Multiuser Wireless Communication Systems Research Articles

Improved Artificial Rabbits Algorithm for Positioning Optimization and Energy Control in RIS Multiuser Wireless Communication Systems

Improved Artificial Rabbits Algorithm for Positioning Optimization and Energy Control in RIS Multiuser Wireless Communication Systems

Placement Optimization and Power Management in a Multiuser Wireless Communication System With Reconfigurable Intelligent Surfaces

Placement Optimization and Power Management in a Multiuser Wireless Communication System With Reconfigurable Intelligent Surfaces

Opportunistic Scheduling in IRS-Aided UAV Multi-User Wireless Communication Systems: Modeling and Analysis

Opportunistic Scheduling in IRS-Aided UAV Multi-User Wireless Communication Systems: Modeling and Analysis

Tri-Polarized Holographic MIMO Surfaces for Near-Field Communications: Channel Modeling and Precoding Design

This paper investigates the utilization of triple polarization (TP) for multi-user (MU) wireless communication systems with holographic multiple-input multi-output surfaces (HMIMOSs), targeting capacity boosting and diversity exploitation without enlarging the antenna array sizes of the transceivers. We specifically consider that both the transmitter and receiver are equipped with an HMIMOS consisting of compact sub-wavelength TP patch antennas and operating in the near-field (NF) regime. To characterize TP MU-HMIMOS systems, a TP NF channel model is constructed using the dyadic Green’s function, whose characteristics are leveraged to design two precoding schemes for mitigating the cross-polarization and inter-user interference contributions. Specifically, a user-cluster-based precoding scheme that assigns different users to one of three polarizations, at the expense of system’s diversity, is presented together with a two-layer precoding technique that removes interference using a Gaussian elimination method. A theoretical correlation analysis for HMIMOS-based systems operating in the NF region is also derived, revealing that both the spacing of transmit patch antennas and user distance impact transmit correlation factors. Our numerical results showcase that the users located far from the transmit HMIMOS experience higher correlation than those closer in the NF region, resulting in a lower channel capacity. In terms of channel capacity, it is demonstrated that the proposed TP HMIMOS-based systems almost achieve 1.25 and 3 times larger gain compared to their dual-polarized version and conventional HMIMOS systems, respectively. It is also shown that the the proposed two-layer precoding scheme combined with two-layer power allocation realizes the highest spectral efficiency, among compared schemes, without sacrificing diversity.

Cross-Layer Optimization: Joint User Scheduling and Beamforming Design With QoS Support in Joint Transmission Networks

User scheduling and beamforming design are two crucial yet coupled topics for multiuser wireless communication systems. They are usually addressed separately with conventional optimization methods. In this paper, cross-layer optimization problem is considered, namely, the user scheduling and beamforming are jointly discussed, subjecting to the requirement of per-user quality of service and the maximum allowable transmit power for multicell multiuser joint transmission networks. To achieve the goal, a mixed discrete-continuous variables combinational optimization problem is investigated with aiming at maximizing the sum rate of the communication system. To circumvent the original non-convex problem with dynamic solution space, we first transform it into a 0-1 integer and continuous variables optimization problem, and then obtain a tractable form with continuous variables by exploiting the characteristics of the 0-1 integer constraints. Finally, the scheduled users and the optimized beamforming vectors are simultaneously calculated by an alternating optimization algorithm. We also theoretically prove that the base stations allocate zero power to the unscheduled users. Furthermore, two heuristic optimization algorithms are proposed respectively based on brute-force search and greedy search. Numerical results validate the effectiveness of our proposed methods, and the optimization approach gets relatively balanced results compared with the other two approaches.

Low-Complexity Beamforming Optimization for IRS-Aided MU-MIMO Wireless Systems

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication.

