SINR Constraints Research Articles

Intelligent reflecting surfaces assisted integrated information and energy networks in D2D communications

AbstractIntelligent Reflecting Surface (IRS) is set to revolutionize future wireless networks, particularly in Device to Device (D2D) communication. Its unique intelligence lies in dynamically reshaping signal propagation and enhancing network performance. The proposed model ensures receivers in a D2D network are accessible only through strategically placed IRS elements, optimizing signal propagation and coverage. Simulations demonstrate the solution's effectiveness, enhancing communication for Information Users (IUs) and energy harvesting for Energy Users (EUs) while meeting SINR constraints. Our efficient algorithms tackle the non‐convex problem, enabling exclusive transmission of information signals from the AP to serve both IUs and EUs with improved performance.

Interleaved Training Scheme for Multi-User Massive MIMO Downlink With User SINR Constraint

Interleaved Training Scheme for Multi-User Massive MIMO Downlink With User SINR Constraint

Dual-Step Hybrid Mechanism for Energy Efficiency Maximization in Wireless Network

Abstract Wireless networks have become essential in daily life, with a growing number of base stations and connected devices. However, increasing traffic and energy consumption pose challenges. This research proposes a Dual Step Hybrid Mechanism (DSHM) for energy optimization, incorporating MIMO technologies. The first step introduces an optimal algorithm that iteratively updates the probability distribution to achieve the best solution. The second step focuses on reducing energy consumption while maximizing energy efficiency, using specific techniques and strategies to minimize usage without compromising energy maximization. The proposed approach is evaluated using parameter settings, including block length, path loss, hardware impairments, and bandwidth. The research investigates the impact of hardware impairments on energy efficiency and analyzes performance under different SINR constraints. The study also examines energy efficiency in active user density and base station density, highlighting the superior energy efficiency achieved by MIMO configurations.

Robust Design of IRS-Aided Multi-Group Multicast System With Imperfect CSI

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

Simultaneous Transmission and Reflection Beamforming Design for RIS-Aided MU-MISO

In this paper, we study the beamforming design for the simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted MU-MISO system, where the energy splitting (ES) mode is adopted for STAR-RIS. We aim to jointly design the beamforming strategy at the BS and the transmission-reflection coefficients (TRCs) at the STAR-RIS to minimize the total downlink transmit power of the base station (BS) subject to each user's SINR constraint. The formulated optimization problem is difficult to solve directly, and therefore we employ the iterative alternating optimization (AO) framework to obtain suboptimal solutions with promising performance. Specifically, we propose an AO-based solution to optimize each amplitude and phase of the TRCs at the STAR-RIS separately in a sequential manner, where two distinct approaches are introduced for the design of the amplitudes of STAR-RIS elements. Simulation results show that the proposed joint beamforming design at BS and passive design at STAR-RIS achieves a promising performance, and requires fewer iterations compared with the state-of-the-art.

Deep Reinforcement Learning-Assisted Optimization for Resource Allocation in Downlink OFDMA Cooperative Systems.

This paper considers a downlink resource-allocation problem in distributed interference orthogonal frequency-division multiple access (OFDMA) systems under maximal power constraints. As the upcoming fifth-generation (5G) wireless networks are increasingly complex and heterogeneous, it is challenging for resource allocation tasks to optimize the system performance metrics and guarantee user service requests simultaneously. Because of the non-convex optimization problems, using existing approaches to find the optimal resource allocation is computationally expensive. Recently, model-free reinforcement learning (RL) techniques have become alternative approaches in wireless networks to solve non-convex and NP-hard optimization problems. In this paper, we study a deep Q-learning (DQL)-based approach to address the optimization of transmit power control for users in multi-cell interference networks. In particular, we have applied a DQL algorithm for resource allocation to maximize the overall system throughput subject to the maximum power and SINR constraints in a flat frequency channel. We first formulate the optimization problem as a non-cooperative game model, where the multiple BSs compete for spectral efficiencies by improving their achievable utility functions while ensuring the quality of service (QoS) requirements to the corresponding receivers. Then, we develop a DRL-based resource allocation model to maximize the system throughput while satisfying the power and spectral efficiency requirements. In this setting, we define the state-action spaces and the reward function to explore the possible actions and learning outcomes. The numerical simulations demonstrate that the proposed DQL-based scheme outperforms the traditional model-based solution.

