Signal-to-leakage-plus-noise Ratio Research Articles

Beam-space MIMO-NOMA Technique for mm-Wave Communication Systems

The multi-input-multi-output-antenna Scheme is regarded as the most promising method for next-generation wireless communication networks that employ millimeter-wave (mm-wave) frequencies and enable them to have a large information throughput. As a result of the large number of radio frequency chains, MIMO mm-wave technology has difficulty with energy consumption. The beam space MIMO (or BS-MIMO for short) reduces the total number of radio frequency components or chains (RF-Chains) without degrading performance significantly. Yeti, VS-MIMO restricts the number of clients that can be supported at the same time-frequency domain (cannot exceed the overall RF-chains number ). In order to cope with such problems, it has been intended to include the NOMA technique (which stands for Non-Orthogonal-Multiple-Access) with the concept of beam space to create the extra model of Non-Orthogonal beam space-MIMO (NOMA-BS-MIMO). With such a scheme the base station can support the service for more than one client (supposed to have correlated channels) through the same beam employing the same radio frequency chain, causing the total clients number greater than the number of employed radio chains in the system. In this research, the Spectral Efficiency of a proposed Non-Orthogonal beam space-MIMO has been considered. More specifically, the research proposes and develops a simple iterative algorithm with near-perfect performance in terms of system Spectral Efficiency. To achieve rate balancing across users, the proposed method, SLNR-IV, employs the signal-to-leakage-plus-noise ratio (SLNR) and the intermediate value method. Considering algorithm validity and a particular parameter setting scenario, the proposed strategy improves bandwidth efficiency by around 15% in comparison to the conventional approach based on OFDM and Zero-forcing digital precoder.

A novel low complexity beamforming scheme for coordinated multipoint networks

This article presents a quick overview of conventional precoding and beamforming techniques in the cellular networks and proposes a novel coordinated beamforming technique for coordinated multipoint (CoMP) networks, which is based upon signal-to-leakage-plus-noise ratio (SLNR) metric. The proposed scheme has reduced computational cost and offers improved performance compared to the conventional multi-cell precoding schemes in the noise-dominated environments. Simulation results reveal that proposed scheme offers a spectrally efficient, low complexity solution with improved average user throughput and sum rate in the CoMP enabled networks.

Proportional-Fair Resource Allocation for User-Centric Networks

Considering the computational complexity and fronthaul capacity requirements of network-wide centralized optimization for Cell-free (CF) massive multiple input multiple output (MIMO) networks, we focus on user-centric (UC) networks wherein each user is served by a subset of access points (APs) rather than all the APs. We study a proportional-fair resource allocation (RA) problem, including slot resource allocation and precoding design in UC networks over consecutive time-slots. We formulate the proportional-fair (PF) resource allocation problem as a series of weighted sum-rate maximization problems and develop a two-stage heuristic RA scheme to solve the problem. The scheme adopts a user grouping process to decompose resource allocation problem into intra-group orthogonal resource allocation sub-problems. A modularity-based user grouping algorithm upon a constructed weighted digraph is proposed. Then, relying on the grouping results, a parallel distributed PFRA algorithm consisting of user selection process and a Signal to Leakage plus Noise Ratio (SLNR)-based precoding design is proposed to fulfil intra-group resource allocation. Rather than global channel state information (CSI) collected from the whole network, local CSI obtained cooperatively within each AP subset is utilized in the proposed resource allocation scheme. Numerical results confirm that the proposed RA scheme outperforms in throughput while maintaining comparable fairness among users.

SLNR-Based Beamspace Precoding and Beam Selection for Wideband mmWave Massive MIMO

Beamspace multiple-input multiple-output (MIMO) systems leverage the lens antenna array to reduce their hardware cost, which is a promising technology in millimeter wave (mmWave) frequency bands. This letter proposes a novel signal-to-leakage-plus-noise ratio (SLNR)-based beamspace precoding and beam selection scheme to improve the downlink sum rate (SR) of the beamspace mmWave massive MIMO channels. First, for a given beam subset, a SLNR maximization-based beamspace precoder is designed to mitigate the inter-user interference caused by leakage power, thus enhancing the SR. Then, an approximated expression of the SR is formulated, based on which an iterative radio frequency (RF) precoding and beam selection method is designed to further improve the system SR. Numerical results demonstrate a superior SR performance of the proposed scheme over state-of-the-art methods.