Capacity Enhancement of Multiuser Wireless Communication System through Adaptive Non-Linear Pre coding

Multiuser multiple-input multiple-output (MIMO) nonlinear pre coding techniques face the issue of poor computational scalability of the size of the network. But by this nonlinear pre coding technique the interference is pre-cancelled automatically and also provides better capacity. So in order to reduce the computational burden in this paper, a definitive issue of MU-MIMO scalability is tackled through a non-linear adaptive optimum vector perturbation technique. Unlike the conventional (Vector Perturbation) VP methods, here a novel anterograde tracing is utilized which is usually recognized in the nervous system thus reducing complexity. The tracing of distance can be done through an iterative-optimization procedure. By this novel non-linear technique the capacity is improved to a greater extend which is explained practically. By means of this, the computational complexity is managed to be in the cubic order of the size of MUMIMO, and this mainly derives from the inverse of the channel matrix. The proposed signal processing system has been implemented in the working platform of MATLAB/SIMULINK. The simulation results of proposed communication system and comparison with existing systems shows the significance of the proposed work.

Deep Learning Based Efficient Symbol-Level Precoding Design for MU-MISO Systems

The recently emerged symbol-level precoding (SLP) technique has been regarded as a promising solution in multi-user wireless communication systems, since it can convert harmful multi-user interference (MUI) into beneficial signals for enhancing system performance. However, the tremendous computational complexity of conventional symbol-level precoding designs severely hinders the practical implementations. In order to tackle this difficulty, we propose a novel deep learning (DL) based approach to efficiently design the symbol-level precoders. Particularly, in this correspondence, we consider a multi-user multi-input single-output (MU-MISO) downlink system. An efficient precoding neural network (EPNN) is introduced to optimize the symbol-level precoders for maximizing the minimum quality-of-service (QoS) of all users under the power constraint. Simulation results demonstrate that the proposed EPNN based SLP design can dramatically reduce the computing time at the price of slight performance loss compared with the conventional convex optimization based SLP design.

Comprehensive solution to the problem of electromagnetic compatibility of modern information and communication systems

The analysis of the problems of electromagnetic compatibility (EMC) of multi-user information and communication systems (ICS), using code division as a way of providing multiple subscribers access to system resources, in which each subscriber occupies the entire frequency band and the entire time interval. It is shown that the problem of electromagnetic compatibility of ICS as the possibility of a conflict-free existence of various wireless ICS in the conditions when each of these systems has the ability to receive its signals and signals of other systems is one of the highest priorities in the design and operation of such systems. It is shown that EMC requirements can be realized through the use of broadband noise-like signals (BNLS) as synchronization signals and signals – physical data carriers under various interfering influences, including narrow-band, wide-band obstruction, intrasystem (imitated, relayed) and other interferences caused by neighboring stations. This possibility is provided due to the use of signals with a large value of the time-frequency product (signal base) without increasing the signal duration and peak radiation power. Based on the use of the criterion of distinguishability of the signals of the minimum root mean square distance between the signals (vectors), the requirements for the synthesis and selection of the BNLS classes are formulated to ensure that the EMC requirements of multi-user wireless communication systems are met. A new class of complex nonlinear discrete cryptographic signals is proposed as data carrier signals and synchronization signals. It is shown that the use of such signals, due to the fact that they have improved ensemble, correlation, and structural properties, will allow for a comprehensive solution to the EMC problem of modern ICS.

Capacity and Optimal Resource Allocation for IRS-Assisted Multi-User Communication Systems

The fundamental capacity limits of intelligent reflecting surface (IRS)-assisted multi-user wireless communication systems are investigated in this paper. Specifically, the capacity and rate regions for both capacity-achieving non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission schemes are characterized under the constraints of a maximum number of IRS reconfiguration times. For NOMA, the ideal case with asymptotically large number of IRS reconfiguration times is considered, where the optimal solution is obtained by employing the Lagrange duality method. Inspired by this result, an inner bound of the capacity region for the general case with a finite number of IRS reconfiguration times is derived. For OMA, the optimal transmission strategy for the ideal case is to serve each individual user alternatingly with its effective channel power gain maximized. Based on this result, a rate region inner bound for the general case is derived. Finally, numerical results are provided to show that: i) a significant capacity and rate region improvement can be achieved by using IRS; ii) the capacity gain can be further improved by dynamically configuring the IRS reflection matrix.

