Multiple-input Multiple-output Downlink Systems Research Articles

A novel non-iterative algorithm for the joint design of transceiver beamforming and surface reflection in an IRS-enhanced MIMO system

Abstract Intelligent reflecting surface (IRS) is an emerging and low-cost revolutionary technology that can be deployed in future communication systems to enhance data transmission performance. An IRS comprises a lot of small, passive, and low-cost elements that can smartly reflect the incident signal from the transmitter to the receiver. In this paper, an IRS-enhanced multiple-input multiple-output (MIMO) downlink network is considered, in which a cell-edge user receives the data signal from a base station (BS). In this network, the direct path between the BS and the user is blocked by an obstacle, and the user receives only the tunable reflected signal from the IRS. To achieve spatial diversity, beamforming is applied to the antennas at the BS and the user. The goal is to jointly design the transceiver beamforming vectors and the IRS reflection coefficients so that the signal-to-noise ratio (SNR) of the user is maximized. Since the IRS elements are passive, the amplitude of the IRS reflection coefficients must be equal to or smaller than one. The constrained SNR optimization problem is non-convex. We propose a three-step procedure to obtain an effective sub-optimal solution for this problem. Accordingly, an innovative non-iterative algorithm is proposed to design the problem parameters. Simulation results show that the IRS-enhanced MIMO downlink system, in which the proposed non-iterative algorithm is used to develop the network parameters, outperforms the conventional network without IRS in terms of bit error rate (BER).

Nonlinear One-Bit Precoding for Massive MIMO Downlink Systems With l₀-Norm Constraint

Using one-bit digital-to-analog converters can significantly reduce the power consumption of base station in the massive multiple-input multiple-output downlink system. In this letter, we introduce $l_{0}$ -norm constraint on quantized transmit signals in the nonlinear one-bit precoding to further reduce the number of radio-frequency chains and thus the power consumption. This precoding is first formulated as a non-convex optimization problem, then the alternative direction method of multipliers (ADMM) is employed to solve this problem. Specifically, an efficient two-step implementation of the non-convex projection operator in ADMM is presented. Finally, the performance of proposed scheme and the effectiveness of proposed algorithm are validated by computer simulations.

Toward the energy efficiency of resource allocation algorithms for OFDMA downlink MIMO systems

The problem of the simultaneous multi-user resource allocation algorithm in orthogonal frequency division multiple access (OFDMA) based systems has recently attracted significant interest. However, most studies focus on maximizing the system throughput and spectral efficiency. As the green radio is essential in 5G and future networks, the energy efficiency becomes the major concern. In this paper, we develop four resource allocation schemes in the downlink OFDMA network and the main focus is on analyzing the energy efficiency of these schemes. Specifically, we employ the advanced multi-antenna technology in a multiple input-multiple output (MIMO) system. The first scheme is based on transmit spatial diversity (TSD), in which the vector channel with the highest gain between the base station (BTS) and specific antenna at the remote terminal (RT) is chosen for transmission. The second scheme further employs spatial multiplexing on the MIMO system to enhance the throughput. The space-division multiple-access (SDMA) scheme assigns single subcarrier simultaneously to RTs with pairwise “nearly orthogonal” spatial signatures. In the fourth scheme, we propose to design the transmit beamformers based on the zero-forcing (ZF) criterion such that the multi-user interference (MUI) is completely removed. We analyze the tradeoff between the throughput and power consumption and compare the performance of these schemes in terms of the energy efficiency.

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

Joint Data and Pilot Power Allocation for Massive MU-MIMO Downlink TDD Systems

This brief investigates the jointly optimal pilot and data power allocation among the users in a massive multiuser multiple-input multiple-output downlink system, in which the base station is equipped with a massive number of antennas and simultaneously serves single-antenna users in the same frequency band. First, a closed-form lower bound on the achievable rate is derived for each user where maximum-ratio transmission precoding is employed, and this closed form lower bound is used in defining the spectral efficiency (SE). Then, in order to maximize the SE, a method of power allocation among each of the pilot and data symbols of all the users is proposed, given the total power budget per coherence interval for all users. Numerical results show that the proposed power allocation scheme is superior to other existing methods in terms of higher SE.

