Low-complexity User Selection Research Articles

Low-Complexity User Selection Algorithms for Multiuser Transmissions in mmWave WLANs

In this paper, we propose a low-complexity user selection algorithm for an uplink multiuser transmission in millimeter wave (mmWave) WLAN. We first formulate the user selection problem, taking hybrid beamforming (HBF), an NP-hard problem, into consideration. We then develop a three-step HBF algorithm that incorporates user selection. Specifically, users can be selected based on semi-orthogonality instead of collecting perfect channel state information (CSI) from all potential users. We optimize the digital beamforming to mitigate residual interference among the selected users. Furthermore, we provide analytical validation for the proposed user selection algorithm and study the impact of angle correlation, analog beam pattern, and beamwidth on the achievable rate of the selected users. Extensive simulations validate the performance of the proposed overall HBF algorithm when compared with existing solutions.

Low Complexity User Selection and Power Allocation for Uplink NOMA Beamforming Systems

In this paper, we develop user selection and power allocation methods for NOMA systems equipped with multi-antenna to enhance the sum capacity of the uplink. The system has the base station (BS) with $$ N $$ antenna supporting 2N users in the same spectrum resource simultaneously, and successive interference cancellation (SIC) is applied at the BS. Because the superposition of multiple users in transmission within the same frequency resource block leads to the interference among users, we derive a user set selection algorithm and a suboptimal power control to mitigate the interference effect and to maximize the sum capacity. The user set selection algorithm is first applied by comparing the designed indicator which balances the factors affecting the sum capacity of the uplink. Second, the derived suboptimal power assignment algorithm is utilized with power control factors where four cases are evaluated. The simulation results show that the proposed schemes can significantly improve the sum capacity than the existing methods.

Low-Complexity Joint Antenna and User Selection Scheme for the Downlink Multiuser Massive MIMO System with Complexity Reduction Factors

Low-Complexity Joint Antenna and User Selection Scheme for the Downlink Multiuser Massive MIMO System with Complexity Reduction Factors

Robust Beamforming Design for Ultra-Dense User-Centric C-RAN in the Face of Realistic Pilot Contamination and Limited Feedback

The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users' rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

Low Complexity User Selection With Optimal Power Allocation in Downlink NOMA

This letter investigates user selection and power allocation among users for an orthogonal frequency division multiplexing-based nonorthogonal multiple access scheme. We have proposed a low-complexity method for user selection by removing users who do not satisfy the derived condition. A closed form solution for optimal power allocation to the chosen users is derived by maximizing the weighted sum rate of the users. Results show that the proposed method achieves a performance similar to the optimal scheme employing exhaustive user search but with much lower complexity.

Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a user-centric cloud radio access network, where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker conditions of the WPM problem. Moreover, a low-complexity algorithm based on Newton's method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side. Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC.

Secure Communication in Uplink Transmissions: User Selection and Multiuser Secrecy Gain

In this paper, we investigate secure communications in uplink transmissions, where there are a base station (BS) with M receive antennas, K mobile users each with a single antenna, and an eavesdropper with N receive antennas. The closed-form expressions of the achievable ergodic secrecy sumrates (ESSR) for a random k users selection scheme in the high and low SNR regimes are presented. It is shown that the scaling behavior of ESSR with respect to the number of served users k can be quite different under different system configurations, determined by the numbers of the BS antennas and that of the eavesdropper antennas. In order to achieve a multiuser gain, two low-complexity user selection schemes are proposed under different assumptions on the eavesdropper's channel state information (CSI). The closed-form expressions of the achievable ESSRs and the multiuser secrecy gains of the two schemes are also presented in both low and high SNR regimes. We observe that, as k increases, the multiuser secrecy gain increases, while the ESSR may decrease. Therefore, when N is much larger than M, serving one user with the strongest channel (TDMA-like) is a favourable secrecy scheme, where the ESSR scales with root square of 2 log K.