LDPC encoding performances for fading suppression in MIMO-CDMA wireless networks

In multiuser wireless communication systems, signal recovery, even at low BER is a relevant factor for ensuring a safe and reliable communication. Efforts for interference minimization include multiple analysis of source coding/decoding techniques, channel coding/decoding techniques and multiuser de

Capacity Enhancement of Multiuser Wireless Communication System through Adaptive Non-Linear Pre coding

Multiuser multiple-input multiple-output (MIMO) nonlinear pre coding techniques face the issue of poor computational scalability of the size of the network. But by this nonlinear pre coding technique the interference is pre-cancelled automatically and also provides better capacity. So in order to reduce the computational burden in this paper, a definitive issue of MU-MIMO scalability is tackled through a non-linear adaptive optimum vector perturbation technique. Unlike the conventional (Vector Perturbation) VP methods, here a novel anterograde tracing is utilized which is usually recognized in the nervous system thus reducing complexity. The tracing of distance can be done through an iterative-optimization procedure. By this novel non-linear technique the capacity is improved to a greater extend which is explained practically. By means of this, the computational complexity is managed to be in the cubic order of the size of MUMIMO, and this mainly derives from the inverse of the channel matrix. The proposed signal processing system has been implemented in the working platform of MATLAB/SIMULINK. The simulation results of proposed communication system and comparison with existing systems shows the significance of the proposed work.

An Efficient Hybrid Transmission Method: Using Nonorthogonal Multiple Access and Multiuser Diversity

Nonorthogonal multiple access (NOMA) and multiuser diversity (MUD) are different transmission techniques that can improve the spectral efficiency of multiuser wireless communication systems. In this paper, we consider a hybrid switching strategy of NOMA and MUD (HSS-NM) by combining these two approaches in two-user and multiuser systems. With NOMA using successive interference cancellation and MUD based on proportional fair scheduling, we find out that one can perform better than the other according to certain channel conditions. From this, HSS-NM is proposed to improve spectral efficiency. Furthermore, we extend HSS-NM to a multiuser scenario with a user selection strategy. Simulation results demonstrate that the proposed method can achieve a relatively high sum-rate gain.

Multicast Pushing With Content Request Delay Information

Multicasting can reduce network traffic in multiuser systems by serving multiple users simultaneously. The benefits of multicasting basically come from the overlapping and synchronism of user requests. Pushing and caching are techniques that prestore content items in buffers closer to users based on the prediction of user requests, thereby providing a promising approach to eliminating the asynchronism of user requests and creating multicasting opportunities. This paper studies a multiuser wireless communication system, in which the buffers for caching are deployed in the user terminals. Based on the request delay information (RDI) which describes when the users request content items in a deterministic or statistical way, a joint pushing and caching (JPC) method is presented to schedule the content items pushed by the base station and cached in the user buffers. In this paper, multicasting JPC systems work in two modes, without or with the feedback of RDI. Furthermore, RDI is classified into three forms, namely, noncausal, statistical, and causal. Static and dynamic JPC policies are, respectively, proposed for the two work modes. In addition, the effective throughput resulting from JPC under the three forms of RDI is demonstrated via theoretical analysis and simulation.

Secure Communication Over Finite State Multiple-Access Wiretap Channel With Delayed Feedback

Recently, it has been shown that the time-varying multiple-access channel (MAC) with perfect channel state information (CSI) at the receiver and delayed feedback CSI at the transmitters can be modeled as the finite state MAC (FS-MAC) with delayed state feedback, where the time variation of the channel is characterized by the statistics of the underlying state process. To study the fundamental limit of the secure transmission over multi-user wireless communication systems, we re-visit the FS-MAC with delayed state feedback by considering an external eavesdropper, which we call the finite state multiple-access wiretap channel (FS-MAC-WT) with delayed feedback. The main contribution of this paper is to show that taking full advantage of the delayed channel output feedback helps to increase the secrecy rate region of the FS-MAC-WT with delayed state feedback. Moreover, by a degraded Gaussian fading example, we show the effects of feedback delay and channel memory on the secrecy sum rate of the FS-MAC-WT with delayed feedback.