Performance Analysis of Multi-User MIMO Schemes under Realistic 3GPP 3-D Channel Model for 5G mmWave Cellular Networks

Novel techniques such as mmWave transmission and massive MIMO have proven to present many attractive features able to support high data demand for 5G NR technologies. Towards the standardization of 5G networks, channel modeling has become an important step in order to test the reliability of theoretical studies. In this paper, we study the performance of a 5G network at mmWave range for the downlink. We consider a single trisectorized base station equipped with planar arrays, and we model users as a spatial Poisson process in a hexagonal grid. We adopt the latest 3GPP channel model described in TR 38.901 and we provide a thorough description and step-by-step tutorial of it along with our customizations and MATLAB scripts for channel generation in the presented scenario. Moreover, we evaluate the performance of Multi-User Multi-Layer MIMO techniques, such as Signal-to-Leakage-plus-Noise Ratio (SLNR) precoding and MMSE combined with different system configurations by means of achievable per-user rate.

SLNR-based Secure Energy Efficient Beamforming in Multibeam Satellite Systems

Motivated by the fact that both security and energy efficiency are the fundamental requirements and design targets of future satellite communications, this letter investigates secure energy efficient beamforming in multibeam satellite systems, where the satellite user in each beam is surrounded by an eavesdropper attempting to intercept the confidential information. To simultaneously improve the transmission security and reduce power consumption, our design objective is to maximize the system secrecy energy efficiency (SEE) under the constraint of total transmit power budget. Different from the existing schemes with high complexity, we propose an alternating optimization scheme to address the SEE problem by decomposing the original nonconvex problem into subproblems. Specifically, we first utilize the signal-to-leakage-plus-noise ratio (SLNR) metric to obtain closed-form normalized beamforming weight vectors, while the successive convex approximation (SCA) method is used to efficiently solve the power allocation subproblem. Then, an iterative algorithm is proposed to obtain the suboptimal solutions. Finally, simulation results are provided to verify the superiority of the proposed scheme compared to the benchmark schemes.

SLINR-Based Downlink Optimization in MU-MIMO Networks

Optimizing the downlink of multi-cell multiuser multiple input multiple output (MU-MIMO) networks has received substantial attention; however, the schemes in the literature consider centralized solutions requiring significant overhead in information exchange (e.g., global channel state information or CSI) and computation load (the need to solve a single large problem). This paper presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized</i> weighted sum-rate (WSR) maximization algorithm for the multiuser downlink, accounting for beamforming, scheduling, and power allocation. We show that the signal-to-leakage-plus-noise ratio (SLNR) used in previous work suffers from significant drawbacks that limit its potential use in WSR maximization. We address this by proposing a new performance measure, the signal-to-leakage-plus-interference-plus-noise ratio (SLINR), which incorporates <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intra-cell interference</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inter-cell leakage</i> . The SLINR exploits the benefits of the SLNR approach, but by explicitly including interference, avoids many of its flaws. We derive an iterative and decentralized resource allocation approach under imperfect CSI, and our simulation results show that, despite BSs using only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local information</i> , the proposed algorithm comes within 3.8% of the throughput achieved by centralized schemes.

Iterative Generalized Eigenvector Precoder With Deterministic Equivalents for 3D Massive MIMO

In this paper, we propose an iterative generalized eigenvector (GE) precoder design for three dimensional (3D) massive multi-input multi-output (MIMO) downlink with uniform planar array (UPA) and imperfect channel state information (CSI). We use a posterior beam based statistical channel model which includes the channel aging and the spatial correlation. We consider the problem of maximizing the expected sum-rate under a total power constraint. By replacing the expected sum-rate with their deterministic equivalents, we obtain the structure of the optimal linear precoders. The columns of the optimal precoding matrices are the generalized eigen-vectors of two matrices. One of the two matrices is related to the transmitted signal of the intended user, and the other is related to the power constraint and the leakage to other users. Furthermore, the two matrices are also functions of the precoders, thus iterative updates of the precoder are needed. Then, we investigate the power allocation and the computation of the Lagrangian multiplier, and propose an iterative algorithm based on the structure of the optimal precoders. Simulation results show that the proposed precoders can achieve significantly performance gain than the widely used regularized zero forcing (RZF) precoder and signal to leakage plus noise ratio (SLNR) precoder.