Multi-User Downlink Beamforming Using Uplink Downlink Duality With 1-Bit Converters for Flat Fading Channels

The increased power consumption of high-resolution data converters at higher carrier frequencies and larger bandwidths is becoming a bottleneck for communication systems. In this paper, we consider a fully digital base station equipped with 1-bit analog-to-digital (in uplink) and digital-to-analog (in downlink) converters on each radio frequency chain. The base station communicates with multiple single antenna users with individual SINR constraints. We first establish the uplink downlink duality principle under 1-bit hardware constraints under an uncorrelated quantization noise assumption. We then present a linear solution to the multi-user downlink beamforming problem based on the uplink downlink duality principle. The proposed solution takes into account the hardware constraints and jointly optimizes the downlink beamformers and the power allocated to each user. Optimized dithering obtained by adding dummy users to the true system users ensures that the uncorrelated quantization noise assumption is true under realistic settings. Detailed simulations carried out using 3GPP channel models generated from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quadriga</i> show that our proposed solution outperforms state of the art solutions in terms of the ergodic sum and minimum rate especially when the number of users is large. We also demonstrate that the proposed solution significantly reduces the performance gap from non-linear solutions in terms of the uncoded bit error rate at a fraction of the computational complexity.

OPTIMAL MULTIUSER DOWNLINK COOPERATIVE BEAMFORMING IN COGNITIVE RADIO NETWORKS USING ADMISSION CONTROL

In our proposed work, optimal multiuser downlink cooperative beam forming in cognitive radio networks is considered with target SINR. In already existing works, the transmit beam forming vectors for maximum number of admitted secondary users into the cognitive radio network are designed with fixed target SINR and sum power constraints. However, this joint optimization problem becomes infeasible as some users may absorb more power to meet the target SINR. Hence, the objective of this work is to design optimal multiuser downlink beam forming vectors for primary and secondary base stations in a cooperative manner using admission control satisfying the practical bounded SINR constraints. This is solved using approximate message passing technique. The performance of this cognitive radio network is analysed with the constraints. Keywords—Beam forming

Transmitter and receiver design for integrated full‐duplex multiple‐input‐multiple‐output communication and multiple‐input‐multiple‐output radar system

This study discusses the integrated system of full-duplex (FD) multiple-input-multiple-output (MIMO) communication and MIMO radar, which has been rarely considered in published studies. Such an integrated system is composed of a base station and several uplink (UL) and downlink (DL) users, and is supposed to simultaneously implement three functionalities, that is, target detection, UL and DL communications. The degrees of freedom of system design involve the precoding and combining schemes for UL and DL communications, as well as the receiving filter for radar echo. An optimization problem is developed to maximise a compound communication sum-rate metric subject to the SINR constraint imposed on target detection performance. The resulted non-convex and NP-hard problem is first divided into two sub-problems. Then, for one of the sub-problems that is still intractable, its solution is iteratively obtained based on the fractional programing technique and the consensus alternating direction method of multipliers algorithm. In the simulation part, the convergence performance and computational efficiency of the proposed algorithm are illustrated. In addition, the target detection performance, DL and UL communication performance of the integrated system are also evaluated.

Optimization of a Color-Based Spatial Modulation Scheme for VLC Under Illuminance and SINR Constraints

Optimization of a Color-Based Spatial Modulation Scheme for VLC Under Illuminance and SINR Constraints

SATPAS: SINR-based adaptive transmission power assignment with scheduling in wireless sensor network