WiFlix: Adaptive Video Streaming in Massive MU-MIMO Wireless Networks

We consider the problem of simultaneous on-demand streaming of stored video to multiple users in a multicell wireless network where multiple unicast streaming sessions are run in parallel and share the same frequency band. Each streaming session is formed by the sequential transmission of video “chunks,” such that each chunk arrives into the corresponding user playback buffer within its playback deadline. We formulate the problem as a network utility maximization (NUM) where the objective is to fairly maximize users’ video streaming quality of experience (QoE) and then derive an iterative control policy using Lyapunov optimization, which solves the NUM problem up to any level of accuracy and yields an online protocol with control actions at every iteration decomposing into two layers interconnected by the users’ request queues : 1) a video streaming adaptation layer reminiscent of dynamic adaptive streaming over HTTP (DASH), implemented at each user node; and 2) a transmission scheduling layer where a max-weight scheduler is implemented at each base station. The proposed chunk request scheme is a pull strategy where every user opportunistically requests video chunks from the neighboring base stations and dynamically adapts the quality of its requests based on the current size of the request queue. For the transmission scheduling component, we first describe the general max-weight scheduler and then particularize it to a wireless network where the base stations have multiuser multiple-input multiple-output (MU-MIMO) beamforming capabilities. We exploit the channel hardening effect of large-dimensional MIMO channels (massive MIMO) and devise a low complexity user selection scheme to solve the underlying combinatorial problem of selecting user subsets for downlink beamforming, which can be easily implemented and run independently at each base station. Furthermore, through simulations, we show that deploying MU-MIMO significantly improves video streaming performance and also that the proposed cross-layer approach is able to serve users more fairly than a baseline scheme representative of current systems running independently designed protocol layers.

Low-Complexity Downlink User Selection for Massive MIMO Systems

In this paper we propose a pair of low-complexity user selection schemes with zero-forcing precoding for multiuser massive MIMO downlink systems, in which the base station is equipped with a large-scale antenna array. First, we derive approximations of the ergodic sum rates of the systems invoking the conventional random user selection (RUS) and the location-dependant user selection (LUS). Then, the optimal number of simultaneously served user equipments (UEs), $K^*$, is investigated to maximize the sum rate approximations. Upon exploiting $K^*$, we develop two user selection schemes, namely $K^*$-RUS and $K^*$-LUS, where $K^*$ UEs are selected either randomly or based on their locations. Both of the proposed schemes are independent of the instantaneous channel state information of small-scale fading, therefore enjoying the same extremely-low computational complexity as that of the conventional RUS scheme. Moreover, both of our proposed schemes achieve significant sum rate improvement over the conventional RUS. In addition, it is worth noting that like the conventional RUS, the $K^*$-RUS achieves good fairness among UEs.

Experimental Evaluation of Low Complexity User Selection Schemes for Multi-User MIMO Systems

Experimental Evaluation of Low Complexity User Selection Schemes for Multi-User MIMO Systems

Resource allocation for multiple input multiple output‐orthogonal frequency division multiplexing ‐based space division multiple access systems

The problem of the system throughput maximisation and the fairness issue among users is considered for the downlink multiuser multiple input multiple output-orthogonal frequency division multiplexing system with MMSE precoding. Since finding the optimal solution by using the exhaustive search has very high computational burden, the authors propose a low-complexity user selection strategy plus the optimal power allocation in this study. With resorting to the mathematical simplification process, the proposed user selection scheme is designed to compare the related value of SINR. The power allocation is performed after the user selection. However, the power allocation is not in interference-free environment but in the presence of crosstalk, which is a nonlinear non-convex optimisation problem. In this study, the authors use an optimisation technique to obtain the optimal solution of the constrained non-convex nonlinear problem by replacing the objective function with a difference of two convex functions. A bisection-based strategy to solve this problem with interference-awareness to find local optimal solutions is presented in this study. Simulation results show that the performance of the proposed algorithm is close to that of the optimal solution. The trade-off between the system throughput and the fairness among users is also considered.

