In this paper, we study the potential of interference exploitation symbol-level precoding (SLP), focusing on maximizing mutual information (MI) in multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. As traditional MI expressions based on Gaussian signalling do not apply to SLP, we firstly derive the expression of the MI for SLP based on finite alphabet inputs, which is shown to be a nonlinear and non-concave function of the precoder. Subsequently, we formulate an optimization problem aimed at maximizing the MI without investing additional transmit signal power. Due to the original problem being non-convex, we design an iterative gradient-projection (GP)-based algorithm by deriving the gradient of the MI with respect to the SLP signal matrix. To alleviate the heavy reliance on Monte Carlo evaluations, we further propose an effective variant that employs the channel cut-off rate (CR) as a tractable optimizaiton metric within the GP framework. Moreover, we develop a low-complexity algorithm by deriving a closed-form approximation of the MI and addressing the resulting problem through successive convex approximation (SCA), thereby obtaining a near-optimal solution to the original formulation. Numerical results verify that the proposed MI-optimization SLP (MIO-SLP) exhibits a noticeable gain over traditional SLP schemes in terms of MI, and achieves close-to-optimal MI performance, while ensuring satisfactory error rate.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems based on the concept of exploiting constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Moreover, an “indirect power scaling” method is proposed for the precoding design based on DAC quantization to enhance the ISAC performance. From the simulation results, we can observe that the proposed precoding schemes can achieve satisfactory ISAC performance with low complexity. In the considered ISAC systems, increasing the quantization bits for DAC and PS quantizations can improve the ISAC performance, and the gain for DAC quantization is more pronounced.

Pattern reconfigurable multi-antenna array can reshape the wireless channel with different radiation patterns to achieve performance gains. In this paper, we exploit the benefits of reconfigurable antennas to maximize the signal-to-noise-ratio (SNR) of users in the downlink of a multi-user multi-input single-output system, when zero-forcing (ZF) precoder is employed. We use the sampling vectors to reveal the physical mechanism of reconfigurable patterns on the wireless channel and further formulate the SNR maximization pattern design problem. For the single-pattern case where all the antenna elements adopt the same radiation pattern, reconfigurable patterns can realize the modification of correlation among each subchannel and redistribute channel gains of all scattering paths, where a Majorization-Minimization (MM) scheme with low complexity is proposed to obtain the optimal solution. For the multi-pattern case where different antenna elements can employ different radiation patterns, a ℓ2-norm approximation problem is formulated to achieve the solution where the target channel is determined by a Randomization-Orthogonalization (RO) scheme. Numerical results demonstrate that reconfigurable patterns can further improve the bit error rate and throughput performance over the legacy MU-MISO system.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems with the assistance of constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes based on them. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Simulation results have been implemented to validate the superiority of the proposed algorithms. When the number of elements in the quantization sets is fixed, the ISAC performance of the precoding scheme based on DAC quantization is superior to that of the precoding scheme based on PS quantization, thanks to the more dispersed level distribution of DAC quantization.

In this work, we explore symbol-level precoding (SLP) and efficient decoding strategies for downlink transmission in multi-user multiple-input multiple-output (MU-MIMO) systems. We specifically study scenarios where the base station (BS) sends multiple multi-level modulated data streams to users for decoding. We formulate an optimization problem for joint symbollevel transmit precoding and receive combining. However, the receive combining matrix is dependent on the transmit symbols in the joint design scheme, thus, we employ maximum likelihood detection (MLD) method at the receiver side. we demonstrate that the smallest singular value of the precoding matrix significantly affects the MLD performance, while traditional SLP scheme returns a rank-one precoding matrix, which results in inferior error-rate performance to users. To overcome this challenge, we propose a novel channel state information (CSI)-Free SLP scheme that employs semidefinite programming (SDP) method to enable SLP technique in systems utilizing MLD decoding, where the design of the precoding matrix depends only on the modulated data symbols. Numerical simulations confirm that our proposed scheme substantially outperforms the traditional block diagonalization methods.

Reconfigurable antennas (RAs) can actively adjust their radiation patterns to meet the demands of the communication network. In this paper, we investigate precoding and radiation pattern design in a multi-user multi-input single-output (MUMISO) downlink system. To fully exploit RAs, we introduce pattern sample vectors corresponding to each user's sub channel to assess the impact of pattern reconfiguration across all scattering paths. Based on this, we formulate the design problem by adopting closed-form precoding to optimize the radiation patterns for maximizing the sum rate. Specifically, we propose two methods: a heuristic approach using singular value optimization (SVO) and a successive convex approximation (SCA)-based minimization strategy to achieve desired patterns. These methods effectively address the non-convexity in the objective function. Numerical results demonstrate that the reconfigurable radiation pattern can strategically manipulate the wireless channel, leading to significant enhancements in system performance.

Reconfigurable antennas have excellent dynamic adaptability to alter their operational state in response to environmental changes, and is considered as one potential technology towards future communication systems. However, acquiring accurate channel state information (CSI) for all radiation patterns with low pilot overheads imposes a significant challenge in pattern reconfigurable MIMO (PR-MIMO) communication systems. To address this issue, in this paper we propose a novel two-stage channel estimation approach based on antenna grouping (AG) to obtain the CSIs for all radiation patterns efficiently. In the first stage, all antennas at the transmitter employ the same radiation pattern, while in the second stage, the antennas at the transmitter are divided into groups according to the number of radiation patterns, where antennas in different groups employ different radiation patterns, while antennas within the same group employ the same radiation pattern. When the exact number of channel paths is known, a closed-form channel extrapolation algorithm and a singular value decomposition (SVD)-based channel extrapolation algorithm are proposed, depending on the value of the channel paths and whether the angle information is known. Extensive simulation results illustrate that the proposed algorithms can accurately extrapolate the CSI of all radiation patterns, with dramatically reduced pilot overheads compared to the conventional channel estimation methods.

Pattern reconfigurable multi-antenna array can reshape the wireless channel with different radiation patterns to achieve performance gains. In this paper, we exploit the benefits of reconfigurable antennas to maximize the signal-to-noise-ratio (SNR) of users in the downlink of a multi-user multi-input singleoutput system, when zero-forcing (ZF) precoder is employed. We use the sampling vectors to reveal the physical mechanism of reconfigurable patterns on the wireless channel, and show that reconfigurable patterns can redistribute the channel gains of all scattering paths. We consider the practical case where all the antenna elements adopt the same radiation pattern, and we propose to optimize the radiation pattern of the reconfigurable array for received SNR maximization, where the Successive Convex Approximation (SCA) algorithm and Majorization-Minimization (MM) scheme are proposed to obtain a promising solution. Numerical results demonstrate that reconfigurable patterns can further improve the bit error rate and throughput performance over the legacy MU-MISO system.

