Sixth-generation (6G) wireless networks have been driving growing demands for the full utilization of spectral efficiency and spatial degrees of freedom (DoFs). This paper investigates a non-orthogonal multiple access (NOMA) empowered integrated sensing and communication (ISAC) system assisted by movable antennas (MAs). We consider a dual functional radar and communication (DFRC) base station (BS) equipped with a two-dimensional (2D) MA array, which simultaneously senses multiple targets and serves users divided into multiple clusters. Successive interference cancellation (SIC) is employed within each cluster to suppress intra-cluster interference. To enhance the total illumination power at the sensing targets while guaranteeing the communication signal-to-interference-plus-noise-ratio (SINR) requirements at the users, we formulate an optimization problem for joint power allocation, beamforming, and antenna position design. To address this highly coupled and non-convex problem, an alternating optimization-based algorithm is proposed. We first determine the SIC decoding order by the equivalent-channel-to-interference-plus-noise-ratios (ECINRs), and derive the close-form solutions of the optimal intra-and-inter cluster power allocation coefficients. The sub-problems of beamforming and antenna position design are solved by semidefinite relaxation (SDR) and successive convex approximation (SCA) based schemes, respectively. Numerical simulation results are provided to verify the effectiveness of the proposed algorithm. The proposed algorithm significantly outperforms baseline schemes, which achieves approximately 2 dB illumination power gain compared to the conventional fixed position antennas (FPA), demonstrating the promising potential of MAs in wireless networks.

This paper investigates a movable antenna (MA)-aided integrated sensing and communication (ISAC) system using non-orthogonal multiple access (NOMA). A base station (BS) configured with a two-dimensional MA array simultaneously serves multiple communication users and sensing multiple targets. To enhance system capacity, superimposed symbols are transmitted to communication users, with successive interference cancellation (SIC) employed for signal decoding. Our objective is to maximize the total illumination power at the targets while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements for communication users. To achieve this goal, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the transmit power allocation, beamforming, sensing covariance matrix, and antenna positions. Numerical results show that the MA system achieves significant improvement in illumination power compared to fixed-position antennas (FPAs), with particularly significant gains under high SINR requirements.

As wireless communication advances toward the 6G era, the demand for ultra-reliable, high-speed, and ubiquitous connectivity is driving the exploration of new degrees-of-freedom (DoFs) in communication systems. Among the key enabling technologies, Movable Antennas (MAs) integrated into Flexible Cylindrical Arrays (FCLA) have shown great potential in optimizing wireless communication by providing spatial flexibility. This paper proposes an innovative optimization framework that leverages the dynamic mobility of FCLAs to improve communication rates and overall system performance. By employing Fractional Programming (FP) for alternating optimization of beamforming and antenna positions, the system enhances throughput and resource utilization. Additionally, a novel Constrained Grid Search-Based Adaptive Moment Estimation Algorithm (CGS-Adam) is introduced to optimize antenna positions while adhering to antenna spacing constraints. Extensive simulations validate that the proposed system, utilizing movable antennas, significantly outperforms traditional fixed antenna optimization, achieving up to a 31% performance gain in general scenarios. The integration of FCLAs in wireless networks represents a promising solution for future 6G systems, offering improved coverage, energy efficiency, and flexibility.

Effective elements selection (ES) is essential to the deployment of reconfigurable intelligent surface (RIS), which, however, receives little attention. In this letter, we propose two novel ES strategies intended for deployment in RIS-assisted multiuser multiple-input-single-output (MISO) wireless networks. The first scheme is designed to maximize the effective gains of channels, while the second ES scheme presents a linear swapping selection (LSS) method that focuses on optimizing the total achievable rate. Numerical results show that the second scheme using the LSS method performs better than the first one, and is able to achieve a near-optimal performance but with significantly reduced computational complexity compared with the optimal exhaustive search scheme.

This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization.