In this work, a centralized metaheuristic algorithm is proposed for assigning minimum transmission power to each sensor node for every transmission link along with a scheduling slot while satisfying the Signal to Interference plus Noise Ratio constraint in the Wireless Sensor Network. Simultaneous communication in all communication links is not possible while maintaining SINR constraints. Thus, the algorithm also tries to maximize the number of concurrent transmission links in each scheduling slot. We formulate the optimization problem with two objective functions as (i) Communication link wise adaptive and minimum transmission power assignment together with scheduling slot number and (ii) Minimization of the required number of scheduling slots. Probabilistic interference model, which is more realistic, is used for calculating the receiver interference, unlike the graph-based interference model mostly described in the literature. Genetic Algorithm with Edge-set and Edge-window-decoder chromosome representation schemes are used for optimization. Extensive simulations are carried out to demonstrate the efficiency of proposed algorithms using different benchmark data sets and special network topologies like spiral, cluster, and mesh. Our proposed algorithm outperforms the existing MST-based algorithm in terms of minimum transmission power requirement, number of scheduling slots required, and rate of convergence.

Optimization of hybrid beamforming for multiuser massive MIMO systems with individual SINR and SLL constraints

Optimization of hybrid beamforming for multiuser massive MIMO systems with individual SINR and SLL constraints

Distributed robust time-efficient broadcasting algorithms for multi-channel wireless multi-hop networks with channel disruption

Broadcasting is a fundamental problem for wireless multi-hop networks such as wireless ad hoc and sensor networks. In this paper, we study distributed robust time-efficient global broadcasting for SINR-based (i.e., Signal-to-Interference-plus-Noise-Ratio-based) multi-channel wireless multihop networks subject to channel disruption. In this study, we make the following assumptions: i) for successful packet reception, the SINR at receivers must exceed a certain threshold; ii) an adversary exists in the network, which uniformly and randomly chooses t > 0 channels to disrupt from total F > 1 available channels in every round. To address the broadcasting issue in such networks, we first propose a degree-aware broadcasting algorithm and then propose a degree-independent broadcasting algorithm. The proposed algorithms elaborately integrate distributed channel selection and transmission scheduling at different nodes while considering the following factors: the impact of SINR constraint, availability of channels, sending probability on selected channel, presence of adversary, with and without node degree information. We present the detailed design of the two algorithms. We deduce their worst-case time performance to accomplish the task of global broadcasting with high probability. We further prove that both algorithms can still preserve the same time performance under more powerful oblivious adversary and weakly adaptive adversary. Simulation results show that the proposed algorithms have satisfactory average-case time performance.

A novel modified genetic algorithm for cooperative multiuser detection with minimum transmitter power and SINR constraint

An efficient solution to prevent multiple access interference and reduce transmitter power in cellular communication systems is cooperative communications. In multiuser cooperative communications, the users transmit their signals to the receivers through multiple relay nodes. Simultaneous optimum detection of multiuser signals considering the constraints of transmitter power and SINR satisfaction is the main challenge of these systems. As known, the optimum detector in multiuser communications is maximum likelihood (ML) detector but it is not applicable because of computational complexity. In this paper, to detect the desired transmitted signal in receivers, the problem of optimum multiuser detection is optimized using Lagrange multipliers and due to complexity of the resulted output, modified dimension-reduction genetic algorithm is applied based on dividing the problem with large number of parameters (users) into several simpler problems with less number of parameters (users). The main idea is the selection of more critical parameters in first-round optimization. The optimization is done subjected to two constraints as SINR and minimum transmitter power to guarantee the quality of service (QoS) in the cooperative communication system. Simulation results show that our proposed algorithm’s performance is near the optimum ML detector and it is more efficient in comparison to other well-known sub-optimum algorithms in the literature with satisfying the QoS of the system.

MU-MIMO Communications With MIMO Radar: From Co-Existence to Joint Transmission

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts both as a 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.