In this paper, we investigate symbol-level precoding (SLP) and efficient decoding techniques for downlink transmission, where we focus on scenarios where the base station (BS) transmits multiple quadrature amplitude modulation (QAM) constellation streams to users equipped with multiple receive antennas. We begin by formulating a symbol-level joint design scheme aimed at collaboratively optimizing the transmit precoding and receive combining matrices. This coupled problem is addressed by employing the alternating optimization (AO) method, and closed-form solutions are derived by analyzing the obtained two subproblems. Furthermore, to address the dependence of the receive combining matrix on the transmit signals, we switch to maximum likelihood detection (MLD) method for decoding. Notably, we have demonstrated that the smallest singular value of the precoding matrix significantly impacts the performance of MLD method. Specifically, a lower value of the smallest singular value results in degraded detection performance. Additionally, we show that the traditional SLP matrix is rank-one, making it infeasible to directly apply MLD at the receiver end. To circumvent this limitation, we propose a novel symbol-level smallest singular value maximization problem, termed SSVMP, to enable SLP in systems where users employ the MLD decoding approach. Moreover, to reduce the number of variables to be optimized, we further derive a more generic semidefinite programming (SDP)-based optimization problem. Numerical results validate the effectiveness of our proposed schemes and demonstrate that they significantly outperform the traditional block diagonalization (BD)-based method.

We study constructive interference based block-level precoding (CI-BLP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. Specifically, our aim is to extend the analysis on CI-BLP to the case when the number of symbol slots in a given transmission block is smaller than the number of users. To this end, we mathematically prove the feasibility of using the pseudo-inverse to obtain a closed-form structure of the optimal CI-BLP precoding matrix. Similar to the case when the number of symbol slots in a given transmission block is not smaller than the number of users, we show that a quadratic programming (QP) optimization on simplex can be constructed. We also design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can achieve a flexible trade-off between communication performance and execution time by modifying the maximum number of iterations. We further analyze the convergence and complexity of the proposed algorithm. Numerical results validate our analysis and the optimality of the QP optimization, and further show that the proposed ADMM algorithm can provide satisfactory results in dozens of iterations, which motivates the use of CI-BLP in practical wireless systems.

This paper designs a parallel solution framework for constructive interference based symbol-level precoding (CI-SLP) in the downlink of a multi-user multiple-input single-output (MU-MISO) system. Most existing works on SLP have considered the sum-power constraint, while in practical systems each transmit antenna is equipped with its dedicated power amplifier. Therefore, it is more realistic to design SLP approaches that incorporate the per-antenna power constraint (PAPC). In this paper, we focus on two specific PAPC-based problems: the constructive interference per-antenna power constraint signal to interference plus noise ratio (SINR) balancing (CI-PASB) problem and the constructive interference per-antenna peak power minimization (CI-PAPM) problem. Similar to sum-power constraint, for the CI-PASB problem, we demonstrate that it is separable, allowing the existing parallel proximal Jacobian alternating direction method of multipliers (PJ-ADMM) algorithm to be directly used. As for the CI-PAPM problem, although it is unseparable, we can leverage the established duality to obtain its solution based on the solution of the corresponding CI-PASB problem. Numerical results verify our proposed parallel methods and show that they are more efficient than the existing centralized schemes, which showcases the advantages of parallel computing and promotes the implementation of CI precoding under practical PAPC scenarios.

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE for both PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed for PSK and QAM, respectively. Moreover, we design an unsupervised SLE-based neural network (SLE-Net) to unfold the proposed iterative algorithm, which helps enhance the interpretability of the neural network. By carefully designing the loss function of the SLE-Net, the time-complexity of the network can be reduced effectively. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods.

In this paper, we investigate the constructive interference (CI) based symbol-level precoding (SLP) design for integrated sensing and communication (ISAC) systems, where a multi-antenna base station (BS) serves multiple single-antenna communication users while simultaneously detecting targets of interest. Specifically, the minimum communication CI scaling factor among the users is maximized under radar performance constraint and power constraint. In order to solve the proposed optimization problem, two groups of approximate feasible domains are adopted to transform the optimization problem into convex. In order to improve the efficiency of the proposed precoding scheme, we adopt a modified Hooke-Jeeves pattern search algorithm for the convex subproblems. We further propose a weighted optimization scheme which considers the tradeoff between radar performance and communication performance as the objective function. By analyzing the Lagrangian function and Karush-Kuhn-Tucker (KKT) condition of the weighted optimization problem, we formulate the corresponding dual problem, which is a simple quadratic programming (QP) problem and can be easily solved. In addition, we further extend the proposed CI precoding scheme from symbol level to block level, in order to be more consistent with the currently used communication systems and achieve better ISAC performance. Extensive simulation results are provided to demonstrate the advantages and the effectiveness of the proposed symbol-scaling based CI-SLP design and CI-based block-level precoding (CI-BLP) design in ISAC systems.

A robust full-space physical layer security (PLS) transmission scheme is proposed in this paper considering the full-space wiretapping challenge of wireless networks supported by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Different from the existing schemes, the proposed PLS scheme takes account of the uncertainty on the eavesdropper’s position within the 360◦ service area offered by the STAR-RIS. Specifically, the large system analytical method is utilized to derive the asymptotic expression of the average security rate achieved by the security user, considering that the base station (BS) only has the statistical information of the eavesdropper’s channel state information (CSI) and the uncertainty of its location. To evaluate the effectiveness of the proposed PLS scheme, we first formulate an optimization problem aimed at maximizing the weighted sum rate of the security user and the public user. This optimization is conducted under the power allocation constraint, and some practical limitations for STAR-RIS implementation, through jointly designing the active and passive beamforming variables. A novel iterative algorithm based on the minimum mean-square error (MMSE) and cross-entropy optimization (CEO) methods is proposed to effectively address the established non-convex optimization problem with discrete variables. Simulation results indicate that the proposed robust PLS scheme can effectively mitigate the information leakage across the entire coverage area of the STAR-RIS-assisted system, leading to superior performance gain when compared to benchmark schemes encompassing traditional RIS-aided scheme.

While symbol-level (SL) precoders have been shown to improve transmission performance by treating multi-user interference (MUI) as a useful resource, the SL precoders only employ uniform modulation for all downlink users, and the incurred complexity increases linearly with the block length. In this letter, we investigate the possibility of mixed-modulations interference exploitation (IE) for satellite communications, at a sub-block level. By exploiting the specific detection regions of constellation points of different modulations, a novel sub-block level mixed-modulation (BL-MIE) design is proposed, guaranteeing that MUI is always constructive in each sub-block duration, regardless of the users’ heterogeneous modulation schemes. Compared to the classic SL design, it is proved that the BL-MIE provides complexity reduction on the order of square root of the sub-block length, i.e., ${\mathcal{O}}(\sqrt{n})$, with n denoting the number of symbols per sub-block. Hence, it well strikes the balance between the performance and complexity. Simulation demonstrates that the proposed designs significantly outperform the benchmarks in terms of power consumption and throughput performance.

This paper addresses the design of the receive combining matrix in a multiuser multiple-input multiple-output (MU-MIMO) downlink system, where the base station (BS) employs symbol-level precoding (SLP) to transmit multiple data streams to multiple users with multiple antennas. Unlike in the single-antenna user scenario, the design of the receive combining matrix becomes crucial in this context. To overcome the challenge of the receive combining matrix's dependency on the transmit signals, we propose a practical scheme utilizing the interference rejection combiner (IRC) for signal decoding. However, directly applying the IRC receiver to the considered MU-MIMO system presents challenges due to the rank-one transmit precoding matrix. To address this issue, we propose a new regularized IRC (RIRC) receiver. The problem is tackled by using the alternating optimization (AO) method, enabling the derivation of an optimal solution structure for the transmit precoding matrix. Numerical results demonstrate the substantial performance gain of the practical SLP scheme with the RIRC receiver over conventional Block Diagonalization (BD) based approach.