Joint Power Allocation and Hybrid Beamforming for Downlink mmWave-NOMA Systems

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter-Wave (mmWave) communications to address resource allocation issues for multiuser downlink transmission. Inspired by the K-means clustering algorithm, a NOMA user grouping policy is first employed in accordance with the channel correlation between users. The first-stage decoding order for each NOMA group is then determined by merely utilizing the users’ channel gain to formulate a joint power allocation and hybrid beamforming optimization problem. Specifically, we use signal-to-leakage-plus-noise ratio (SLNR) as the performance index of the optimization problem to reduce the computational complexity, because it can decouple the design of the beamforming and power allocation issues so they can be executed iteratively. Under given beamforming matrix, the power allocation issue is expressed as a quadratic programming (QP) problem, and then transformed into a convex problem by introducing an auxiliary-positive real variable to solve through the Lagrange multiplier method. On the contrary, the beamforming optimization problem is non-convex and very difficult to solve due to the constant modulus constraints in the hybrid architecture. However, the optimum solution of the ideal full-digital beamforming can be acquired primarily by generalized eigenvalue decomposition (GED). In this condition, two hybrid beamforming algorithms are proposed, in which normalized phase matching and equal gain transmission are performed respectively in the analog domain. On this basis, a second-stage decoding order can be employed according to the effective channel gain of users to perform successive interference cancellation (SIC) successfully. Numerical results show that the proposed joint power allocation and hybrid beamforming algorithms are superior to the classical mmWave-NOMA and orthogonal multiple access (OMA) schemes not only in lower implementation complexity, but also in better spectrum efficiency and energy efficiency performance.

Trajectory Learning, Clustering, and User Association for Dynamically Connectable UAV Base Stations

This work examines trajectory learning, clustering, and user association policies for dynamically connectable unmanned aerial vehicle base stations (UAV-BSs). Here, UAV-BSs are allowed to dynamically form physically-connected collocated antenna arrays that enable joint transmission and cooperative energy sharing among the connected UAV-BSs. The UAVs' trajectories, their clustering decisions, and the user association are jointly determined for the case with static users by maximizing a proportional-fair objective given by the sum log-rate of all users. Then, for the case with dynamic users, the UAVs' trajectory learning, re-clustering, and user handover policies are developed to adapt to changes in the users' locations. In particular, the UAVs' locations are adjusted gradually in each time slot based on a stochastic gradient ascent algorithm, and user handover decisions are made in each time slot based on a reward function that is inspired by the solution in the static case. The clustering decisions are updated every T time slots by combining two UAV clusters or by separating a cluster into two. To determine the transmission schemes in each time slot, a semi-orthogonal user scheduling policy is first employed to determine the users that are served in each time slot. Then, joint design of the transmit beamformers and powers is proposed to maximize the sum of log signal-to-leakage-plus-noise ratio (SLNR) of scheduled users. Simulations are provided to demonstrate the effectiveness of the proposed schemes.

Low-Complexity Multi-User Parameterized Beamforming in Massive MIMO Systems

In this paper, we design a complexity-reduced transmission scheme in massive antenna environments. To reduce the implementation complexity for the generation of beam weight, we design a multi-user parameterized beamforming (MUPB) scheme that can control the beam direction using a single parameter with combined use of maximum ratio transmission and partial zero-forcing scheme that partially nulls out interference. We design the MUPB to maximize the signal-to-leakage plus noise ratio (SLNR). To further reduce the implementation complexity, we optimize the MUPB based on approximated SLNR instead of accurate SLNR. Finally, the performance of the proposed MUPB is verified by computer simulation.