Movable antenna (MA) is a promising technology that can enhance communication performance by properly adjusting the antenna position within a local region at transceivers. To further explore the potential of an antenna array, this article proposes a new rotatable and movable antenna (RMA) architecture where the antenna array at a base station (BS) not only employs multiple MAs but also is capable of being rotated along its yaw, pitch, and roll angles. In this context, we first characterize the wireless channel with respect to different rotation angles and antenna positions and formulate an optimization problem to maximize the downlink sum rate under practical system constraints. Subsequently, we solve the non-convex problem for single-user and multi-user scenarios, respectively. In particular, for the single-user case, we optimize the rotation angles and antenna positions to maximize the user’s rate and propose a gradient ascent (GA) algorithm based on the alternating optimization (AO) framework. For the multi-user scenario with the purpose of maximizing the sum rate of all users, we make the original problem more tractable by exploiting the Lagrangian dual transform and fractional programming (FP) techniques. On this basis, a GA-based algorithm is also proposed to jointly optimize the rotation angles and MAs’ positions together with the precoding matrix at the BS in an iterative manner. Finally, numerical results show that the RMA architecture can improve the sum rate by using the proposed algorithm to adjust rotation angles and antenna positions, compared to the element-level MA, rotatable antenna, and fixed-position antenna. Moreover, the proposed optimization algorithm outperforms its counterparts in achieving a trade-off between performance and computational complexity.

In this paper, we investigate a novel integrated sensing and communication (ISAC) system aided by movable antennas (MAs). A bistatic radar system, in which the base station (BS) is configured with MAs, is integrated into a multi-user multiple-input-single-output (MU-MISO) system. Flexible beamforming is studied by jointly optimizing the antenna coefficients and the antenna positions. Compared to conventional fixed-position antennas (FPAs), MAs provide a new degree of freedom (DoF) in beamforming to reconfigure the field response, and further improve the received signal quality for both wireless communication and sensing. We propose a communication rate and sensing mutual information (MI) maximization problem by flexible beamforming optimization. The complex fractional objective function with logarithms are first transformed with the fractional programming (FP) framework. Then, we propose an efficient algorithm to address the non-convex problem with coupled variables by alternatively solving four sub-problems. We derive the closed-form expression to update the antenna coefficients by Karush-Kuhn-Tucker (KKT) conditions. To improve the direct gradient ascent (DGA) scheme in updating the positions of the antennas, a 3-stage search-based projected GA (SPGA) method is proposed. Simulation results show that MAs significantly enhance the overall performance of the ISAC system, achieving 59.8% performance gain compared to conventional ISAC system enabled by FPAs. Meanwhile, the proposed SPGA-based method has remarkable performance improvement compared the DGA method in antenna position optimization.

Flexible antenna arrays (FAAs), distinguished by their rotatable, bendable, and foldable properties, are extensively employed in flexible radio systems to achieve customized radiation patterns. This paper aims to illustrate that FAAs, capable of dynamically adjusting surface shapes, can enhance communication performances with both omni-directional and directional antenna patterns, in terms of multi-path channel power and channel angle Cramér-Rao bounds. To this end, we develop a mathematical model that elucidates the impacts of the variations in antenna positions and orientations as the array transitions from a flat to a rotated, bent, and folded state, all contingent on the flexible degree-of-freedom. Moreover, since the array shape adjustment operates across the entire beamspace, especially with directional patterns, we discuss the sum-rate in the multi-sector base station that covers the 360° communication area. Particularly, to thoroughly explore the multi-sector sum-rate, we propose separate flexible precoding (SFP), joint flexible precoding (JFP), and semi-joint flexible precoding (SJFP), respectively. In our numerical analysis comparing the optimized FAA to the fixed uniform planar array, we find that the bendable FAA achieves a remarkable 156% sum-rate improvement compared to the fixed planar array in the case of JFP with the directional pattern. Furthermore, the rotatable FAA exhibits notably superior performance in SFP and SJFP cases with omni-directional patterns, with respective 35% and 281%.