We study constructive interference based block-level beamforming (CI-BLB) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. CI-BLB achieves im-proved performance over the traditional CI-based symbol-level beamforming (CI-SLB) method, because of its more intelligent power allocation scheme over the considered block of symbol slots. In this paper, we design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can efficiently solve QP problems. We analyze the convergence and complexity of the proposed algorithm. Nu-merical results validate the optimality of the proposed algorithm, and further show that the proposed algorithm offers a flexible performance-complexity tradeoff by limiting the maximum num-ber of iterations, which motivates the use of CI - BLB in practical wireless systems.

Recently, fingerprint-based localization has become a promising solution for indoor positioning because of its great performance in complex multipath environments. However, the extensive time and labor effort of constructing the radio map has become the bottleneck that hinders the adaptation of fingerprint-based localization in practice. In this article, we propose a novel cost-efficient radio map construction scheme, which relies on the fingerprint measurements from only a small number of reference points (RPs) via graph signal sampling and recovery techniques. First, using the topological characteristics of RPs, we model the radio map as a graph and design the angle fingerprint for the band-limited graph signal. Subsequently, the radio map is built based on graph clustering, sampling set selection and signal recovery. Extensive simulations are performed in a geometry-based ray tracing signal propagation model, which demonstrates that the proposed method can recover the radio map with low-collection cost and outperform existing solutions in terms of fingerprint accuracy and localization performance.

This article investigates block-level interference exploitation (IE) precoding for multiuser multiple-input-single-output (MU-MISO) downlink systems. To overcome the need for symbol-level IE precoding to frequently update the precoding matrix, we propose to jointly optimize all the precoders or transmit signals within a transmission block. The resultant precoders only need to be updated once per block, and while not necessarily constant over all the symbol slots, we refer to the technique as block-level slot-variant IE precoding. Through a careful examination of the optimal structure and the explicit duality inherent in block-level power minimization (PM) and signal-to-interference-plus-noise ratio (SINR) balancing (SB) problems, we discover that the joint optimization can be decomposed into subproblems with smaller variable sizes. As a step further, we propose block-level slot-invariant IE precoding by adding a structural constraint on the slot-variant IE precoding to maintain a constant precoder throughout the block. A novel linear precoder for IE is further presented, and we prove that the proposed slot-variant and slot-invariant IE precoding share an identical solution when the number of symbol slots does not exceed the number of users. Numerical simulations demonstrate that the proposed precoders achieve a significant complexity reduction compared against benchmark schemes, without sacrificing performance.

Consider a multiuser multiple-input multiple-output (MU-MIMO) downlink system in which the base station (BS) sends multiple data streams to multi-antenna users via symbol-level precoding (SLP), where the optimization of receive combining matrix becomes crucial, unlike in the single-antenna user scenario. We begin by introducing a joint optimization problem on the symbol-level transmit precoder and receive combiner. The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions. To address the challenge that the above joint design is difficult to implement, we propose a more practical scheme where the receive combining optimization is replaced by the interference rejection combiner (IRC), which is however difficult to directly use because of the rank-one transmit precoding matrix. Therefore, we introduce a new regularized IRC (RIRC) receiver to circumvent the above issue. Numerical results demonstrate that the practical SLP-RIRC method enjoys only a slight communication performance loss compared to the joint transmit precoding and receive combining design, both offering substantial performance gains over the conventional BD-based approaches.

In this paper, we present an innovative symbol-level precoding (SLP) approach for a wideband multi-user multi-input multi-output (MU-MIMO) downlink integrated sensing and communications (ISAC) system employing faster-than-Nyquist (FTN) signaling. Our proposed technique minimizes the minimum mean squared error (MMSE) for the sensed parameter estimation while ensuring the communication per-user quality-of-service through the utilization of constructive interference (CI) methodologies. While the formulated problem is non-convex in general, we tackle this issue using proficient minorization and successive convex approximation (SCA) strategies. Numerical results substantiate that our FTN-ISAC-SLP framework can increase communication throughput by up to 20% while reducing sensing MMSE by about 1 dB.

In this paper, we study symbol-level precoding (SLP) design for time-domain peak-to-average power ratio (PAPR) reduction in a multi-user MISO-OFDM transmission through the idea of constructive interference (CI). Specifically, we design the precoded transmit signals that minimize the symbol-level transmit power subject to per-antenna time-domain PAPR constraint and CI condition, using the knowledge of both data information and channel state information (CSI), based on which a non-convex problem is established. This non-convex problem is transformed into a convex one by the vectorization and relaxation method. For the relaxed problem, we employ Lagrangian method and Karush-Kuhn-Tucker (KKT) conditions to obtain a closed-form expression on the precoded signals as a function of the Lagrangian multipliers. Subsequently, we study the dual problem and obtain the optimal Lagrangian multipliers via the proposed alternating iterative algorithm. We further consider the practical communication scenario with imperfect CSI, where the original CI constraint is transformed into a probabilistic constraint in order to achieve robustness against statistically CSI errors. Numerical results validate that the proposed low-complexity algorithm achieves an enhanced performance over existing methods in terms of transmit power, PAPR and computation complexity, both in ideal perfect CSI and practical imperfect CSI cases.

Pattern reconfigurable multiple-input multiple-output (PR-MIMO) can manipulate the wireless channel according to different communication requirements. In this paper, we discuss the potential of constructive interference (CI)-based symbol-level precoding (CI-SLP) in PR-MIMO communication systems. The joint design problem that optimizes the SLP strategy and the radiation pattern of PR-MIMO for phase-shift keying (PSK) modulation is formulated to maximize the worst serviced user’s communication quality. Since the optimization variables are softly-coupled, we employ the alternating optimization framework to decompose the joint design problem into the SLP design sub-problem and the pattern design sub-problem. We simplify the pattern design sub-problem and propose an interior-point algorithm, where a sequential optimization-based scheme is further proposed as a sub-optimal solution with low complexity. Furthermore, the discussion is extended to quadrature amplitude modulation (QAM) modulated systems, where a special stopping criterion is proposed to guarantee the performance gain of the proposed scheme. The practical realization of the designed reconfigurable antenna array is also discussed, where we propose a design scheme using programmable metasurface antennas based on time-division switching to enable quick and adaptive pattern reconfiguration. Numerical results demonstrate that the radiation pattern configurability can further enhance the benefit of SLP over conventional precoding approaches.