Queue‐aware energy minimisation through sparse beamforming in C‐RAN

This paper considers the queue-aware optimal energy minimisation sparse beamforming design (QESB) in a downlink cloud radio access network (C-RAN) system where multi-RRH communicates with multi-user through a central computing cloud via digital front-haul links. The problem is formulated as the joint optimisation problem of the transmission energy consumption, system queue length and front-haul cost with sparse beamforming design. As we know, the beamforming design adaptive to both QSI and CSI is challenging because of the high complexity. Apart from previous works that take queue length as constraints, in this paper we directly minimise the queue length state involved joint optimisation problem with SINR constraints. A smooth function is proposed to approximate the l 0 -norm function which is discrete and non-convex. To overcome the challenge due to the non-convexity of the optimisation problem, the semidefinite relaxation (SDR) technology is utilised to convert the primitive problem into the difference of convex (DC) programming problem, and convex and concave procedure (CCP) algorithm is used to induce the sparsity of the beamforming control. The simulation results show that the scheme proposed by this paper can obtain a good tradeoff between system energy consumption, queue length and front-haul cost with SINR constraints in C-RAN system.

D2D multicast retransmission algorithm in mobile cloud based on SINR constraint

In cellular network, users with same demand and in proximity to each other form the mobile cloud, in which the short-range D2D technology is employed by users to improve the data dissemination efficiency. In view of the fact that the D2D links with the poor channel conditions are likely to be the bottleneck of resource utilization improvement, aiming at the differentiation of link quality, this paper proposes a intra-cloud D2D multicast retransmission algorithm based on SINR constraint to meet the minimum requirement of D2D retransmission for QoS. In the proposed algorithm, the model of system link cost is built, the number of multicast retransmission times is restricted and each link quality matrix is traversed to reasonably select the multicast transmitter as well as its routing, which further reduces the link cost consumption, and in turn improves the bandwidth efficiency. Simulation results show that the proposed algorithm is more efficient to improve the bandwidth utilization when the ratio between normal user and non-normal user is small in mobile cloud.

Sample Approximation-Based Deflation Approaches for Chance SINR Constrained Joint Power and Admission Control

Consider the joint power and admission control (JPAC) problem for a multi-user single-input single-output (SISO) interference channel. Most existing works on JPAC assume the perfect instantaneous channel state information (CSI). In this paper, we consider the JPAC problem with the imperfect CSI, that is, we assume that only the channel distribution information (CDI) is available. We formulate the JPAC problem into a chance (probabilistic) constrained program, where each link's SINR outage probability is enforced to be less than or equal to a specified tolerance. To circumvent the computational difficulty of the chance SINR constraints, we propose to use the sample (scenario) approximation scheme to convert them into finitely many simple linear constraints. Furthermore, we reformulate the sample approximation of the chance SINR constrained JPAC problem as a composite group sparse minimization problem and then approximate it by a second-order cone program (SOCP). The solution of the SOCP approximation can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure (the deflation approach). We exploit the special structure of the SOCP approximation and custom-design an efficient algorithm for solving it. Finally, we illustrate the effectiveness and efficiency of the proposed sample approximation-based deflation approaches by simulations.

Outage Based Robust Relay Beamforming Problem in Multi-User Networks

In this paper, a robust relay beamforming problem has been dealt with by using the stochastic approach with imperfect knowledge of channel state information. Specifically, we have modeled the channel estimation error as a Gaussian random variable. Our objective is to minimize the total relay's transmit power subject to outage SINR constraint criterion at each receiver. We aim to establish approximate probabilistic SINR constrained formulations in the form of convex conic optimization problem. It is shown that the original robust problem is a nonconvex problem, but it can be relaxed to an SDP convex problem using the semi-definite relaxation technique. In this paper, we have compared our stochastic method with the worst-case robust method in terms of power consumption efficiency and complexity. Simulation results verify that our robust method outperforms the worst case approach.

Interference Neutralization for Non-Regenerative Two-Way Relaying Networks&amp;#x2014;Part II: Optimization

In Part II of this two-part paper, we design optimal relay transmit strategies for a nonregenerative two-way relaying network with multiple repeaters and smart relays with or without interference neutralization (IN). We develop a general framework to optimize different system utility functions, i.e., minimize the required transmit power at the relay subject to minimum SINR constraints, maximize the minimum SINR of the users subject to relay transmit power constraint(s), and maximize the weighed system sum rate subject to relay transmit power constraint(s). Simulation results show that the proposed algorithms outperform the state of art time-division multiplexing (TDM) based algorithm. Compared to the optimal design without applying IN, the IN approach has a reduced computational complexity although it suffers from a small performance loss.