As extremely large-scale (XL) arrays advance, near-field (NF) communications have gained significant attention.With this shift, traditional far-field techniques are being revised for compatibility with new XL NF communication paradigms. This work presents NF hybrid beamforming (NF-HBF) approaches for XL-MIMO, focusing on challenges like near-field effects and spatial non-stationarity. First, it redefines the sparse recovery-based NF-HBF problem, shifting from angular- to polar-domain code-books, leading to direct greedy hybrid beamforming (DG-HBF). However, challenges such as high computational complexity, phase shifter (PS) resolution, and spatial non-stationarities persist. To overcome these, this study proposes stepwise-individual and stepwise-joint greedy HBF methods, namely SIG-HBF and SJG-HBF. These methods simplify the process by approximating spherical-wave beams with planar-wave beams, promising lower PS resolution needs, reduced complexity, and the ability to tackle spatially non-stationary channels. Moreover, by exploring conjugate symmetric sequency-ordered Hadamard transforms, NF-HBF can be efficiently achieved using 2-bit PSs with values in {1,−1,j,−j}, facilitated by the SJG-HBF and SJG-HBF methods. Numerical simulations on the proposed methods demonstrate that DG-HBF can approach NF fully-digital beamforming, while SIG-HBF and SJG-HBF highlight the feasibility of utilizing angular-domain codebooks with low PS cost and low memory storage for NF-HBF.

In this paper, reconfigurable intelligent surface (RIS) is employed in a millimeter wave (mmWave) integrated sensing and communications (ISAC) system. To alleviate the multi-hop attenuation, the semi-self sensing RIS approach is adopted, wherein sensors are configured at the RIS to receive the radar echo signal. Focusing on the estimation accuracy, the Cramér-Rao bound (CRB) for estimating the direction-of-the-angles is derived as the metric for sensing performance. A joint optimization problem on hybrid beamforming and RIS phase shifts is proposed to minimize the CRB, while maintaining satisfactory communication performance evaluated by the achievable data rate. The CRB minimization problem is first transformed as a more tractable form based on Fisher information matrix (FIM). To solve the complex non-convex problem, a double layer loop algorithm is proposed based on penalty concave-convex procedure (penalty-CCCP) and block coordinate descent (BCD) method with two sub-problems. Successive convex approximation (SCA) algorithm and second order cone (SOC) constraints are employed to tackle the non-convexity in the hybrid beamforming optimization. To optimize the unit modulus constrained analog beamforming and phase shifts, manifold optimization (MO) is adopted. Finally, the numerical results verify the effectiveness of the proposed CRB minimization algorithm, and show the performance improvement compared with other baselines. Additionally, the proposed hybrid beamforming algorithm can achieve approximately 96% of the sensing performance exhibited by the full digital approach within only a limited number of radio frequency (RF) chains.

Near-field communications present new opportunities over near-field channels, however, the spherical wavefront propagation makes near-field signal processing challenging. In this context, this paper proposes efficient near-field channel estimation methods for wideband MIMO mmWave systems with the aid of extremely large-scale reconfigurable intelligent surfaces (XL-RIS). For the wideband signals reflected by the analog RIS, we characterize their near-field beam squint effect in both angle and distance domains. Based on the mathematical analysis of the near-field beam patterns over all frequencies, a wideband spherical-domain dictionary is constructed by minimizing the coherence of two arbitrary beams. In light of this, we formulate a two-dimensional compressive sensing problem to recover the channel parameter based on the spherical-domain sparsity of mmWave channels. To this end, we present a correlation coefficient-based atom matching method within our proposed multi-frequency parallelizable subspace recovery framework for efficient solutions. Additionally, we propose a two-dimensional oracle estimator as a benchmark and derive its lower bound across all subcarriers. Our findings emphasize the significance of system hyperparameters and the sensing matrix of each subcarrier in determining the accuracy of the estimation. Finally, numerical results show that our proposed method achieves considerable performance compared with the lower bound and has a time complexity linear to the number of RIS elements.