In this paper, we study the interference exploitation precoding in the presence of distortion from nonlinear power amplifiers (PAs) in multi-user multiple-input single-output (MU-MISO) downlink communication systems. We consider the memoryless polynomial model of nonlinear PAs, which is incorporated into the symbol-level precoding (SLP) design to allow the PA nonlinearities in the constructive interference (CI) exploitation. The optimization problem that aims to enhance the signal-to-interference-plus-noise ratio (SINR) without investing additional transmit signal power is formulated for both PSK and QAM signaling. Since the original optimization problem is nonconvex, we first introduce auxiliary variables to transform the optimization problem and adopt the alternating optimization framework for the new optimization problem. For non-convex subproblems, additional auxiliary variables are introduced and several approximations are employed to transform the problem into a semidefinite programming (SDP) form, where the semidefinite relaxation (SDR) method is adopted to obtain feasible solutions. In order to reduce the computational cost of the iterative algorithm, we further propose a low-complexity algorithm for the original PA-aware SLP optimization problem. Numerical results verify the superiority of our proposed PA-aware SLP approach in the presence of nonlinear PAs in the MU-MISO downlink in terms of the error-rate performance over the state-of-the-art.

This paper addresses the constructive interference (CI) precoding problem under per-antenna power constraint (PAPC) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. In this setup, we extend the phase rotation metric and symbol scaling metric of CI precoding under sum power constraint (SPC) to the scenario of PAPC. Against SPC, the scenario of PAPC represents a practical constraint acknowledging that each antenna would have its dedicated power amplifier. Nevertheless, the optimization problem of CI-PAPC precoding becomes more challenging than that under SPC. By analyzing the KKT conditions and leveraging the generalized matrix inverse theory, we obtain a closed-form structure of the CI-PAPC precoder as a function of introduced variables, which facilitates a low-complexity solver. Since the power constraint of each antenna is not always active under PAPC, existing iterative schemes under CI-SPC are no longer applicable. Therefore, the primal-dual interior point method (IPM) is employed to solve the simplified problem with reduced complexity and fast convergence. Simulation results verify our mathematical derivations and demonstrate that our proposed method can reduce the complexity of solving CI-PAPC problem while preserving the error-rate performance, which promotes the practical implementation of CI precoding in real PAPC scenarios.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden’s minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

We propose a parallel constructive interference (CI)-based symbol-level precoding (SLP) approach for massive connectivity in the downlink of multiuser multiple-input single-output (MU-MISO) systems, with only local channel state information (CSI) used at each processor unit and limited information exchange between processor units. We explore and reveal the separability of the SLP model. By reformulating the power minimization (PM) SLP problem and exploiting the separability of the corresponding reformulation, the original problem is decomposed into several parallel subproblems via the ADMM framework with closed-form solutions, leading to a substantial reduction in computational complexity. The sufficient condition for guaranteeing the convergence of the proposed approach is derived, based on which an adaptive parameter tuning strategy is proposed to accelerate the convergence rate. To avoid the large-dimension matrix inverse operation, an efficient algorithm is proposed by employing the standard proximal term and by leveraging the singular value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted to fully eliminate the matrix inversion, and a parallel inverse-free SLP (PIF-SLP) algorithm is finally obtained. Numerical results validate our derivations above, and demonstrate that the proposed PIF-SLP algorithm can significantly reduce the computational complexity compared to the state-of-the-arts.

In this paper, we focus on simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multiuser multiple-input single-output (MISO) interference channels (ICs), where the RIS can serve the users in both forward and backward half-spaces at the same time by transmitting and reflecting incident signals. To suppress the inevitable inter-user interference, we adopt improper Gaussian signaling (IGS) and propose to jointly optimize the beamforming vectors of APs and the passive transmission and reflection coefficients of RIS to maximize the minimum achievable information rate among these users. In order to address the non-convex optimization problem, we provide an efficient iterative optimization algorithm to attain high-quality solutions. Numerical results demonstrate the superiority of IGS over conventional proper Gaussian signaling in STAR-RIS assisted MU-MISO ICs.

This paper proposes an indoor localization system by exploring the temporal and frequency features of complex Channel State Information (CSI) under the fifth-generation (5G) cellular network. In particular, we first acquire some successive raw CSIs from multiple base stations (BSs). Then, amplitude-based sequences are obtained by employing a sliding window moving over a consecutive time step on CSI amplitudes. Moreover, the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are adopted to learn robust time-frequency features from the constructed CSI sequences. To emphasize the contributions of the critical elements to final location estimations, we utilize an attention mechanism to assign the local learned features with different weights. We implement the proposed scheme and verify its performance with extensive experiments in some representative indoor scenes.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter-wave (mmWave) system. Specifically, analytical derivations are performed to obtain the closed-form expression of the warden’s minimum detection error probability (DEP) considering the practical assumption. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

We investigate constructive interference (CI)-based symbol-level precoding (SLP) in large-scale systems with massive connectivity of users to minimize the transmit power subject to the instantaneous signal-to-interference-plus-noise-ratio (SINR) and CI constraints. By converting the considered problem into a novel separable formulation, we reveal the existence of separability in SLP, which is therefore well-suited for decomposition. The proximal Jacobian alternating direction method of multipliers (PJ-ADMM) framework is adopted to decompose the reformulated problem into multiple subproblems, which can be solved in parallel with closed-form solutions. We further linearize the second-order terms by approximation, which leads to a parallelizable first-order fast solution to SLP. Our derivations are validated by simulation results, which also show that our algorithm can provide optimal performance with substantially lower computational complexity than state-of-the-art algorithms.

This paper reveals the latent relation inherent in two typical problems in constructive interference (CI)-based symbol-level precoding (SLP). One is the power minimization (PM) problem subject to instantaneous signal-to-interference-plus-noise ratio (SINR) constraints, and the other is the weighted max-min SINR balancing (SB) problem with the symbol-level transmit power budget. In particular, we establish an explicit duality between the PM-SLP and SB-SLP problems, where we prove that one of the two problems can be uniquely mapped to the other. The proposed duality not only provides insights into the intrinsic structure of the problems and solutions but also facilitates obtaining the solution to the SB-SLP given the solution to the PM-SLP without the need for one-dimension search, and vice versa. We further propose a closed-form power scaling algorithm to solve the SB-SLP via PM-SLP, by which the separability of the PM-SLP can be leveraged to solve the two problems simultaneously. Numerical results demonstrate our derivations on the duality as well as the efficiency of the proposed power scaling algorithm.

Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint.

Symbol-level precoding (SLP) can fully exploit the multi-user interference in the downlink. This paper investigates fast SLP algorithms for phase-shift keying (PSK) and quadrature amplitude modulation (QAM). In particular, we prove that the weighted max-min signal-to-interference-plus-noise ratio (SINR) balancing (SB) SLP problem with PSK signaling is not separable, which is contrary to the power minimization (PM) SLP problem, and accordingly, existing decomposition methods are not applicable. To tackle this issue, we establish an explicit duality between the SB-SLP and PM-SLP problems with PSK modulation. The proposed duality enables simultaneously obtaining the solutions to the SB-SLP and PM-SLP problems. We concurrently propose a closed-form power scaling algorithm to address the SB-SLP problem by the solution to the PM-SLP problem, via which the separability can be leveraged to decompose the problem. In terms of QAM signaling, a succinct model is used to formulate the PM-SLP problem and convert it into a separable equivalent. The new problem is decomposed into several simple parallel subproblems with closed-form solutions, employing the proximal Jacobian alternating direction method of multipliers (PJ-ADMM). We further prove that the proposed duality can be generalized to the multi-level modulation case, based on which a power scaling parallel inverse-free algorithm is proposed to solve the SB-SLP problem with QAM signaling. Numerical results show that the proposed algorithms offer optimal performance with lower complexity than the state-of-the-art.