Low-Complexity User Selection for Rate Maximization in MIMO Broadcast Channels with Downlink Beamforming

We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves a large portion of the optimum user selection sum rate (90%) for a moderate number of active users.

A low-complexity user selection scheme in a multicell MIMO environment

Abstract An efficient user selection scheme for the downlink of multiuser MIMO systems is proposed in a multicell environment. In a multicell environment, the intercell interference is one of the most influential factors limiting the performance. Thus, a user selection scheme that considers intercell interference is essential to increase the sum rate. The proposed scheme is based on an interference-aware precoding. It sequentially selects users such that the sum rate is maximized. In particular, we develop a simple incremental metric for the sum rate. The use of the derived metric enables a significant reduction in the computational complexity of the user selection process, as compared to the optimal exhaustive search. Numerical results show that the proposed scheme provides near-optimal performance with substantially reduced complexity.

Weighted Sum Capacity Maximization Using a Modified Leakage-Based Transmit Filter Design

This paper proposes a linear transmit filter design to maximize weighted sum capacity (WSC) in multiuser multiple-input–multiple-output (MU-MIMO) systems. The proposed scheme is based on a modified signal-to-leakage-plus-noise ratio (SLNR) criterion, which integrates receiver structures and power allocations into the precoder design and can efficiently exploit unused receiver subspaces. Based on the proposed transmitter (TX) design with receive matched filters (MFs), the WSC maximization problem can be simplified to power-allocation and data-stream selection problems. A power-allocation algorithm for finding a local optimal solution is also proposed and is shown to be obtained by the iteration of closed-form water-filling (WF) solutions. Furthermore, a low-complexity user and substream selection is proposed as an alternative solution to maximize WSC. Simulation results show that the proposed algorithms outperform the conventional scheme and achieve comparable performance to a joint transceiver design, despite requiring simpler receiver structures.

A novel low-complexity user selection scheme for multiuser MIMO downlink with zero-forcing beamforming

This letter proposed a user selection with swap (USS) algorithm based on zero-forcing beamforming (ZFBF). USS starts with an initial user group with the largest channel 2-norms, iteratively swap the “worst” user (has the minimal achieved rate) among the scheduled users with the “best” user (has the maximum channel 2-norm) among the remaining users. Based on the simulation results, we can confirm that the proposed USS algorithm can provide a comparable performance with previous algorithms with greatly reduced complexity of O(KN2).

Iterative Precoder Design and User Scheduling for Block-Diagonalized Systems

The block diagonalization (BD) scheme is a low-complexity suboptimal precoding technique for multiuser multiple input-multiple output (MIMO) downlink channels, which completely precancels the multiuser interference. Accordingly, the precoder of each user lies in the null space of other users' channel matrices. In this paper, we propose an iterative algorithm using QR decompositions (QRDs) to compute the precoders. Specifically, to avoid dealing with a large concatenated matrix, we apply the QRD to a sequence of matrices of lower dimensions. One problem of BD schemes is that the number of users that can be simultaneously supported is limited due to zero interference constraints. When the number of users is large, a set of users must be selected, and selection algorithms should be designed to exploit the multiuser diversity gain. Finding the optimal set of users requires an exhaustive search, which has too high computational complexity to be practically useful. Based on the iterative precoder design, this paper proposes a low-complexity user selection algorithm using a greedy method, in which the precoders of selected users are recursively updated after each selection step. The selection metric of the proposed scheduling algorithm relies on the product of the squared row norms of the effective channel matrices, which is related to the eigenvalues by the Hadamard and Schur inequalities. An asymptotic analysis is provided to show that the proposed algorithm can achieve the optimal sum rate scaling of the MIMO broadcast channel. The numerical results show that the proposed algorithm achieves a good trade-off between sum rate performance and computational complexity. When users suffer different channel conditions, providing fairness among users is of critical importance. To address this problem, we also propose two fair scheduling (FS) algorithms, one imposing fairness in the approximation of the data rate, and another directly imposing fairness in the product of the squared row norms of the effective channel matrices.