Binary array codes are widely used in storage systems to prevent data loss, such as the Redundant Array of Independent Disks (RAID). Most designs for such codes, such as Blaum-Roth (BR) codes and Independent-Parity (IP) codes, are carried out on the polynomial ring F2[x]/⟨ Σp-1i=0xi⟩, where F2is a binary field, andpis a prime number. In this paper, we consider the polynomial ring F2[x]/⟨ Σp-1i=0xiτ⟩, where p > 1 is an odd number and τ ≥ 1 is any power of two, and explore variant codes from codes over this polynomial ring. Particularly, the variant codes are derived by mapping parity-check matrices over the polynomial ring to binary parity-check matrices. Specifically, we first propose two classes of variant codes, termed V-ETBR and V-ESIP codes. To make these variant codes binary maximum distance separable (MDS) array codes that achieve optimal storage efficiency, this paper then derives the connections between them and their counterparts over polynomial rings. These connections are general, making it easy to construct variant MDS array codes from various forms of matrices over polynomial rings. Subsequently, some instances are explicitly constructed based on Cauchy and Vandermonde matrices. In the proposed constructions, both V-ETBR and V-ESIP MDS array codes can have any number of parity columns and have the total number of data columns of exponential order with respect top. In contrast, previous binary MDS array codes only have a total number of data columns of linear order with respect top. This makes the codes proposed in this paper more suitable for application to large-scale storage systems. In terms of computation, two fast syndrome computations are proposed for the Vandermonde-based V-ETBR and V-ESIP MDS array codes, both meeting the lowest known asymptotic complexity among MDS codes. Due to the fact that all variant codes are constructed from parity-check matrices over simple binary fields instead of polynomial rings, they are attractive in practice.

This paper investigates the potential of near-field localization using widely-spaced multi-subarrays (WSMSs) and analyzing the corresponding angle and range Cramér-Rao bounds (CRBs). By employing the Riemann sum, closed-form CRB expressions are derived for the spherical wavefront-based WSMS (SW-WSMS). We find that the CRBs can be characterized by the angular span formed by the line connecting the array’s two ends to the target, and the different WSMSs with same angular spans but different number of subarrays have identical normalized CRBs. We provide a theoretical proof that, in certain scenarios, the CRB of WSMSs is smaller than that of uniform arrays. We further yield the closed-form CRBs for the hybrid spherical and planar wavefront-based WSMS (HSPW-WSMS), and its components can be seen as decompositions of the parameters from the CRBs for the SW-WSMS. Simulations are conducted to validate the accuracy of the derived closed-form CRBs and provide further insights into various system characteristics. Basically, this paper underscores the high resolution of utilizing WSMS for localization, reinforces the validity of adopting the HSPW assumption, and, considering its applications in communications, indicates a promising outlook for integrated sensing and communications based on HSPW-WSMSs.

Millimeter wave (mmWave) full-duplex (FD) is a promising technique for improving capacity by maximizing the utilization of both time and the rich mmWave frequency resources. Still, it has restrictions due to FD self-interference (SI) and mmWave’s limited coverage. Therefore, this study dives into FD mmWave MIMO with the assistance of reconfigurable intelligent surfaces (RIS) for capacity improvement. First, we demonstrate the angular-domain reciprocity of FD antenna arrays under the far-field planar wavefront assumption. Accordingly, a strategy for joint downlink-uplink (DL-UL) channel estimation is presented. For estimating the SI channel, the direct channel, and the cascaded channel, the Khatri-Rao product-based compressive sensing (KR-CS), distributed CS (D-CS), and two-stage multiple measurement vector-based D-CS (M-D-CS) frameworks are proposed, respectively. Additionally, we propose a passive beamforming optimization solution based on the angular-domain cascaded channel. With hybrid beamforming architectures, a novel hybrid weighted minimum mean squared error method for SI cancellation (H-WMMSE-SIC) is proposed. Simulations have revealed that joint DL-UL processing significantly improves estimation performance in comparison to separate DL/UL channel estimation. Particularly, when the interference-to-noise ratio is less than 35 dB, our proposed H-WMMSE-SIC offers spectral efficiency performance comparable to fully-digital WMMSE-SIC. Finally, the computational complexity is analyzed for our proposed methods.