Transmitter diversity scheme for OFCDMA systems based on space‐time spreading with iterative detection receiver

Orthogonal frequency code division multiple access (OFCDMA) system is one of the most promising multi-user wireless communications systems. It outperforms orthogonal frequency division multiplexing (OFDM) and multi-carrier CDMA (MC-CDMA) through utilisation of two-dimensional spreading. This study proposes a downlink air interface that targets data rate increase and bit error rate (BER) performance enhancement with low complexity receiver. The proposed system is an integration of space-time spreading (STS) and OFCDMA that exploits transmit diversity needed for BER enhancing and data rate boosting with low complexity advantage. Further BER improvement was achieved using an effective iterative interference cancellation (IIC) algorithm at the receiver. Analytical and complexity analyses for the proposed system performance are presented in addition to simulation results. The proposed system attains better performance with lower complexity compared to the STS-aided direct sequence MC-CDMA that uses beamforming as a closed-loop transmit diversity. Moreover, the effect of frequency domain spreading factor with different number of iteration loops is investigated. The achieved BER performance was very close to maximal ratio receive combining system with 1Tx and 4Rx. A considerable improvement was obtained by increasing the number of IIC iteration loops. The system performance was enhanced significantly with the frequency domain spreading factor increase.

Optimal Performance Versus Fairness Tradeoff for Resource Allocation in Wireless Systems

Resource allocation is a challenging issue in multiuser wireless systems. Since users are not all in the same conditions and do not achieve the same performance given the same amount of resources, resource allocation must typically deal with the following two conflicting objectives: on the one hand, the overall performance should be maximized, tending to prioritize few users in better conditions, and on the other hand, fairness among users should be maximized, consequently reducing the overall performance. Identifying the compromise that is somehow optimum is all but simple and this aspect is normally worsened by the absence of a curve describing how the maximum performance varies changing the accepted level of fairness. To cover this gap, in this paper, we propose a unified and general mathematical formulation of the optimal performance versus fairness tradeoff in multiuser wireless communication systems. Differently from the existing literature, our approach is based on the generalized Lagrange method and adopts general definitions for both performance and fairness. Besides the exact solving equations describing how the maximum performance varies with fairness, we also derive a simpler lower bound with reduced computational cost. Example results are provided for two case studies, respectively, concerning linear and logarithmic dependence of performance on resources.

Layered Spatial Modulation for Multiuser Communications

A layered spatial modulation (LSM) scheme is proposed for the downlink of multiuser wireless communication systems. The proposed scheme falls in the class of the well-known point-to-point SM as it employs only one antenna in each transmission interval. However, it is designed based on assigning different sets of transmit antennas, rather than single antenna elements, to transmit different symbols of each user. We first study the error performance of the proposed LSM. In order to improve the overall error performance of the system, we employ a graphical approach to adaptively allocate antennas to different users. We also propose some techniques to increase the spectral efficiency of LSM. Extending the proposed method to the generalized LSM, we also compare the maximum transmission rate of the proposed scheme with that of the existing SM-based multiuser schemes, and show that this technique can significantly improve the spectral efficiency of the SM-based multiuser communications.

Multi-Objective Optimization for Robust Power Efficient and Secure Full-Duplex Wireless Communication Systems

In this paper, we investigate the power efficient resource allocation algorithm design for secure multiuser wireless communication systems employing a full-duplex (FD) base station (BS) for serving multiple half-duplex (HD) downlink (DL) and uplink (UL) users simultaneously. We propose a multi-objective optimization framework to study two conflicting yet desirable design objectives, i.e., total DL transmit power minimization and total UL transmit power minimization. To this end, the weighed Tchebycheff method is adopted to formulate the resource allocation algorithm design as a multi-objective optimization problem (MOOP). The considered MOOP takes into account the quality-of-service (QoS) requirements of all legitimate users for guaranteeing secure DL and UL transmission in the presence of potential eavesdroppers. Thereby, secure UL transmission is enabled by the FD BS and would not be possible with an HD BS. The imperfectness of the channel state information of the eavesdropping channels and the inter-user interference channels is incorporated for robust resource allocation algorithm design. Although the considered MOOP is non-convex, we solve it optimally by semidefinite programming (SDP) relaxation. Simulation results not only unveil the trade-off between the total DL transmit power and the total UL transmit power, but also confirm the robustness of the proposed algorithm against potential eavesdroppers.

Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel

An interference alignment (IA) transceiver design scheme for multiple-input–multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI).