB) on the leak localization errors is derived. A geometric insight of CRLB evolution is presented to give us an intuitive understanding of the contribution of new measurements to leak localization performance.

Considering the expensive radio frequency (RF) chain, huge training overhead, and feedback burden issues in massive MIMO, in this letter, we propose a mixed-timescale per-group hybrid precoding scheme under an adaptive partially connected antenna structure, where the RF precoder is implemented using an adaptive connection network (ACN) and M analog phase shifters (APSs), where M is the number of antennas at the base station. Exploiting the mixed time stage channel state information (CSI) structure, the joint-design of ACN, and APSs is formulated as a statistical signal-to-leakage-and-noise ratio maximization problem, and a heuristic group RF precoding algorithm is proposed to provide a near-optimal solution. Simulation results show that the proposed design advances at better energy efficiency and lower hardware cost, CSI signaling overhead and computational complexity than the conventional hybrid precoding schemes.

We consider a wireless device-to-device caching network where n nodes are placed on a regular grid of area A (n). Each node caches LCF (coded) bits from a library of size LF bits, where L is the number of files and F is the size of each file. Each node requests a file from the library independently according to a popularity distribution. Under a commonly used “physical model” and Zipf popularity distribution, we characterize the optimal per-node capacity scaling law for extended networks (i.e., A (n) = n). Moreover, we propose a cache-induced hierarchical cooperation scheme and associated cache content placement optimization algorithm to achieve the optimal per-node capacity scaling law. When the path loss exponent α <; 3, the optimal per-node capacity scaling law achieved by the cache-induced hierarchical cooperation can be significantly better than that achieved by the existing state-of-the-art schemes. To the best of our knowledge, this is the first work that completely characterizes the per-node capacity scaling law for wireless caching networks under the physical model and Zipf distribution with an arbitrary skewness parameter τ. While scaling law analysis yields clean results, it may not accurately reflect the throughput performance of a large network with a finite number of nodes. Therefore, we also analyze the throughput of the proposed cache-induced hierarchical cooperation for networks of practical size. The analysis and simulations verify that cache-induced hierarchical cooperation can also achieve a large throughput gain over the cache-assisted multihop scheme for networks of practical size.

Caching at base stations (BSs) is a promising approach for supporting the tremendous traffic growth of content delivery over future small-cell wireless networks with limited backhaul. This paper considers exploiting spatial caching diversity (i.e., caching different subsets of popular content files at neighboring BSs) that can greatly improve the cache hit probability, thereby leading to better overall system performance. A key issue in exploiting spatial caching diversity is that the cached content may not be located at the nearest BS, which means that to access such content, a user needs to overcome strong interference from the nearby BSs; this significantly limits the gain of spatial caching diversity. In this paper, we consider a joint design of frequency reuse and caching, such that the benefit of an improved cache hit probability induced by spatial caching diversity and the benefit of interference coordination induced by frequency reuse can be achieved simultaneously. We obtain a closed-form characterization of the approximate successful transmission probability for the proposed scheme and analyze the impact of key operating parameters on the performance. We design a low-complexity algorithm to optimize the frequency reuse factor and the cache storage allocation. Simulations show that the proposed scheme achieves a higher successful transmission probability than existing caching schemes.

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization, which jointly optimizes flow control, routing, interference mitigation, and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signaling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signaling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.

Due to the demand for higher throughput, there is need for aggregating more carriers to serve one user equipment. Massive carrier aggregation (MCA) may help as it aggregates a large number of potentially non-contiguous carriers spanning a wide bandwidth. However, implementing MCA brings challenges to the design of the receiver and corresponding data recovery algorithms. For example, if we assign a separate receiver chain for each carrier, the number of receiver chains will be large, which imposes a huge cost. If we use a single receiver chain for all non-contiguous carriers, an expensive high rate analog-to-digital converter (ADC) is required to sample the entire span of the carriers. To reduce the cost, we propose a receiver architecture that employs only one receiver chain with a non-uniform ADC, whose sampling rate is much smaller than the Nyquist rate, and a low cost power amplifier with small dynamic range. Under such architecture, the received signal suffers from non-linear distortion and interference, and the resulting data recovery is a challenging non-linear compressive sensing problem. We propose an algorithm to jointly mitigate the interference and recover the data, which is proved to have theoretical performance guarantees and verified advantageous over baselines in simulations.

Channel estimation (CE) in massive MIMO system with limited RF chains is known to be a challenging problem. In many cases, base station (BS) can obtain certain channel support side information (CSSI), which can be exploited to enhance the CE performance and reduce the pilot overhead. Due to imperfectness of prior information, it's very important to utilize the CSSI in an optimal way to improve the CE performance. We propose an optimal-tuned weighted LASSO algorithm which can fully exploit the imperfect CSSI to optimize the CE performance in massive MIMO system with limited RF chains. In the proposed algorithm, the weighted l_{1} norm is used as the regularization function and the weights on the known support part (as indicated by the CSSI) and the rest are different. Based on the accuracy of CSSI, we obtain closed-form solution for the optimal LASSO weights which minimize the asymptotic normalized squared error (aNSE). Moreover, we derive closed-form expression of the minimum aNSE and characterize the minimum number of required pilots to achieve stable channel recovery. The theoretical analysis and simulation both show the performance advantages of our proposed solution over various baselines.

In content-centric wireless networks, physical layer (PHY) caching has been proposed to exploit the dynamic side information induced by base station (BS) cache to support Coordinated Multi-Point (CoMP) and achieve huge capacity gain. The performance of PHY caching depends heavily on the cache content placement algorithm. In existing algorithms, the cache content placement is adaptive to the long-term popularity distribution in an offline manner. We propose an online PHY caching framework based on the concept of virtual interest packet (VIP) in a virtual network. The VIP captures microscopic spatial and temporary popularity variations, and thus the VIP-based online PHY caching can adapt the cached content to the microscopic popularity variations to fully exploit the benefits of PHY caching. The joint optimization of online caching and content delivery is formulated as a mixed timescale drift minimization problem and a low complexity algorithm is proposed to find the optimal solution. Simulations show that the proposed solution achieves large gain over existing solutions.

Recently, compressive channel estimation (CE) has been proposed to reduce the pilot overhead for massive MIMO with limited RF chains. One key issue is how to design the RF (analog) training vectors to achieve higher beamforming (BF) gain with fewer pilots. Specifically, narrow-beam RF training requires large pilot overhead for finding strongest paths, and random RF training suffers from low BF gain. We propose to use a mixture of narrow-beam and random RF training vectors, and exploit the channel support side information (CSSI) at the BS to do joint RF training and compressive CE. The narrow-beam RF training vectors are used to achieve a high BF gain, and the random RF training vectors are used to explore the unknown channel support to reduce the pilot overhead. Moreover, we derive closed-form bounds on the CE error. Both the analysis and simulations show that the proposed method can achieve substantial gains over various baseline methods.

We study the capacity scaling law of a device-to-device (D2D) caching network where n nodes are placed on a regular grid of area n. Each node caches some (coded) bits from a content library and requests a file from the library independently according to the Zipf popularity distribution. We propose a cache-induced hierarchical cooperation scheme which achieves the optimal capacity scaling law under a commonly used “physical model”. When the path loss exponent α <; 3, the capacity scaling law can be significantly better than the throughput scaling laws achieved by the existing state-of-the-art schemes. To the best of our knowledge, this is the first work that completely characterizes the capacity scaling law for wireless caching networks under the physical model.

This paper proposes a new design of non-orthogonal multiple access (NOMA) under secrecy considerations. We focus on a NOMA system, where a transmitter sends confidential messages to multiple users in the presence of an external eavesdropper. The optimal designs of decoding order, transmission rates, and power allocated to each user are investigated. Considering the practical passive eavesdropping scenario where the instantaneous channel state of the eavesdropper is unknown, we adopt the secrecy outage probability as the secrecy metric. We first consider the problem of minimizing the transmit power subject to the secrecy outage and quality of service constraints, and derive the closed-form solution to this problem. We then explore the problem of maximizing the minimum confidential information rate among users subject to the secrecy outage and transmit power constraints, and provide an iterative algorithm to solve this problem. We find that the secrecy outage constraint in the studied problems does not change the optimal decoding order for NOMA, and one should increase the power allocated to the user whose channel is relatively bad when the secrecy constraint becomes more stringent. Finally, we show the advantage of NOMA over orthogonal multiple access in the studied problems both analytically and numerically.

Dense wireless networks are a promising solution to meet the huge capacity demand in 5G wireless systems. However, there are two implementation issues, namely, the interference and backhaul issues. To resolve these issues, we propose a novel network architecture called the backhaul-limited cached dense wireless network (C-DWN), where a physical layer (PHY) caching scheme is employed at the base stations (BSs), but only a fraction of the BSs have wired payload backhauls. The PHY caching can replace the role of wired backhauls to achieve both the cache-induced multiple-input-multiple-output (MIMO) cooperation gain and cache-assisted multihopping gain. Two fundamental questions are addressed. Can we exploit the PHY caching to achieve linear capacity scaling with limited payload backhauls? If so, how much cache is needed? We show that the capacity of the backhaul-limited C-DWN indeed scales linearly with the number of BSs if the BS cache size is larger than a threshold that depends on the content popularity. We also quantify the throughput gain due to cache-induced MIMO cooperation over conventional caching schemes (which exploit purely the cached-assisted multihopping). Interestingly, the minimum BS cache size needed to achieve a significant cache-induced MIMO cooperation gain is the same as that needed to achieve the linear capacity scaling.

In this paper, we study the average sum DoF of 2 cached interference networks over random user requests. First, 3 we derive a closed-form average sum DoF upper bound of 4 the cached interference channels over all possible caching and 5 transmission strategies. Then, we propose an online cache content 6 placement algorithm to maximize the achievable average sum 7 DoF without explicit knowledge of the popularity of the content 8 files. We show that the gap between the DoF upper bound and 9 an achievable DoF is small for some typical scenarios. Moreover, 10 we quantify the DoF gain due to caching with respect to some important system parameters. We show that large DoF gain over 11 12 interference network without cache is possible even if the number 13 of files L → ∞. Both simulation and analysis indicate that the proposed scheme has significant gains over various baselines.

Recently, physical layer (PHY) caching at the base station (BS) has been proposed to induce MIMO cooperation among peer BSs to reduce interference and improve the spectrum efficiency in the radio access network (RAN). Compared with caching at the core network (CN) gateway (CN-caching), which covers a large number of users, it may appear that PHY-caching covers less users and results in lower cache hit rate. In this paper, we analyze the tradeoff between spectrum efficiency gain induced by PHY-caching and CN-backhaul offloading gain induced by CN-caching. Specifically, we derive the Pareto optimal tradeoff between the spectrum efficiency gain and CN-backhaul offloading gain. Surprisingly, we show that even though PHY-caching covers less users than CN-caching (single cell versus multiple cells), it is still worthwhile to do PHY-caching at the BS.

The knowledge of CSI at the BS (CSIT) is required to achieve the high spectrum efficiency promised by massive MIMO. In Frequency-Division Duplex (FDD) Massive MIMO systems, the CSIT is obtained via downlink channel estimation and uplink channel feedback, However, the acquisition of CSIT is a very challenging problem in practical FDD massive MIMO systems with a large number of antennas. Recently, compressive sensing has been applied to reduce pilot and CSIT feedback overheads in massive MIMO systems by exploiting the underlying channel sparsity. However, standard sparse recovery algorithms have stringent requirement on the channel sparsity level for robust channel recovery and this severely limits the operating regime of the solution. To overcome this issue, we propose a joint burst LASSO algorithm to exploit additional joint burst-sparse structure in multi-user (MU) massive MIMO channels. Simulations show that the joint burst LASSO algorithm can alleviate the stringent requirement on the sparsity level for robust channel recovery and substantially enhance the channel estimation performance over existing solutions.

Acquisition of accurate channel state information (CSI) at the base station (CSIT) is a major challenge of deploying frequency-division duplexing (FDD) massive MIMO systems. Although compressive sensing (CS) based CSIT estimation approaches have been proposed to reduce the pilot training overhead for massive MIMO systems, the existing schemes cannot properly dimension the minimum required pilot symbols to estimate the CSIT of all users at the required CSIT quality, because of the loose bounds on the required number of measurements for successful CS recovery and the unknown sparsity levels of user channels. In this paper, we propose a robust closed-loop compressive CSIT feedback and estimation framework which not only exploits the joint sparsity structure of the multi-user (MU) massive MIMO channels to improve the CSIT estimation performance, but also has the learning capability to adapt to the minimum pilot and feedback resources needed under unknown and time-varying channel sparsity levels. We establish the convergence of the proposed closed-loop adaptation algorithm. Simulations show that the proposed framework has substantial performance gain over conventional open-loop algorithms and is robust to dynamic sparsity and model mismatch.

Recently, hybrid beamforming, which consists of an analog RF precoder and a digital baseband precoder, has been proposed for massive MIMO systems to reduce the number of RF chains and power consumption at the base station (BS). This paper studies the impact of channel state information (CSI) on the sum capacity of massive MIMO systems with quantized hybrid beamforming where the RF precoder is selected from a finite size codebook. Two types of CSI at the BS (CSIT) are assumed: full instantaneous CSIT (full channel matrix between the BS and users) and hybrid CSIT (channel statistics plus the low dimensional effective channel matrix after RF precoding). We derive asymptotic sum capacity expressions under these two types of CSIT. We find that, in most cases, exploiting the full instantaneous CSIT can only achieve a marginal SNR gain and hybrid CSIT is sufficient to achieve the first order gain provided by massive MIMO.

In this paper, we consider the problem of cross-layer radio resource management in heterogeneous networks (HetNets) with flexible backhaul, which aims at minimizing the total transmit power of base stations (BSs) while guaranteeing the average end-to-end data rate requirement of each data flow. We formulate the problem as a two-time scale stochastic optimisation, where the long-timescale control variables are flow control, routing control and interference coordination, while the short-timescale control variable is instantaneous beamforming within each cell. Using a stochastic cutting plane (SCP) method, we propose a cross-layer queue-state information (QSI) aware radio resource management (RRM) solution in which the long-term controls are updated centrally at radio resource management server (RRMS) without needing to know the global statistical information of the network, and the short-term control variables are updated locally at each BS with only the local instantaneous QSI and channel-state information (CSI) available at each cell. Simulation results show the significant performance gain of our proposed algorithm compared to various baselines.

We propose a physical layer (PHY) caching scheme for wireless ad hoc networks. The PHY caching exploits cache-assisted multihop gain and cache-induced dual-layer CoMP gain, which substantially improves the throughput of wireless ad hoc networks. In particular, the PHY caching scheme contains a novel PHY transmission mode called the cache-induced dual-layer CoMP, which can support homogeneous opportunistic CoMP in the wireless ad hoc network. Compared with traditional per-node throughput scaling results of Θ ( 1/√N ), we can achieve O(1) per node throughput for a cached wireless ad hoc network with N nodes. Moreover, we analyze the throughput of the PHY caching scheme for regular wireless ad hoc networks and study the impact of various system parameters on the PHY caching gain.

How to obtain accurate channel state information at the base station (CSIT) is a key implementation challenge behind frequency-division duplex massive MIMO systems. Recently, compressive sensing (CS) has been applied to reduce pilot and CSIT feedback overheads in massive MIMO systems by exploiting the underlying channel sparsity. However, brute-force applications of standard CS may not lead to good performance in massive MIMO systems, because standard sparse recovery algorithms have quite a stringent requirement on the sparsity level for robust recovery and this severely limits the operating regime of the solution. Moreover, since the channel support is usually correlated across time, it is possible to obtain partial channel support information (P-CSPI) from previously estimated channel support. Motivated by the above observations, we propose a P-CSPI aided burst Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to exploit both the P-CSPI and additional structured properties of the sparsity, namely, the burst sparsity in massive MIMO channels. We also accurately characterize the asymptotic channel estimation error of the P-CSPI aided burst LASSO algorithm. Both the analysis and simulations show that the P-CSPI aided burst LASSO algorithm can alleviate the stringent requirement on the sparsity level for robust channel recovery and substantially enhance the channel estimation performance over existing solutions.

Interference is a key issue that limits the Degrees of Freedom (DoF) in conventional wireless networks. In this paper, we propose a novel physical layer (PHY) caching scheme to achieve DoF gains in interference networks. Specifically, by properly caching a portion of the content files at each transmitters, the PHY topology can be opportunistically transformed from the unfavorable interference channel topology into a more favorable MIMO broadcast channel topology and enjoy a large DoF gain. We first propose a novel caching scheme to significantly improve the MIMO cooperation opportunity induced by PHY caching. Then we quantify the DoF gain w.r.t. some important system parameters. Both analysis and simulation show that the proposed scheme has significant gains over various baselines.

A mixed timescale cross-layer resource optimization scheme is proposed for a multi- antenna device-to-device (D2D) network. We consider the two timescale joint optimization of beamforming, routing and flow control, where the short-term beamforming control at each transmit device is adaptive to the local real-time CSI, while the long-term routing and flow control is adaptive to the global topology and channel statistics of the D2D network. We propose a stochastic cutting plane algorithm to find the global optimal solution of the joint optimization problem. The proposed solution has low signaling overhead and does not require explicit knowledge of the channel statistics. Simulations show that the proposed solution achieves significant performance gain over several baselines.

In this paper, we focus on the problem of hierarchical cross-layer dynamic resource allocation for heterogeneous networks with flexible backhaul. We formulate the radio resource management problem as a two timescale non-convex stochastic optimisation problem which jointly optimizes flow control, routing control, interference mitigation and link scheduling in order to maximize a generic network utility. We propose an iterative hierarchical control structure where the long-term controls are adaptive to large scale fading and the short-term control is adaptive to the local CSI within a pico or macro BS, to find the optimal solution. The proposed solution benefits from low complexity and requires low signalling and message passing among different nodes, which makes it scalable. Moreover, due to the proposed two-timescale hierarchical design, it is robust to the backhaul signalling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.

Consider a multicarrier communication scenario where both the information (data) and channel have sparse structure. To exploit such sparse structure, we propose to use a receiver working at sampling rate much lower than Nyquist rate (sub-Nyquist sampling) to acquire the data corrupted by noise as well as multipath channel. With the sub-Nyquist rate samples, the joint channel estimation and data recovery is formulated as a sparse maximum likelihood estimation (MLE) problem which maximizes the associated non-concave likelihood function under the sparsity constraints on channel and data. This Sparse MLE framework is proved to provide solutions with bounded error w.r.t. the true value of channel and data under certain restricted isometry property (RIP) conditions. We propose an alternating sparse matching pursuit (ASMP) algorithm to solve the non-convex Sparse MLE problem. We also establish the sufficient conditions for ASMP to converge to solution with bounded error under certain restricted isometry property (RIP) conditions. Simulations show that when sparse structure is exploited, a low rate sub-Nyquist receiver using ASMP performs nearly as well as a Nyquist rate receiver.

In this paper, we propose a physical layer (PHY) caching scheme to exploit cache-assisted dynamic multihopping gain and cache-induced opportunistic CoMP gain in wireless ad hoc networks. In particular, the PHY caching scheme contains a novel PHY transmission mode called the cache-induced dual-layer CoMP which can support homogeneous opportunistic CoMP in wireless adhoc networks and substantially improves the throughput. Compared with traditional per-node throughput scaling results of Θ (1/√N), we can achieve O(1) per node throughput for a cached wireless ad hoc network with N nodes. Moreover, we study the impact of various system parameters on the PHY caching gain. Simulations also verified that the proposed PHY caching achieves significant gains over existing wireless caching schemes.

One of the main advantages of utilizing flexible backhaul in future HetNets is that it can provide better user experience through flexible resource allocation. For this purpose, it is important to provide the required quality of service (QoS) in such networks. In this paper, we consider the problem of cross-layer radio resource management in HetNets with flexible backhaul which guarantees minimum total transmit power of BSs as well as the average end-to-end data rate requirement of each data flow. The problem is formulated as a two-timescale stochastic optimisation, where the long-timescale control variables are flow control, routing control and interference mitigation, while the short-timescale control variable is instantaneous beamforming within each cell. Using stochastic cutting plane, we propose an online cross-layer hierarchical algorithm in which the long-term controls are updated centrally at radio resource management server (RRMS) without needing to know the global statistical information of the network, and the short-term control variables are updated locally at each BS with only the local instantaneous CSI available at each cell. The proposed algorithm has low complexity and signalling overhead. Moreover, simulation results show the significant gains of our proposed algorithm over various baselines.

MIMO interference network optimization is important for increasingly crowded wireless communication networks. This paper presents a new algorithm, named Dual Link Algorithm, for weighted sum-rate maximization where the interference is efficiently managed. We consider general MIMO interference channels with Gaussian input and a total power constraint. Two of the previous state-of-the-art algorithms are the WMMSE algorithm and the polite water-filling (PWF) algorithm. The WMMSE algorithm is provably convergent, while the PWF algorithm takes the advantage of the optimal transmit signal structure and converges the fastest in most situations but is not guaranteed to converge in all situations. Therefore, it is highly desirable to design an algorithm that has the advantages of both algorithms. The proposed dual link algorithm is such an algorithm. Its fast and guaranteed convergence is important to distributed implementation and time varying channels. In addition, the technique and a scaling invariance property used in the convergence proof may find applications in other non-convex problems in communication networks.