In our early work, we proposed a class of joint source channel anytime coding (JSCAC) scheme based on spatially coupled repeat-accumulate (SC-RA) codes, in addition to an improved partial joint expanding window decoding (PJEWD) algorithm. In this paper, we extend our preliminary results and make a comprehensive theoretical analysis for this system. First, we propose an improved density evolution (DE) algorithm for the SC-RA based JSCAC with PJEWD scheme. Based on the proposed DE algorithm, we systematically analyze the anytime property, the belief propagation (BP) decoding threshold, and the decoding complexity of this system over binary additive white Gaussian noise channel (BIAWGN), as well as the effects of code parameters on performances of the above three aspects. Moreover, we explore some application scenarios for JSCAC schemes, where it could maximize its advantages over the prior-art JSCC schemes. Both numerical and simulation results demonstrate the potentials of the proposed JSCAC scheme for high reliability and low latency communications. The proposed DE algorithm could also provide a good reference for the analysis of other JSCAC systems.

This paper concentrates on cell-free massive multiple-input and multiple-output (MIMO) network with variable-resolution analog-to-digital converters (ADCs). In such an architecture, all ADCs equipping at any access point (AP) can use arbitrary bit resolution to realize adaptive quantization and reduce power consumption. Under this circumstance, we first introduce a quantization-aware channel estimator based on linear minimum mean-square error (LMMSE) theory. On this basis, intra-AP and inter-AP bit allocation problems are investigated to maximize channel estimation quality subject to the total number of quantization bits. By leveraging the statistical characteristics of the estimated channels and estimation errors, we then derive the theoretical expressions of the achievable uplink spectral efficiency (SE) for maximal ratio combining (MRC) and minimum mean-square error (MMSE) combining, respectively. Furthermore, to maximize the sum SE under the constraint of total ADC quantization bits, we also investigate intra-AP and inter-AP bit allocation problems for both single-user and multi-user scenarios. Finally, simulation results confirm that our theoretical analyses are correct and accurate. In addition, we resort to numerical results to achieve some new insights and verify the advantages and conclusions pertinent to the proposed bit allocation techniques.

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels’ sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead. Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework.

Mobile crowdsensing (MCS) is a promising paradigm where sensor-embedded mobile devices are exploited for collecting and sharing environmental data. In MCS, the participating mobile devices sense the environment, collect the data, (pre-)process the data and transmit the data or pre-processing results to the server for further processing. In wireless edge networks, transmission and/or processing of sensed data may be unsuccessful due to the unstable wireless channels, limited bandwidth, energy and computation resources. To optimize the MCS performance, it is imperative to jointly consider the data sensing, processing and transmission for MCS system design. In this paper, we propose a joint sensing, communication and computation (JSCC) framework for multi-dimensional resource constrained MCS systems. We formulate the JSCC design as an optimization problem, by jointly controlling the data sensing, transmission and computation offloading schemes in the system. Simulation results show that the proposed JSCC framework significantly outperforms several baseline solutions without jointly considering data sensing-transmission-computation and/or multi-dimensional resource limitations.

Due to the very narrow beam used in millimeter wave communication (mmWave), beam alignment (BA) is a critical issue. In this work, we investigate the issue of mmWave BA and present a novel beam alignment scheme on the basis of a machine learning strategy, Bayesian optimization (BO). In this context, we consider the beam alignment issue to be a black box function and then use BO to find the possible optimal beam pair. During the BA procedure, this strategy exploits information from the measured beam pairs to predict the best beam pair. In addition, we suggest a novel BO algorithm based on the gradient boosting regression tree model. The simulation results demonstrate the spectral efficiency performance of our proposed schemes for BA using three different surrogate models. They also demonstrate that the proposed schemes can achieve spectral efficiency with a small overhead when compared to the orthogonal match pursuit (OMP) algorithm and the Thompson sampling-based multi-armed bandit (TS-MAB) method.