Majorization-Minimization Aided Hybrid Transceivers for MIMO Interference Channels

The potential of deploying large-scale antenna arrays in future wireless systems has stimulated extensive research on hybrid transceiver designs aiming to approximate the optimal fully-digital schemes with much reduced hardware cost, and signal processing complexity. Generally, this hybrid transceiver structure requires a joint design of analog, and digital processing to enable both beamsteering, and spatial multiplexing gains. In this paper, we develop various weighted mean-square-error minimization (WMMSE) based hybrid transceiver designs for ${K}$ -user multiple-input multiple-output (MIMO) interference systems, which are applicable to both millimeter wave (mmWave) channels, and Rayleigh fading channels. Firstly, a heuristic joint design of hybrid precoder, and combiner using alternating optimization is proposed, in which the majorization-minimization (MM) method is utilized to design the analog precoder, and combiner under unit-modulus constraints. It is demonstrated that this scheme achieves comparable performance to the fully-digital WMMSE solution. To further reduce the computational complexity, a phase projection based two-stage scheme is proposed to decouple the designs of the analog, and digital precoder/combiner. Secondly, inspired by the fully-digital solutions based on the block-diagonalization zero-forcing (BD-ZF), and signal-to-leakage-plus-noise ratio (SLNR) criteria, the low-complexity MM-based BD-ZF, and SLNR hybrid designs are proposed, respectively, for approximating the corresponding fully-digital solutions. Thirdly, the partially-connected hybrid structure conceived for reducing system hardware cost, and power consumption is considered, for which the MM-based alternating optimization algorithm still works. Our numerical results characterize the sum rate performance of all proposed hybrid designs in comparison to the existing benchmarks.

Transmission of wireless backhaul signal in a cellular system with small moving cells

Deployment of small moving cells (SMCs) has been considered in advanced cellular systems, where wireless backhaul links are required between base stations and SMCs. In this paper, we consider signal transmission by means of multiuser beamforming in the wireless backhaul link. We generate the beam weight in an eigen-direction of weighted combination of short- and long-term channel information of the backhaul link. The beam weight can maximize the average signal-to-leakage-plus-noise ratio (SLNR), while providing the transmission robust to SMC mobility. We analyze the performance of the proposed scheme in terms of the average signal-to-interference-plus-noise ratio (SINR) and optimize the transmit power by iterative water-filling. Finally, we verify the performance of the proposed scheme by computer simulation.

Multi-layer beamforming in uplink/downlink massive MIMO Systems with multi-antenna users

In this paper, considering a hybrid architecture at the base station (BS) in an m-MIMO system, multi-antenna users transmit/receive multiple symbols to/from the BS in the uplink/downlink transmissions. In the uplink transmission, we propose a hybrid analog/digital beamformer at the BS and digital precoders at users. The BS designs the analog beamformer via signal-to-leakage-plus-noise ratio (SLNR) maximization criterion. Then, in the baseband, each user designs its precoder maximizing its effective signal power. Finally, the received signal at the BS is digitally combined with a regularized zero-forcing (RZF) filter. For the downlink transmission, we also design the hybrid beamformer at the BS and digital reception filters at users. In such a configuration, in order to design the hybrid beamformer, the digital precoders and the reception filters, we need much less channel knowledge compared to the large dimensional original channel matrices. Besides, as we show in the simulations, in spatially correlated channels, with smaller number of RF chains compared to the number of antennas, the performance of the proposed approaches would achieve the performance of the fully digital beamformer in terms of sum spectral efficiency (SE) and bit error rate (BER). Moreover, applying beamforming techniques at multi-antenna users further improves the overall network throughput.

Joint Beamforming Design for Energy Efficient Wireless Communications in Heterogeneous Intelligent Connected Vehicles Networks

The wireless communications is studied for the intelligent connected vehicles (ICV) networks by supporting heterogeneous access with direct access of one vehicle yet the dual-hop access of another vehicle. The dual-hop access is enhanced by one dedicated vehicle as relaying vehicle with multiple antennas. The goal in this paper is to joint design of both receive beamforming and transmit beamforming at the relaying multi-antenna vehicle to suppress the interference between the two wireless receive vehicles yet match the dual-hop wireless channel as much as possible. The energy efficiency is optimized as the objective function under the constraint of the limited power over every beamforming vector. Due to the difficulty of the optimization, the principle of the signal to leakage plus noise ratio (SLNR) is introduced to the studied ICV networks and thus to obtain the analytical expression of the beamforming vectors. With the derived closed form over all the beamforming vectors, an iterative algorithm is developed for jointly optimizing all the beamforming vectors to maximize the energy efficiency of the whole system, which is guaranteed to be convergent to one local optimum. Numerical simulations show the good performances of the proposed method in ICV networks.