We propose a subspace constrained precoding scheme to fully unleash the gain provided by massive antenna array with reduced channel state information (CSI) signaling overhead. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace control matrix. The inner precoder is adaptive to the local CSI at each BS for spatial multiplexing gain. The Tx subspace control is adaptive to the channel statistics for inter-cell interference mitigation. Specifically, the Tx subspace control is formulated as a QoS optimization problem which involves an SINR chance constraint where the probability of each user's SINR not satisfying a service requirement must not exceed a given outage probability. Such chance constraint cannot be handled by the existing methods due to the two-stage precoding structure. To tackle this, we propose a bi-convex approximation approach, which consists of three key ingredients: random matrix theory, chance constrained optimization and semidefinite relaxation. Then we propose an efficient algorithm to find the optimal solution of the resulting bi-convex approximation problem. Simulations show that the proposed design has significant gain over various baselines.

Consider the scenario where a receiver acquires information (data) corrupted by interference and noise. Both the information and interference have a sparse structure. To fully exploit theindividual sparse structureof the information and interference, the joint interference mitigation and data recovery is formulated as a sparse maximum likelihood estimation (MLE) problem which maximizes the associated likelihood function underindividual sparsity levels(ISLs) constraints. We propose analternating optimization(AO) recovery algorithm to solve thenon-convexsparse MLE problem. Under certain restricted isometry property (RIP) conditions, we show that the proposed AO algorithm converges to the optimal solution of the sparse MLE problem. We also derive an upper bound of the corresponding estimation error for the information. Simulations show that the proposed solution achieves significant gain over various baselines.

We propose a multi-timescale interference mitigation scheme for massive MIMO cellular networks. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and an outer precoder. The inner precoder controls the intra-cell interference and is adaptive to local channel state information (CSI) at each BS (CSIT). The outer precoder controls the inter-cell interference and is adaptive to channel statistics. Such hierarchical precoding structure reduces the number of pilot symbols required for CSI estimation in massive MIMO downlink and is robust to the backhaul latency. We study non-convex joint optimization of the outer precoders, the user selection, and the power allocation to maximize a general concave utility. Using the hidden convexity of the non-convex problem, we propose an iterative algorithm to find the optimal solution. We also obtain a low complexity algorithm with provable convergence. Simulations show that the proposed design has significant gain over various state-of-the-art baselines.

Advanced interference mitigation techniques such as cooperative MIMO (CoMP) have been proposed to improve the spectral efficiency of future 5G wireless networks. However, conventional CoMP is quite costly because it requires high capacity backhaul for payload exchange between the BSs. In this paper, we propose a novel solution framework of cache-induced opportunistic CoMP to achieve the CoMP gain for multimedia streaming applications without expensive backhaul. Specifically, by caching a portion of the media files, the base stations (BS) opportunistically employ CoMP to transform the cross-link interference into spatial multiplexing gain. We study a mixed-timescale optimization of (short timescale) MIMO precoding and (long timescale) cache control to minimize the transmit power under the QoS constraint. By exploiting the timescale separations of the optimization variables, we derive low complexity multi-timescale solution for the MIMO precoding and cache control. The solution has significant gains over various baselines.

Interference is a major performance bottleneck in Heterogeneous Network (HetNet). The LTE-A working groups are actively studying enhanced inter-cell interference coordination (eICIC) techniques such as almost blank subframes (ABS). We propose a two timescale hierarchical radio resource management (RRM) scheme for HetNet with dynamic ABS control. The long term controls, such as dynamic ABS, are adaptive to the large scale fading at a RRM server for interference control. The short term control (user scheduling) is adaptive to the local channel state information (CSI) within each BS to exploit the multi-user diversity. Such hierarchical RRM has low signaling overhead, good scalability, and robustness w.r.t. latency of the backhaul signaling. The two timescale optimization problem is challenging due to the exponentially large solution space. We exploit the sparsity in the HetNet interference graph and derive structural properties for the optimal ABS control. Based on that, we propose a two timescale alternative optimization solution which has low complexity and is asymptotically optimal at high SNR.

Massive multiple-input multiple-output (MIMO) is an attractive solution to achieve high data rate and tackle interference. In this paper, we study the downlink of a two-cell massive MIMO system employing coordinated zero-forcing beamforming with limited feedback, where each base station (BS) acquires both the direct link channel state information (DCSI) and the cross link CSI (CCSI) from the users through a limited feedback link. The DCSI is used to support multi-user MIMO transmission (control intra-cell interference) at each BS and the CCSI is used to control the inter- cell interference. Since the total number of feedback bits at each user is fixed, there is a tradeoff between the quantization errors of the DCSI and CCSI. We consider the allocation of the feedback bits over the DCSI and CCSI to maximize the sum-rate of the system. We obtain closed-form solution for the asymptotically optimal feedback bits allocation as the number of antennas per BS goes to infinity. The solution reveals insights on how the key system parameters such as path gains, transmit powers and the number of antennas/users affect the optimal feedback bits allocation. Numerical results validate our theoretical analysis.