For fingerprint-based localization, the time-consuming and labor-intensive construction of offline radio map is the bottleneck which hinders its large-scale implementation. In this paper, we propose a radio map extrapolation and localization algorithm by exploiting the angles and delays of specular multipath components. First, using the concept of virtual anchor nodes (VANs), we calculate the virtual transmitter (VT) of each multipath component according to angle and delay information measured at a known point. By estimating the positions of reflector with these VTs, the angles and delays of the uplink multipath components transmitted at other locations in the indoor environment are extrapolated. Then, a channel fingerprint composed of the extrapolated angles and delays is proposed to represent the channel response in the angle-delay domain, which is spatially unique and discriminative. Thus, the radio map constructed such can significantly reduce the labor and time costs. Lastly, a convolutional neural network (CNN) is applied for indoor localization. The performance of the proposed fingerprint extrapolation and localization method is validated through extensive simulations with a ray-tracing channel model, which exhibits promising localization performance for our proposed scheme with reduced construction costs.

In this paper, we propose a constructive interference (CI)-based block-level precoding (CI-BLP) approach for the downlink of a multi-user multiple-input single-output (MU-MISO) communication system. Contrary to existing CI precoding approaches which have to be designed on a symbol-by-symbol level, here a constant precoding matrix is applied to a collection of symbols within a given transmission block, thus significantly reducing the computational costs over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP optimization problem only needs to be solved once per block. For both PSK and QAM modulation, we formulate an optimization problem to maximize the minimum CI effect over the block subject to a block- rather than symbol-level power budget. We mathematically derive the optimal precoding matrix for CI-BLP as a function of the Lagrange multipliers in closed form. By formulating the dual problem, the original CI-BLP optimization problem is further shown to be equivalent to a quadratic programming (QP) optimization. Numerical results validate our derivations, and show that the proposed CI-BLP scheme achieves improved performance over the traditional CI-SLP method, thanks to the relaxed power constraint over the considered block of symbol slots.

Reconfigurable multiple-input multiple-output can provide performance gains over traditional MIMO by reshaping the channels, i.e., introducing more channel realizations. In this paper, we focus on the achievable rate maximization pattern design for reconfigurable MIMO systems. Firstly, we introduce the matrix representation of pattern reconfigurable MIMO (PR-MIMO), based on which a pattern design problem is formulated. To further reveal the effect of the radiation pattern on the wireless channel, we consider pattern design for both the single-pattern case where the optimized radiation pattern is the same for all the antenna elements, and the multi-pattern case where different antenna elements can adopt different radiation patterns. For the single-pattern case, we show that the pattern design is equivalent to a redistribution of gains among all scattering paths, and an eigenvalue optimization based solution is obtained. For the multi-pattern case, we propose a sequential optimization framework with manifold optimization and eigenvalue decomposition to obtain near-optimal solutions. Numerical results validate the superiority of PR-MIMO systems over traditional MIMO in terms of achievable rate, and also show the effectiveness of the proposed solutions.

In this paper, the signal recovery problem of incomplete ocean data based on graph signal processing is studied. In order to properly process the sparse and inhomogeneous data, the ocean data set is defined as a graph signal and its spatio-temporal characteristics is analyzed. The data are analyzed using the spatial smoothness of the signal based on topology, the correlation of time-varying signals over time, and the low-rank nature of the signal. Based on that, a signal recovery optimization problem is established and the optimization problem is solved via the alternating optimization and alternating direction method of multipliers (ADMM) framework. Simulations based on real-world dataset are performed to reveal the performance gain of the proposed approach.

In this paper, we focus on the pattern reconfigurable multiple-input multiple-output (PR-MIMO), a technique that has the potential to bridge the gap between electro-magnetics and communications towards the emerging Electro-magnetic Information Theory (EIT). Specifically, we focus on the pattern design problem aimed at maximizing the channel capacity for reconfigurable MIMO communication systems, where we firstly introduce the matrix representation of PR-MIMO and further formulate a pattern design problem. We decompose the pattern design into two steps, i.e., the correlation modification process to optimize the correlation structure of the channel, followed by the power allocation process to improve the channel quality based on the optimized channel structure. For the correlation modification process, we propose a sequential optimization framework with eigenvalue decomposition to obtain near-optimal solutions. For the power allocation process, we provide a closed-form power allocation scheme to redistribute the transmission power among the modified subchannels. Numerical results show that the proposed pattern design scheme offers significant improvements over legacy MIMO systems, which motivates the application of PR-MIMO in wireless communication systems.

Constructive Interference (CI) has shown great advantages in improving security and reliability of communication systems, which utilizes channel state information (CSI) and knowledge of the instantaneous information data on symbol level. In this paper, we explore the CI-based secure precoding problem under a dual-hop and half-duplex downlink transmission relay system in the presence of an eavesdropper. We propose to jointly optimize the precoding strategy at the source and at the relay through an alternating optimization process. To alleviate the high computational costs and circumvent the difficulty of practical implementation, we propose a low-complexity iterative algorithm for the optimization, where we use Karush-Kuhn-Tucker (KKT) conditions to analyze and simplify the previous optimization problem at the relay. Numerical results show that the proposed algorithm can achieve an improved performance compared with traditional zero-forcing/regularized zero-forcing (ZF/RZF) methods and significantly degrade the eavesdropper’s performance.

This paper investigates the security problem of a multi-eavesdrop multiple-input-single-output (MISO) wiretap channel, where an N-antenna transmitter communicates with a single-antenna legitimate user in the presence of multiple single-antenna smart eavesdroppers. To overcome the security risk of the traditional secure constructive interference-based (CI-based) scheme when facing the smart eavesdroppers, we propose a novel deception scheme (DS) via a random transmission strategy, where the eavesdroppers are expected to decode the deception symbols correctly but unable to distinguish the authenticity of the decoded symbol. Then, an efficient algorithm is proposed for the deception signal-to interference-plus-noise (SINR)-balancing problem when perfect channel state information (CSI) is assumed. Furthermore, we consider a practical scenario where only imperfect CSI is available, and explore two different methods for the deception optimization problem, i.e., convexification relaxation approach (CRA) and Lagrangian relaxation approach (LRA), respectively. For both CSI cases, a closed-form solution to the considered CI-based deception scheme is obtained. Simulation results validate the superiority of the proposed approach over traditional secure precoding schemes, and also demonstrate the significant computation efficiency improvements for the proposed algorithms.