Agglomerative User Clustering and Cluster Scheduling for FDD Massive MIMO Systems

Two-stage precoding is a promising transmission strategy for frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its large multiplexing gain with significant overhead reduction in both downlink training and feedback. In this paper, we propose a new agglomerative clustering method to significantly simplify the user clustering process. In order to suppress the residual inter-cluster interference in realistic two-stage precoding transmission, we propose an average signal-to-leakage-plus-noise ratio (SLNR)-based iterative cluster scheduling and outer precoder design scheme to achieve a balance between providing high multiplexing gain and improving per-user rate. For uniform linear arrays (ULAs), a fast implementation of the scheme with discrete Fourier transformation (DFT) approximation is proposed. The numerical results demonstrate the performance improvement of the proposed methods over the existing methods.

Joint Scheduling and Deep Learning-Based Beamforming for FD-MIMO Systems Over Correlated Rician Fading

In this paper, we investigate the downlink transmission for full-dimension multiple-input multiple-output (FD-MIMO) systems over correlated Rician fading channels. With only statistical channel state information at the BS, the beamforming vectors of each user are decoupled from each other through the maximization of average signal-to-leakage-plus-noise ratio (SLNR) lower bound, and the optimal beamforming vector which involves the line-of-sight component and correlation matrix of both directions is derived. To reduce the time of acquiring the beamforming vector for each user, a deep learning (DL)-based algorithm is proposed. A sub-optimal analytical beamforming algorithm is also proposed for comparison. Based on the proposed beamforming method, the ergodic rate of each user is analyzed and simple closed-form approximations are derived, which are very useful in evaluating the system performance and user scheduling. Moreover, two user scheduling algorithms referred to as most dissimilar and minimum interference-to-signal factor algorithm are proposed, which greatly reduce the complexity compared to the sum rate based greedy scheduling algorithm. Simulation results validate the performance of the proposed beamforming and scheduling algorithms, and verify the accuracy of the derived ergodic rate approximations.

User scheduling for downlink FD-MIMO systems under Rician fading exploiting statistical CSI

In this paper, we consider the user scheduling algorithm for downlink full-dimension multiple-input multiple-output (FD-MIMO) system under Rician fading channels. We assume that two-dimensional (2D) large-scale antenna array is deployed at base station (BS). An approximation of users signal-to-interference-plus-noise ratio (SINR) and a lower bound of users average signal-to-leakage-plus-noise ratio (SLNR) are derived. Based on these, two user scheduling algorithms exploiting only statistical channel state information (CSI) are proposed. The proposed algorithms take both the achievable sum rate and fairness into account. Simulation results reveal that the proposed user scheduling algorithms can make good trade-off between the achievable rate and fairness.

MU-MIMO Downlink Capacity Analysis and Optimum Code Weight Vector Design for 5G Big Data Massive Antenna Millimeter Wave Communication

Multiuser multiple input multiple output (MU-MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam-vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal-to-leakage plus noise ratio (SLNR), we analyze the ergodic sum-rate capacity using statistical Eigen-mode (SE) and zero-forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum-rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum-rate capacity.

Base Station Selection for Massive MIMO Networks With Two-Stage Precoding

The two-stage precoding has been proposed to reduce the overhead of both the channel training and the channel state information (CSI) feedback for the massive multiple-input multiple-output (MIMO) system. But the overlap of the angle-spreading-ranges (ASR) for different user clusters may seriously degrade the performance of the two-stage precoding. In this letter, we propose one ASR overlap mitigating scheme through the base station (BS) selection. Firstly, the BS selection is formulated as a sum signal-to-interference-plus-noise ratio (SINR) maximization problem. Then, the problem is solved by a low-complex algorithm through maximizing signal-to-leakage-plus-noise ratio (SLNR). In addition, we propose one low-overhead algorithm with the lower bound on the average SLNR as the objective function. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.