It is often impractical to obtain global channel knowledge and conduct centralized optimization for wireless networks. We study distributed weighted sum-rate maximization (WSRM) in general MIMO interference networks, named B-MAC interference networks. It is desirable to exploit the structure of the problem to design distributed algorithms with high performance and low signaling overhead. We recently unveiled a polite water-filling (PWF) structure satisfied by all Pareto optimal inputs of important achievable regions of the B-MAC interference networks. The PWF offers an elegant method to decompose a network into multiple equivalent single user channels and thus, facilitates the design of distributed algorithms. Based on the PWF, we design efficient distributed algorithms which only need local channel knowledge and converge to a stationary point of the WSRM problem. For TDD networks, the duality inherited in PWF and the channel reciprocity are further exploited to reduce the signaling overhead. The proposed algorithms are shown by simulations to outperform the state-of-the-art.

The algorithms in this paper exploit optimal input structure in interference networks and is a major advance from the state-of-the-art. Optimization under multiple linear constraints is important for interference networks with individual power constraints, per-antenna power constraints, and/or interference constraints as in cognitive radios. While for single-user MIMO channel transmitter optimization, no one uses general purpose optimization algorithms such as steepest ascent because water-filling is optimal and much simpler, this is not true for MIMO multiaccess channels (MAC), broadcast channels (BC), and the non-convex optimization of interference networks because the traditional water-filling is far from optimal for networks. We recently found the right form of water-filling, polite water-filling, for some capacity/achievable regions of the general MIMO interference networks, named B-MAC networks, which include BC, MAC, interference channels, X networks, and most practical wireless networks as special cases. In this paper, we use weighted sum-rate maximization under multiple linear constraints in interference tree networks, a natural extension of MAC and BC, as an example to show how to design highly efficiency and low complexity algorithms. Several times faster convergence speed and orders of magnitude higher accuracy than the state-of-the-art are demonstrated by numerical examples.

We found a network version of water-filling, named polite water-filling, that is optimal for all boundary points of the capacity regions of MAC and BC and for all boundary points of a set of achievable regions of a general class of interference networks, named MIMO B-MAC networks that include BC, MAC, interference channels, X networks, and most practical networks as special cases. It is polite because it strikes an optimal balance between reducing interference to others and maximizing a link's own rate. Unlike in single-user MIMO channels, where the optimal input covariance can be solved by the water-filling, the traditional water-filling is far from optimal in networks. Thus, general purpose optimization algorithms have been used for networks but have high complexity and do not work well for non-convex cases. Together with our duality result, the polite water-filling can be used to design highly efficient low-complexity iterative centralized/distributed algorithms for the optimization of input covariance matrices, including both power and beamforming matrices, because it takes the advantage of the structure of the problems. References to the resulting algorithms that outperform the state-of-the-art by a wide margin are provided.

Spread spectrum techniques such as Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping (FH) have been commonly used for anti-jamming wireless communication. However, traditional spread spectrum techniques require that sender and receivers share a common secret in order to agree upon, for example, a common hopping sequence (in FH) or a common spreading code sequence (in DSSS). Such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the key from a compromised receiver and then disrupt the wireless communication. In this paper, we develop a novel Delayed Seed-Disclosure DSSS (DSD-DSSS) scheme for efficient anti-jamming broadcast communication. DSD-DSSS achieves its anti-jamming capability through randomly generating the spreading code sequence for each message using a random seed and delaying the disclosure of the seed at the end of the message. We also develop an effective protection mechanism for seed disclosure using content-based code subset selection. DSD-DSSS is superior to all previous attempts for anti-jamming spread spectrum broadcast communication without shared keys. In particular, even if a jammer possesses real-time online analysis capability to launch reactive jamming attacks, DSD-DSSS can still defeat the jamming attacks with a very high probability. We evaluate DSD-DSSS through both theoretical analysis and a prototype implementation based on GNU Radio; our evaluation results demonstrate that DSD-DSSS is practical and have superior security properties.

It is well known that in general, the traditional water-filling is far from optimal in networks. We recently found the long-sought network version of water-filling named polite water-filling that is optimal for a large class of MIMO networks called B-MAC networks, of which interference Tree (iTree) networks is a subset whose interference graphs have no directional loop. iTree networks is a natural extension of both broadcast channel (BC) and multiaccess channel (MAC) and possesses many desirable properties for further information theoretic study. Given the optimality of the polite water-filling, general purpose optimization algorithms for networks are no longer needed because they do not exploit the structure of the problems. Here, we demonstrate it through the weighted sum-rate maximization. The significance of the results is that the algorithm can be easily modified for general B-MAC networks with interference loops. It illustrates the properties of iTree networks and for the special cases of MAC and BC, replaces the current steepest ascent algorithms for finding the capacity regions. The fast convergence and high accuracy of the proposed algorithms are verified by simulation.