We study closed-form interference-exploitation precoding for multi-level modulations in the downlink of multi-user multiple-input single-output (MU-MISO) systems. We consider two distinct cases: first, when the number of served users is not larger than the number of transmit antennas at the base station (BS), we mathematically derive the optimal precoding structure based on the Karush-Kuhn-Tucker (KKT) conditions. By formulating the dual problem, the precoding problem is transformed into a pre-scaling operation using quadratic programming (QP) optimization. We further consider the case where the number of served users is larger than the number of transmit antennas at the BS. By employing the pseudo inverse, we show that the optimal solution of the pre-scaling vector is equivalent to a linear combination of the right singular vectors corresponding to zero singular values, and derive the equivalent QP formulation. We also present the condition under which multiplexing more streams than the number of transmit antennas is achievable. For both considered scenarios, we propose a modified iterative algorithm to obtain the optimal precoding matrix, as well as a sub-optimal closed-form precoder. Numerical results validate our derivations on the optimal precoding structures for multi-level modulations, and demonstrate the superiority of interference-exploitation precoding for both scenarios.

In this paper, a novel training beam sequence design for multiuser millimeter wave tracking systems is proposed. For each receiver, a single-path channel model is firstly investigated, where we introduce a maximum a posteriori (MAP) criterion to estimate the time-varying angle of departure (AoD), followed by an extended Kalman filter to update the stale complex path gain. We then employ training beam sequence design to minimize the estimated AoD’s average mean squared error (AMSE), which however has no explicit expression. We firstly derive a closed-form upper bound for the AMSE and then simplify this upper bound into a tractable form, based on which a nonlinear optimization problem (NLP) is formulated. By solving this NLP optimally using its corresponding Karush-Kuhn-Tucker conditions, we obtain an efficient training beam sequence. The proposed MAP criterion and its associated training beam sequence design are further extended to multi-path scenarios, where a joint estimation of the multiple paths is firstly discussed, followed by a sequential estimation as a low-complexity alternative. Numerical results demonstrate the superiority of our proposed scheme over the existing benchmark methods, especially in the case when the receivers’ channels change rapidly.

In this paper, we focus on the physical layer security for a $K$ -user multiple-input-single-output (MISO) wiretap channel in the presence of a malicious eavesdropper, where we propose several interference exploitation (IE) precoding schemes for different types of the eavesdropper. Specifically, in the case where a common eavesdropper decodes the signal directly and Eve's full channel state information (CSI) is available at the transmitter, we show that the required transmit power can be further reduced by re-designing the `destructive region' of the constellations for symbol-level precoding and re-formulating the power minimization problem. We further study the SINR balancing problems with the derived `complete destructive region' with full, statistical and no Eve's CSI, respectively, and show that the SINR balancing problem becomes non-convex with statistical or no Eve's CSI. On the other hand, in the presence of a smart eavesdropper using maximal likelihood (ML) detection, the security cannot be guaranteed with all the existing approaches. To this end, we further propose a random jamming scheme (RJS) and a random precoding scheme (RPS), respectively. To solve the introduced convex/non-convex problems in an efficient manner, we propose an iterative algorithm for the convex ones based on the Karush-Kuhn-Tucker (KKT) conditions, and deal with the non-convex ones by resorting to Taylor expansions. Simulation results show that all proposed schemes outperform the existing works in secrecy performance, and that the proposed algorithm improves the computation efficiency significantly.

In this paper, we focus on 1-bit precoding for large-scale antenna systems in the downlink based on the concept of constructive interference (CI). By formulating the optimization problem that aims to maximize the CI effect subject to the 1-bit constraint on the transmit signals, we mathematically prove that, when relaxing the 1-bit constraint, the majority of the obtained transmit signals already satisfy the 1-bit constraint. Based on this important observation, we propose a 1-bit precoding method via a partial branch-and-bound (P-BB) approach, where the BB procedure is only performed for the entries that do not comply with the 1-bit constraint. The proposed P-BB enables the use of the BB framework in large-scale antenna scenarios, which was not applicable due to its prohibitive complexity. Numerical results demonstrate a near-optimal error rate performance for the proposed 1-bit precoding algorithm.

In this paper, we focus on the constructive interference (CI) precoding for the scenario when the number of streams simultaneously transmitted by the base station (BS) is larger than that of transmit antennas at the BS, and derive the optimal precoding structure by employing the pseudo inverse. We show that the optimal pre-scaling vector in IE precoding is equal to a linear combination of the right singular vectors that correspond to zero singular values of the coefficient matrix. By formulating the dual problem, we further show that the optimal precoding matrix can be expressed as a function of the dual variables in a closed form, and an equivalent quadratic programming (QP) formulation is derived for computational complexity reduction. Numerical results validate our analysis and demonstrate significant performance improvements for interference exploitation precoding in the considered scenario.

We consider computation-offloading for Internet-of-things (IoT) applications in multiple-input-multiple-output (MIMO) cloud-radio-access-network (C-RAN). Specifically, the computational tasks of the IoT devices (IoTDs) are offloaded to a MIMO C-RAN, where a MIMO radio resource head (RRH) is connected to a baseband unit (BBU) through a capacity-limited fronthaul link, facilitated by the spatial filtering and uniform scalar quantization. We formulate a computation-offloading optimization problem to minimize the total transmit power of the IoTDs while satisfying the latency requirement of the computational tasks. To obtain a feasible solution for the non-convex problem, firstly the spatial filtering matrix is locally optimized at the MIMO RRH. Subsequently, leveraging the alternating optimization framework for joint optimization on the residual variables at the BBU, the baseband combiner, the optimal resource allocation and the number of quantization bits are obtained through the minimum-mean-squared-error (MMSE) metric, the successive inner convexification method and the line-search method, respectively. As a low-complexity approach, we apply a supervised deep learning (DL) method, which learns from the solutions obtained with our proposed algorithm. In addition, the deep transfer learning is adopted to adjust the neural network in dynamic IoT systems. Numerical results validate the effectiveness of the proposed optimization algorithm and the learning based methods.

In this paper, we focus on 1-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI). For both PSK and QAM signaling, we firstly formulate the optimization problem that maximizes the CI effect subject to the requirement of the 1-bit transmit signals. We then mathematically prove that, when employing the CI formulation and relaxing the 1-bit constraint, the majority of the transmit signals already satisfy the 1-bit formulation. Building upon this important observation, we propose a 1-bit precoding approach that further improves the performance of the conventional 1-bit CI precoding via a partial branch-and-bound (P-BB) process, where the BB procedure is performed only for the entries that do not comply with the 1-bit requirement. This operation allows a significant complexity reduction compared to the fully-BB (F-BB) process, and enables the BB framework to be applicable to the complex massive MIMO scenarios. We further develop an alternative 1-bit scheme through an `Ordered Partial Sequential Update' (OPSU) process that allows an additional complexity reduction. Numerical results show that both proposed 1-bit precoding methods exhibit a significant signal-to-noise ratio (SNR) gain for the error rate performance, especially for higher-order modulations.

In this paper, we propose the hybrid-precoding design that alleviates the performance loss caused by beam-misalignment in the mmWave multi-user communication systems. To this end, we firstly design the beam-misalignment aware fully-digital precoders for two distinct scenarios. First, for a base-station (BS) with full estimated channel-state-information (CSI), the minimum-mean-squared-error metric incorporating the `error-statistics' of the beam-misalignment error is used to analytically derive a closed-form expression for the fully-digital precoder, which maximizes the array gain while suppressing the inter-user interference for each user-equipment (UE). Second, for a BS which can only acquire partial estimated CSI, a min-max non-convex optimization is considered to obtain the fully-digital precoder, which minimizes the maximum loss in the array gains of the expected beam-misalignment `error-range' over the UEs while cancelling the inter-user interference. Subsequently, we propose the hybrid-precoding design that approximates the fully-digital designs based on the gradient-projection method, which is mathematically proven to converge to an approximate local solution with further reduced complexity compared to the state-of-the-art algorithms. Finally, the proposed hybrid-precoding design is further extended to the wideband mmWave communication systems. Numerical results show that the proposed hybrid-precoding design can effectively alleviate the performance degradation incurred by the beam-misalignment.

In this paper, we investigate the interference exploitation precoding for multi-level modulations in the downlink multi-antenna systems. We mathematically derive the optimal precoding structures based on the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, by formulating the dual problem, the precoding problem for multi-level modulations can be transformed into a pre-scaling operation using quadratic programming (QP) optimization. Compared to the original second-order cone programming (SOCP) formulation, this transformation that finally leads to a QP optimization allows a considerable complexity reduction. Simulation results validate our derivations on the optimal precoding structure, and demonstrate significant performance improvements for interference exploitation precoding over traditional precoding methods for multi-level modulations.

In this paper, we study the cooperative beamforming schemes for multi-cell multi-user full-dimensional (FD) massive multiple-input multiple-out (MIMO) networks. In the considered network, base stations (BSs) work together to direct their beams to user equipments (UEs) such that inter-cell interference is minimized and each UE is assigned a beam from one BS by user association. We propose to maximize the network capacity by jointly optimizing the beamforming vectors and the user association factors (UAFs), which is further transformed into an optimization on UAFs only by maximizing a lower bound of the signal-to-leakage-and-noise ratio (SLNR). To solve the optimization on UAFs, we propose a belief propagation (BP) based algorithm to obtain the UAFs at the UE level in a parallel manner. Simulation results show that the proposed cooperative beamforming method significantly outperforms the benchmarks in the literature.

In this paper, we propose a fast beam tracking strategy for mobile millimeter-wave systems, where the temporal variations of the angle of departure (AoD) are considered and modeled as a discrete Markov process. In contrast to most existing works that rely on the slow-fading assumption, we consider a more practical scenario in which the AoD can vary rapidly due to blockage and other environmental obstructions. In this case, the use of narrow training beams becomes inefficient, and therefore we propose to employ multiple radio-frequency chains generating wide beams to reduce the training time. By optimizing the selected training beams, we aim to minimize the average tracking error probability (ATEP). However, since the exact expression for ATEP is difficult to obtain, we derive its upper bound in a closed form, and aim to minimize this upper bound instead. The associated training beam sequence design problem is transformed into the construction of a bipartite graph that does not contain cycles of length 4, which is implemented with the progressive edge-growth algorithm. Numerical results demonstrate significant gains of the proposed beam tracking strategy over the existing benchmark methods.

In this paper, we propose a codebook-based beam tracking strategy for mobile millimeter-wave (mmWave) systems, where the temporal variation of the angle of departure (AoD) is considered. A closed-form upper bound of the average tracking error probability (ATEP) is derived and further optimized. We first consider a slow-varying scenario where narrow training beams implemented by single radio-frequency (RF) chain are employed. We show that the ATEP can be reduced by optimizing the power allocation strategy over these training beams, which is formulated and transformed into a second-order cone programming. The fast-varying scenario is further considered where the use of narrow training beams becomes inefficient due to the rapid variations of AoD. In order to reduce the training time, multiple RF chains generating wide beams are employed to track the AoD's variations, and the associated beam pattern design problem is shown to be a 0 - 1 nonlinear optimization problem (NLP). A sequential quadratic programming method is used to solve this binary NLP. To reduce the complexity, a progressive edge-growth algorithm is further introduced by associating the binary NLP with a bipartite graph. Numerical results demonstrate significant gains of the proposed beam tracking strategy over existing benchmarks for both scenarios.

In this paper, we consider the downlink transmission for a dual-hop amplify-and-forward (AF) multiple-antenna relay systems, where we propose beamforming techniques for interference exploitation on a symbol level. Based on the constructive interference (CI), we firstly propose a joint source/relay precoding, where the precoding matrices at the source and the output signals at the relay are jointly optimized. To alleviate the high computational costs and circumvent the difficulty of practical implementation of the joint design, we further propose a low-complexity decoupled approach, where a closed-form linear precoding method is first employed at the source, and we then optimize the beamforming matrix at the relay for interference exploitation. It is revealed by numerical results that the proposed approaches that exploit the instantaneous interference can achieve an improved performance over the conventional case with a linear approach.

In this paper, we propose closed-form precoding schemes with optimal performance for constructive interference (CI) exploitation in the multiuser multiple-input single-output downlink, where the cases of both strict and non-strict phase rotation are considered. For optimization with strict phase rotation, we mathematically derive the optimal precoding structure with Lagrangian and Karush-Kuhn-Tucker conditions. By formulating its dual problem, the optimization problem is further shown to be equivalent to a quadratic programming over a simplex, which can be solved more efficiently. We then extend our analysis to the case of non-strict phase rotation, where it is mathematically shown that a K -dimensional optimization for non-strict phase rotation is equivalent to a 2K -dimensional optimization for strict phase rotation in terms of the problem formulation. The connection with the conventional zero-forcing precoding is also discussed. Based on the above-mentioned analysis, we further propose an iterative closed-form scheme to obtain the optimal precoding matrix, where within each iteration a closed-form solution can be obtained. Numerical results validate our analysis and the optimality of the proposed iterative closed-form algorithm, and further show that the proposed iterative closed-form scheme offers a flexible performance-complexity tradeoff by limiting the maximum number of iterations, which motivates the use of CI precoding in practical wireless systems.

We study multi-user massive multiple-input single-output systems and focus on downlink transmission for PSK modulation, where the base station employs a large antenna array with low-cost 1-bit digital-to-analog converters (DACs). The direct combination of existing beamforming schemes with 1-bit DACs is shown to lead to an error floor at medium-to-high SNR regime, due to the coarse quantization of the DACs with limited precision. In this paper, based on the constructive interference, we consider both a quantized linear beamforming scheme where we analytically obtain the optimal beamforming matrix and a non-linear mapping scheme where we directly design the transmit signal vector. Due to the 1-bit quantization, the formulated optimization for the non-linear mapping scheme is shown to be non-convex. The non-convex constraints of the 1-bit DACs are first relaxed into convex, followed by an element-wise normalization to satisfy the 1-bit DAC transmission. We further propose a low-complexity symbol scaling scheme that consists of three stages, in which the quantized transmit signal on each antenna element is selected sequentially. Numerical results show that the proposed symbol scaling scheme achieves a comparable performance to the optimization-based non-linear mapping approach, while the corresponding performance-complexity tradeoff is more favorable for the proposed symbol scaling method.