In this paper, we focus on the sum-rate optimization in a multi-user millimeter-wave (mmWave) system with distributed intelligent reflecting surfaces (D-IRSs), where a base station (BS) communicates with users via multiple IRSs. The BS transmit beamforming, IRS switch vector, and phase shifts of the IRS are jointly optimized to maximize the sum-rate under minimum user rate, unit-modulus, and transmit power constraints. To solve the resulting non-convex optimization problem, we develop an efficient alternating optimization (AO) algorithm. Specifically, the non-convex problem is converted into three subproblems, which are solved alternatively. The solution to transmit beamforming at the BS and the phase shifts at the IRS are derived by using the successive convex approximation (SCA)-based algorithm, and a greedy algorithm is proposed to design the IRS switch vector. The complexity of the proposed AO algorithm is analyzed theoretically. Numerical results show that the D-IRSs-aided scheme can significantly improve the sum-rate and energy efficiency performance.

In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate.

This paper is concerned with the secrecy rate based optimization problems in a mixed radio frequency (RF) / free space optical (FSO) spectrum sharing network, wherein multiple single-antenna secondary users (SUs) are connected to a decode and forward (DF) single-antenna relay through RF links and the relay is connected with the secondary destination through an FSO link. The RF and FSO channels are assumed to follow Rayleigh and Gamma-Gamma distributions with the effect of pointing errors, respectively. The three schemes, i.e., direct transmission, single-user cooperative jamming and multi-user cooperative beamforming jamming, are proposed to improve the physical-layer information security in the presence of a single-antenna eavesdropper. The mutual interference between the primary and secondary networks is considered. The problems are to jointly optimize the SU transmit power and jamming power for the best secrecy performance under both the RF and FSO dominant cases. Such problems are nonconvex. The Dinkelbach approach is adopted to solve the optimization in the direct transmission and single-user cooperative jamming schemes and a two-level optimization approach with semi-definite relaxation (SDR) is applied to obtain the solution for the multi-user cooperative beamforming scheme. Moreover, the SDR optimality is proved by Karush-Kuhn-Tucker conditions analysis. Simulation results are provided, and the results show that the proposed multi-user cooperative beamforming jamming can attain the higher achievable secrecy rate than the zero-forcing beamforming scheme and other two suboptimal designs.

Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study how to exploit LBT strategies for frame based equipment (FBE) in unlicensed spectrum. We consider two optimization problems: Throughput optimal stopping and nominal throughput optimal stopping. We discover that the throughput optimal transmission strategy for FBE in unlicensed spectrum is to transmit whenever the channel is clear. In contrast, we find that the nominal throughput optimal transmission strategy is less aggressive: The FBE does not transmit until it finds that the channel is clear and the channel quality exceeds an optimized threshold.

Prior results on performance analysis for cognitive relay networks mainly involve perfect channel state information (CSI), which is not readily available. Thus, the effects of outdated CSI on the performance of a multi-antenna multi-relay cognitive radio system are investigated in this work. To exploit the benefits of multiple antennas, collaborative zero-forcing beamforming at the secondary source is proposed to enhance the system performance under the outdated CSI. At the destination, the maximum ratio combining (MRC) diversity scheme is adopted. The Nth best relay selection strategy is applied before the secondary data transmission process. Closed-form expressions for the exact and asymptotic outage probabilities are derived for the secondary user over the Rayleigh fading in the first hop and Nakagami-m fading in the second hop with and without feedback delay. These analytical results can reveal the system diversity order and coding gain. Monte Carlo simulations are carried out to verify the correctness of our analysis. These new analytical results can reveal insights into the characteristics of the proposed system over the outdated fading channels, and can provide useful design criteria for relay-assisted spectrum sharing networks.

The effort to extend cellular technologies to unlicensed spectrum has been gaining high momentum. Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study throughput optimal LBT transmission strategy for load based equipment (LBE). We find that the optimal rule is a pure threshold policy: The LBE should stop listening and transmit once the channel quality exceeds an optimized threshold. We also reveal the optimal set of LBT parameters that are compliant with regulatory requirements. Our results shed light on how the regulatory LBT requirements can affect the transmission strategies of radio equipment in unlicensed spectrum.

Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We investigate maxmin fairness energy-harvesting optimization problem in such a system by taking into account of the individual link fairness. In particular, we maximize the energy harvested by the worstcase link when the problem is subject to the rate requirements, transmit power constraint, interference power constraint and subchannels assignment constraint. Due to the nonconvexity of the formulated problem, we relax the integer variable and introduce an auxillery variable. The Lagrangian and subgradient methods are adopted to obtain a suboptimal solution. Simulation results are presented to verify the fairness performance of the proposed algorithm, and to reveal a new tradeoff between the network harvested energy and link fairness.

A performance-acceptable and low-complexity detection method for massive multiple-input multiple-output (MIMO) involving low-resolution analog digital converter (ADC) at each antenna is proposed. The proposed method combines the generalized approximate message passing (GAMP) detection with channel decoder and exchanges extrinsic information between them, by which the remaining information filtered by the ADCs can be recovered as accurate as possible. Contrasted to the iterative minimum mean squared error (MMSE) detection, our method circumvents large-scale matrix inverse operation and leverages the statistical properties of both quantization errors and transmitted symbols. Moreover, we analyze the computational complexity and storage occupation for both algorithms to authenticate the superiority of the proposed approach. For visualization, the numerical results reveal that 3-bit ADCs are capable of achieving the almost same performance as the full resolution ADCs and substantiate that the bit error ratio (BER) performance of the proposed method is equivalent to that of iterative MMSE but with less complexity for implementation.

Interference alignment (IA) is a data transmission scheme that achieves maximum multiplexing gains in interference channel. This study investigates the theoretical sum rate for IA. By analysing both the asymptotic eigenvalues distribution and the magnitude of the effective channel after interference cancellation in IA, a closed‐form of sum rate expression is derived for Gaussian interference channel. Numerical results show that the closed‐form results nearly coincide with that derived from numerical results of IA.

The timing synchronization reciprocity error(TSRE) is modeled and investigated in this paper, which can be found in an OFDM/TDD coordinated multi-point transmission(CoMP) system. In order to avoid its adverse impact on the radio frequency(RF) calibration, we improve the traditional calibration algorithm. Moreover, we propose two robust precoding schemes to alleviate the performance degradation of the multi-user CoMP system caused by the TSRE. The design criteria of these precoding schemes are based on compensating the linear phase rotation at every subcarrier and maximizing the lower bound of the average signal-to-leakage-plus-noise ratio(SLNR). Simulation results demonstrate that the proposed robust precoding schemes can achieve higher system throughput than the conventional SLNR precoding scheme under the non-ideal reciprocity situation.

We consider the joint exploitation of coding and diversity techniques to achieve efficient, reliable wireless transmission. The system of interest comprises a powerful non-binary low-density parity-check (LDPC) code that will be soft-decoded to supply strong error protection, a quadratic amplitude modulator (QAM) that directly takes in the non-binary LDPC symbols and will be soft-demodulated, and a modulation diversity (or signal space diversity, SSD) operator that will provide power- and bandwidth-efficient diversity gain. By relaxing the rate of the SSD rotation matrices to below 1, we show that a better rate allocation can be arranged between the LDPC codes and the SSD, which brings significant performance gain over previous systems. To facilitate the design and evaluation of the relaxed SSD rotation matrices, three types of efficient constructions are demonstrated and a set of four criteria are developed. Through analysis and simulations, we show that our strategies are very effective in combating random fading and strong noise on Rayleigh fading channels.

A novel trellis coded modulation scheme for a multicarrier DS-CDMA system is studied in this paper. Based on the concept of transmission diversity in the time-frequency domain, we propose a code design criterion that includes diversity gain and coding gain. In terms of the proposed criterion, we can search for codes that have larger coding gain while keeping the maximum diversity. Results show that the bit error rate performance of the system with the novel trellis coded modulation is much better than that of the traditional system while not occupying additional spectral bandwidth.