Jamming resistance is crucial for applications where reliable wireless communication is required. Spread spectrum techniques such as Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) have been used as countermeasures against jamming attacks. Traditional anti-jamming techniques require that senders and receivers share a secret key in order to communicate with each other. However, such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the shared key from a compromised or malicious receiver and disrupt the reception at normal receivers. In this paper, we propose a Randomized Differential DSSS (RD-DSSS) scheme to achieve anti-jamming broadcast communication without shared keys. RD-DSSS encodes each bit of data using the correlation of unpredictable spreading codes. Specifically, bit ``0'' is encoded using two different spreading codes, which have low correlation with each other, while bit ``1'' is encoded using two identical spreading codes, which have high correlation. To defeat reactive jamming attacks, RD-DSSS uses multiple spreading code sequences to spread each message and rearranges the spread output before transmitting it. Our theoretical analysis and simulation results show that RD-DSSS can effectively defeat jamming attacks for anti-jamming broadcast communication without shared keys.

Spread spectrum techniques (e.g., Frequency Hopping (FH), Direct Sequence Spread Spectrum (DSSS)) have been widely used for anti-jamming wireless communications. Such techniques require that communicating devices agree on a shared secret before communication. However, it is non-trivial for two devices that do not share any secret to establish one in presence of a jammer. Recently, several schemes relying on Uncoordinated Frequency Hopping (UFH) were proposed to allow two devices to establish a secret key using Diffie-Hellman (DH) key establishment protocol in presence of jammers. Unfortunately, all these schemes are limited in efficiency. In this paper, we propose a novel scheme named USD-FH, which uses Uncoordinated Seed Disclosure in Frequency Hopping to establish a shared secret in presence of jammers. The basic idea is to transmit each DH key establishment message using a one-time pseudo-random hopping pattern and disclose the corresponding seed in an uncoordinated manner before the actual message. Due to the large number of channels available for wireless communication, the jammers cannot control all channels at the same time. When the receiver and the sender use the same channel during seed disclosure, the receiver can get the seed. If the jammer does not listen on the same channel (and thus it does not know the hopping pattern), the receiver can receive the actual message without being jammed. We validate USD-FH through both theoretical analysis and simulation. Our results show that USD-FH is much more efficient and robust than previous solutions.

In this paper, we establish a rate duality between the forward and reverse links of MIMO interference channel, where the reverse links are obtained by exchanging the roles of transmitters and receivers in the forward links, and the corresponding channel matrices are conjugate transpose of the forward channel matrices. Since the capacity region for general interference channel is unknown, we show that the forward and reverse links have the same achievable rate region by treating interference as noise under some sum power constraint. The explicit expression of the corresponding input covariance matrix transformation is provided. We discuss the connection between the proposed transformation and the MAC-BC transformations in the previous works. As an application, a duality based iterative algorithm is proposed to maximize the sum rate of MIMO interference channel under sum power constraint. We also extend the algorithm to individual power constraint. The proposed algorithms are shown to be effective by simulation.

Wireless sensor networks are considered ideal candidates for a wide range of applications. It is desirable and sometimes necessary to reprogram sensor nodes through wireless links after they are deployed to remove bugs or add new functionalities. Several approaches (e.g., Seluge, Sluice) have been proposed recently for secure code dissemination in wireless sensor networks, all as security extensions to the state-of-the- art code dissemination system named Deluge. However, existing approaches all focused on securing the propagation of code images, but overlooked the security vulnerabilities in other image management aspects such as rebooting and erasing code images. In this paper, we identify the security vulnerabilities in epidemic image management in all existing solutions to secure code dissemination in wireless sensor networks. Such vulnerabilities allow an attacker to reboot a sensor network to undesirable images or erase critical images, exposing the network to security risks. We then develop a sequence of lightweight techniques to address these vulnerabilities. Our approach takes into consideration the limited resources on current sensor platforms, and removes the security vulnerabilities without introducing significant overhead. To evaluate the feasibility of our approach, we implement the proposed approach as a remote image management system named Seluge-ImageMan, which is intended to work with Seluge, a security extension to Deluge for injecting new code images. We perform a substantial set of experiments in the WiSeNeT sensor testbed, which consists of 72 MicaZ motes, to assess the performance overhead of Seluge-ImageMan. The experimental results indicate that our approach introduces very light overhead while completing the secure remote code image management solution for wireless sensor networks.

In wireless sensor networks, it is critical to restrict the network access only to eligible sensor nodes, while messages from outsiders will not be forwarded in the networks. In this paper, we present the design, implementation, and evaluation of a secure network access system for wireless sensor networks. This paper makes three contributions: First, it develops a network admission control subsystem using Elliptic Curve public key cryptosystem to add new sensor nodes into a sensor network. The admission control subsystem employs a polynomial-based weak authentication scheme to mitigate Denial of Service (DoS) attacks against the public key cryptographic operations. Second, it implements an interface in TinyOS to provide symmetric key cryptography using the hardware security support in IEEE 802.15.4 radio components (e.g., CC2420). The hardware security can satisfy both message authentication and timely delivery requirements in real-time applications. The third contribution is an implementation of a stateless group key update scheme to update a network-wide secret key in a sensor network. We implement all the proposed techniques on Imote2 sensor platform running TinyOS and conduct an evaluation through field experiments.