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts as radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

In this paper, we propose a novel approach for the spectrum sharing between Multi-Input-Multi-Output (MIMO) radar and downlink multi-user Multi-Input- Single-Output (MU-MISO) communication system. To obtain a power-efficient beamforming at the base station (BS), we utilize the constructive multi- user interference (MUI) as a source of green signal power. The proposed beamforming design mainly focuses on two optimization problems, i.e., transmit power minimization for BS and interference minimization for radar, subject to given performance requirements of the two systems. We further consider the impact of the proposed methods on radar, where the detection probability for MIMO radar in the presence of the interference from BS is analytically derived, and important trade-offs are revealed. Numerical results show that the proposed approach outperforms the conventional beamforming designs by achieving a significant performance gain under the discussed coexistence scenario.

In this paper, the mutual coupling effect among antenna elements for the downlink multiuser multiple-input-single-output (MU-MISO) is studied. Different from conventional knowledge that the mutual coupling effect usually degrades the system performance, a joint analog-digital precoding scheme is proposed so that the system can benefit from this effect. Linear precoding approaches are applied in the digital domain, while in the analog domain, convex optimization is applied to determine the value of each load impedance such that the resulting noise amplification factor for the precoder is minimized. Simulation results show that the proposed analog-digital precoding schemes can achieve a significant performance gain over conventional precoding approaches with fixed mutual coupling.

In this paper, we propose a joint analog-digital (A/D) beamforming scheme for the point-to-point (P2P) multiple-input-multiple-output (MIMO) systems, where we exploit the mutual coupling effect to further improve the system performance. By judiciously selecting the value of each load impedance for the antenna array, it will be shown that the mutual coupling effect can be beneficial. We firstly prove that the full elimination of mutual coupling is not achievable solely by changing the values of each load impedance. We further propose a joint A/D technique where the resulting interference aligns constructively to the useful signal vector with the concept of constructive interference. Numerical results show that the proposed schemes can achieve an improved performance compared to systems with fixed mutual coupling, especially when the antenna spacing is small.

In this paper, we focus on the millimeter-wave multi-user multiple-input-multiple-output (mmWave MU-MIMO) systems and propose a low-complexity hybrid precoding and combining design, which is applicable to both fully-connected structures and sub-connected structures. Based on the channel knowledge of each user, the analog combiner for each user is independently designed based on the singular value decomposition (SVD), while the analog precoder is obtained by the conjugate transposition to maximize the effective channel gain. Then, with the resulting effective analog channel, low-dimensional baseband precoders can be efficiently applied. The proposed scheme requires no optimization techniques or any complicated iterative algorithms, while the numerical results show that it can approach the performance of fully digital schemes and even achieve a better performance in some scenarios. It is also observed that sub-connected structures can achieve a much higher power efficiency compared to fully-connected structures and are therefore promising for the future green communication systems.

Compact parasitic arrays in the form of electronically steerable parasitic antenna radiators (ESPARs) have emerged as a new antenna structure that achieves multiple-input-multiple-output (MIMO) transmission with a single RF chain. In this paper, we study the application of precoding on practical ESPARs, where the antennas are equipped with load impedances of quantized values. We analytically study the impact of the quantization on the system performance, where it is shown that while ideal ESPARs with ideal loads can achieve a similar performance to conventional MIMO, the performance of ESPARs will be degraded when only loads with quantized values are available. We further extend the performance analysis to imperfect channel state information. In order to alleviate the performance loss, we propose to approximate the ideal current vector by optimization, where a closed-form solution is further obtained. This enables the use of ESPARs in practice with quantized loads. Simulation results validate our analysis and show that a significant performance gain can be achieved with the proposed scheme over ESPARs with quantized loads. Finally, the tradeoff between performance and power consumption is shown to be favorable for the proposed ESPAR approaches compared with conventional MIMO, as evidenced by our energy efficiency results.

In this paper, we propose a joint analog-digital (A/D) beamforming scheme for the point-to-point multiple-input-multiple-output system, where we exploit mutual coupling by optimizing the load impedances of the transmit antennas. Contrary to the common conception that mutual coupling strictly harms the system performance, we show that mutual coupling can be beneficial by exploiting the concept of constructive interference. By changing the value of each load impedance for the antenna array based on convex optimization, the mutual coupling effect can be manipulated so that the resulting interference aligns constructively to the useful signal vector. We first prove that the full elimination of mutual coupling effect is not achievable solely by tuning the values of the antenna load impedances. We then introduce the proposed A/D scheme for both PSK and QAM modulations, where performance gains with respect to conventional techniques are obtained. The implementation of the proposed schemes is also discussed, where a lookup table can be built to efficiently apply the calculated load impedances. The numerical results show that the proposed schemes can achieve an improved performance compared to systems with fixed mutual coupling, especially when the antenna spacing is small.

Existing MIMO precoding techniques assume conventional antenna arrays with multiple radio-frequency (RF) chains each connected to a different antenna. Towards small portable devices and base stations, single-fed compact arrays, also known as electronically steerable parasitic antenna radiators (ESPAR) have recently emerged as a new antenna structure that requires only a single RF chain. In this paper, we study the ESPAR based antenna arrays and explore linear precoding schemes for ESPAR antennas. The closed-form expression for the computation of the tunable loads and the feeding voltage is firstly shown and the impact of impedance errors and imperfect CSI on the performance is also investigated analytically. It will be shown that the impedance errors will act as an additional noise source that is independent of the SNR and thus result in an error floor at high SNR. We further study the energy efficiency of both conventional MIMO and ESPAR-based MIMO systems. Simulation results validate our analysis and show that ESPAR without impedance errors can achieve a similar performance to conventional antenna arrays and a higher energy efficiency, while the performance degradation due to impedance errors motivates the design of robust precoding schemes.

Femtocells have attracted more and more attention in academia, industry and standardization forums. Besides, energy efficiency has been widely discussed in recent years. However, most of the existing works focus on the energy efficiency of macro base stations, the energy efficiency of femto base stations is neglected. In this paper, we study the maximization of energy efficiency of downlink OFDMA macro-femto networks. Both the transmit power constraint of femto base stations and the SINR thresholds of femto users and macro users are considered. We model the subchannel and power allocation problem as a non-cooperative game and a price function is introduced. To decrease the computational complexity, joint subchannel and power allocation are decomposed into two steps and a distributed resource allocation scheme is proposed to resolve the resource allocation problem. Simulation results show that the proposed algorithm has better performance in terms of energy efficiency compared with equal power allocation and an energy-efficient power control algorithm.

Femtocell technology, which aims at improving indoor signal coverage and offloading traffic from macro base stations (MBSs), has attracted interest in wireless industry. Among all the access methods, hybrid access proves to be the most promising one. However, it is difficult to promote the hybrid access mode since all the femto holders (FHs) merely care about their own benefits. In this paper, we propose a price discount strategy for wireless service provider (WSP) to promote the hybrid access mode of femtocell in which WSP provides a price discount in exchange for the femto holders to share part of their resource to macro users. The problem is formulated and analyzed as a Stackelberg game where WSP acts as the leader and femto holders as the followers. The optimal resource allocation ratio for each FH is decided independently and the optimal price discount factor for WSP is also obtained. Furthermore, we analyze how the bandwidth allocation strategy will affect the utility of WSP and an optimal bandwidth allocation ratio is decided. Numerical results show that both WSP and femto holders can benefit from the price discount strategy.