Greedy User Selection Using a Lattice Reduction Updating Method for Multiuser MIMO Systems

User selection plays a crucial role in multiple-access channels (e.g., uplink channels of cellular systems) to exploit the multiuser diversity. Although the achievable rate can be adopted for a performance indicator in user selection, it may not be proper if a suboptimal detector or decoder is employed. In particular, for multiuser multiple-input-multiple-output (MIMO) systems, a low-complexity suboptimal MIMO detector can be used instead of optimal MIMO detectors, which require prohibitively high computational complexity. Under this practical circumstance, it may be desirable to derive user selection criteria based on the error probability for a given low-complexity MIMO detector. In this paper, we propose a low-complexity greedy user selection scheme with an iterative lattice reduction (LR) updating algorithm when an LR-based MIMO detector is used. We also analyze the diversity gain for combinatorial user selection approaches with various MIMO detectors. Based on the simulation results, we can confirm that the proposed greedy user selection approach can provide a comparable performance with the combinatorial approaches with much lower complexity.

Eigen-Based Transceivers for the MIMO Broadcast Channel With Semi-Orthogonal User Selection

This paper studies the sum rate performance of two low complexity eigenmode-based transmission techniques for the MIMO broadcast channel, employing greedy semi-orthogonal user selection (SUS). The first approach, termed ZFDPC-SUS, is based on zero-forcing dirty paper coding; the second approach, termed ZFBF-SUS, is based on zero-forcing beamforming. We first employ new analytical methods to prove that as the number of users K grows large, the ZFDPC-SUS approach can achieve the optimal sum rate scaling of the MIMO broadcast channel. We also prove that the average sum rates of both techniques converge to the average sum capacity of the MIMO broadcast channel for large K. In addition to the asymptotic analysis, we investigate the sum rates achieved by ZFDPC-SUS and ZFBF-SUS for finite K, and show that ZFDPC-SUS has significant performance advantages. Our results also provide key insights into the benefit of multiple receive antennas, and the effect of the SUS algorithm. In particular, we show that whilst multiple receive antennas only improves the asymptotic sum rate scaling via the second-order behavior of the multi-user diversity gain; for finite K, the benefit can be very significant. We also show the interesting result that the semi-orthogonality constraint imposed by SUS, whilst facilitating a very low complexity user selection procedure, asymptotically does not reduce the multi-user diversity gain in either first (log K) or second-order (loglog K) terms.

Sum rates, rate allocation, and user scheduling for multi-user MIMO vector perturbation precoding

This paper considers the multiuser multiple-input multiple-output (MIMO) broadcast channel. We consider the case where the multiple transmit antennas are used to deliver independent data streams to multiple users via vector perturbation. We derive expressions for the sum rate in terms of the average energy of the precoded vector, and use this to derive a high signal-to-noise ratio (SNR) closed-form upper bound, which we show to be tight via simulation. We also propose a modification to vector perturbation where different rates can be allocated to different users. We conclude that for vector perturbation precoding most of the sum rate gains can be achieved by reducing the rate allocation problem to the user selection problem. We then propose a low-complexity user selection algorithm that attempts to maximize the high-SNR sum rate upper bound. Simulations show that the algorithm outperforms other user selection algorithms of similar complexity.

Sum-rate analysis of multiuser MIMO system with zero-forcing transmit beamforming

In this letter, we present the exact sum-rate analysis of the multiuser multiple-input multiple-output (MIMO) systems with zero-forcing transmit beamforming (ZFBF). We develop the analytical expressions of the ergodic sum-rate for two low-complexity user selection strategies for the dual-transmit-antenna scenario. Based on the analytical results, we examine the parameter optimization problem to properly trade off between channel power gain and directional gain in term of maximizing the ergodic sum rate.