Wireless sensor networks are considered ideal candidates for a wide range of applications, such as industry monitoring, data acquisition in hazardous environments, and military operations. It is desirable and sometimes necessary to reprogram sensor nodes through wireless links after deployment, due to, for example, the need of removing bugs and adding new functionalities. The process of propagating a new code image to the nodes in a wireless sensor network is referred to as code dissemination. This paper presents the design, implementation, and evaluation of an efficient, secure, robust, and DoS-resistant code dissemination system named Seluge for wireless sensor networks. Seluge is a secure extension to Deluge, an open source, state-of-the-art code dissemination system for wireless sensor networks. It provides security protections for code dissemination, including the integrity protection of code images and immunity from, to the best of our knowledge, all DoS attacks that exploit code dissemination protocols. Seluge is superior to all previous attempts for secure code dissemination, and is the only solution that seamlessly integrates the security mechanisms and the Deluge efficient propagation strategies. Besides the theoretical analysis that demonstrates the security and performance of Seluge, this paper also reports the experimental evaluation of Seluge in a network of MicaZ motes, which shows the efficiency of Seluge in practice.

Public key cryptography (PKC) has been the enabling technology underlying many security services and protocols in traditional networks such as the Internet. In the context of wireless sensor networks, elliptic curve cryptography (ECC), one of the most efficient types of PKC, is being investigated to provide PKC support in sensor network applications so that the existing PKC-based solutions can be exploited. This paper presents the design, implementation, and evaluation of TinyECC, a configurable library for ECC operations in wireless sensor networks. The primary objective of TinyECC is to provide a ready-to-use, publicly available software package for ECC-based PKC operations that can be flexibly configured and integrated into sensor network applications. TinyECC provides a number of optimization switches, which can turn specific optimizations on or off based on developers' needs. Different combinations of the optimizations have different execution time and resource consumptions, giving developers great flexibility in integrating TinyECC into sensor network applications. This paper also reports the experimental evaluation of TinyECC on several common sensor platforms, including MICAz, Tmote Sky, and Imotel. The evaluation results show the impacts of individual optimizations on the execution time and resource consumptions, and give the most computationally efficient and the most storage efficient configuration of TinyECC.

A wireless sensor network is expected to consist of a potentially large number of low-cost, low-power, and multi-functional sensor nodes that communicate over short distances through wireless links. Due to their potential to provide fine-grained sensing and actuation at a reasonable cost, wireless sensor networks are considered ideal candidates for a wide range of applications, such as industry monitoring, data acquisition in hazardous environments, and military operations.

It is difficult to implement optimal beamforming for multi-user multiple-input multiple-output (MIMO) downlink due to the high complexity. This paper proposes a low complexity generalized beamforming (GBF) scheme combined with an efficient user selection to maximize the weighted sum-rate. For each user, the outputs of the multiple antennas are combined with a receive GBF vector to create an equivalent multiple-input single-output (MISO) effective channel. First, user selection and receive GBF vectors are jointly optimized to construct a group of preferable effective channels. Then, transmit GBF vectors are obtained by zero-forcing over these effective channels. Simulation results show significant gain over currently known suboptimal schemes in various scenarios.

Many sensor network applications require sensors' locations to function correctly. Despite the recent advances, location discovery for sensor networks in hostile environments has been mostly overlooked. Most of the existing localization protocols for sensor networks are vulnerable in hostile environments. The security of location discovery can certainly be enhanced by authentication. However, the possible node compromises and the fact that location determination uses certain physical features (e.g., received signal strength) of radio signals make authentication not as effective as in traditional security applications. This article presents two methods to tolerate malicious attacks against range-based location discovery in sensor networks. The first method filters out malicious beacon signals on the basis of the “consistency” among multiple beacon signals, while the second method tolerates malicious beacon signals by adopting an iteratively refined voting scheme. Both methods can survive malicious attacks even if the attacks bypass authentication, provided that the benign beacon signals constitute the majority of the beacon signals. This article also presents the implementation and experimental evaluation (through both field experiments and simulation) of all the secure and resilient location estimation schemes that can be used on the current generation of sensor platforms (e.g., MICA series of motes), including the techniques proposed in this article, in a network of MICAz motes. The experimental results demonstrate the effectiveness of the proposed methods, and also give the secure and resilient location estimation scheme most suitable for the current generation of sensor networks.

Broadcast authentication is a critical security service in wireless sensor networks. There are two general approaches for broadcast authentication in wireless sensor networks: digital signatures and μTESLA-based techniques. However, both signature-based and μTESLA-based broadcast authentication are vulnerable to Denial of Services (DoS) attacks: An attacker can inject bogus broadcast packets to force sensor nodes to perform expensive signature verifications (in case of signature-based broadcast authentication) or packet forwarding (in case of μTESLA-based broadcast authentication), thus exhausting their limited battery power. This paper presents an efficient mechanism called message-specific puzzle to mitigate such DoS attacks. In addition to signature-based or μTESLA-based broadcast authentication, this approach adds a weak authenticator in each broadcast packet, which can be efficiently verified by a regular sensor node, but takes a computationally powerful attacker a substantial amount of time to forge. Upon receiving a broadcast packet, each sensor node first verifies the weak authenticator, and performs the expensive signature verification (in signature-based broadcast authentication) or packet forwarding (in μTESLA-based broadcast authentication) only when the weak authenticator is valid. A weak authenticator cannot be precomputed without a non-reusable (or short-lived) key disclosed only in a valid packet. Even if an attacker has intensive computational resources to forge one or more weak authenticators, it is difficult to reuse these forged weak authenticators. Thus, this weak authentication mechanism substantially increases the difficulty of launching successful DoS attacks against signature-based or μTESLA-based broadcast authentication. A limitation of this approach is that it requires a powerful sender and introduces sender-side delay. This article also reports an implementation of the proposed techniques on TinyOS, as well as initial experimental evaluation in a network of MICAz motes.