A Hybrid Tomlinson–Harashima Transceiver Design for Multiuser mmWave MIMO Systems

This letter presents a new hybrid transceiver design for multiuser millimeter wave (mmWave) multiple-input–multiple-output downlink systems. To achieve improved sum-rate and error-rate performances, the proposed hybrid system is based on a combination of the Tomlinson–Harashima precoding and the block-diagonal geometric mean decomposition. With some novel observations and design choices, we showed that a closed-form solution can be obtained similarly for the hybrid design. Simulation shows the proposed design performs very close to its fully digital counterpart, and substantially outperforms the existing linear precoder-based hybrid transceivers.

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem which maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem that maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.

Statistical Eigenmode Transmission for the MU-MIMO Downlink in Rician Fading

In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen's inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SE-SDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the impact of the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician $K$-factor, on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician $K$-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zero-forcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician $K$-factor regime.

Efficient transceiver design for large-scale antenna systems

We present linear transceiver design strategies for large-scale multiple-input multiple-output downlink systems when the user nodes have multiple receive antennas, and they receive multiple streams. First, we propose a matched-filtering-based optimal power allocation method using the sum-rate maximization criterion and utilizing the favorable propagation property of large-scale antenna systems. In uncorrelated transmit antennas, we observe that the effective channel can be represented by the receive antenna correlation matrix. Motivated by this observation, we also propose a suboptimal two-step power allocation method in which each user node is equipped with two receive antennas to reduce the computational complexity of the optimum power allocation method. Compared with the optimum power allocation method, the proposed two-step method reduces the dimensions of the bisection search space while achieving essentially the same level of sum rate.

Joint Transmission Mode Selection and Scheduling in LTE Downlink MIMO Systems

In this article, we investigate multiple-input multiple-output (MIMO) transmission mode selection and scheduling for LTE downlink with users of different mobilities. Our algorithm first classifies users into high- and low-mobility ones, then selects open-loop spatial multiplexing (OLSM) or close-loop spatial multiplexing (CLSM) mode based on user's mobility, and finally assigns the time-frequency resources to ensure multi-user or frequency diversity. The proposed algorithm is with low complexity, considers all practical constraints in LTE systems, and can achieve up to 27% cell-average and 25% cell-edge throughput gain.

On the Performance of Downlink MIMO Systems with Different Operating Frequencies

Currently, multiple input multiple output (MIMO) systems are adopted in many current cellular systems such as Long Term Evolution or 802.16 m. However, the performance of MIMO systems is greatly dependent on system parameters and environments. In this study, we evaluate the performance of downlink MIMO systems to find out which operating frequency among 800 MHz, 2.5 GHz, and 3.5 GHz is the best for next generation MIMO cellular systems. For each operating frequency, we consider the most prevalent types of mobile stations (MSs), the USB type, PDA type, and laptop type, which experience different mutual coupling between neighbor antenna elements mainly caused by the miniaturization of MSs. System simulation results show that regardless of the MS type, average and edge throughputs are almost the same for all the considered frequencies in the interference-limited environment. In contrast, the operating frequency of 800 MHz at which the mobile type with a larger antenna spacing has spatial multiplexing gain, provides the best average and edge throughputs in noise limited system environments.

Joint beamforming and power control in downlink multiuser multiple-input multiple-output systems

In this paper, we study joint beamforming and power control for downlink multiple-input multiple-output systems with multiple users and target values for signal-to-interference plus noise ratios SINRs. We formulate this as a constrained optimization problem of minimizing total interference subject to constraints on the beamforming vector norms, target SINRs, and total transmit power. Necessary and sufficient conditions satisfied by the optimal beamformer and power allocation are presented, and a new algorithm for joint beamforming and power control is proposed. This adapts the beamforming vectors and transmit powers incrementally, and it stops when the specified SINR targets are achieved with minimum powers. The proposed algorithm is illustrated with numerical results obtained from simulations, which study its convergence and compare it with other similar algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

Low-complexity scheduling strategy for wireless multiuser multiple-input multiple-output downlink system

A new volume-based user selection algorithm with low complexity is proposed for a multiuser multiple-input and multiple-output downlink system based on block diagonalisation precoding. The new algorithm achieving this reduced computational complexity is compared with other user selection algorithms, such as semi-orthogonal user selection (SUS) and capacity-based user selection algorithms. The proposed algorithm stems from the new volume-based user selection method that uses the product of the diagonal elements in the upper-triangular matrix obtained via Householder reduction procedure of QR factorisation to the selected users channel matrix. The computational effort of the new algorithm is reduced by one-fourth compared with SUS algorithm. Compared with the capacity-based algorithm, the proposed algorithm does not need to perform the singular value decomposition operation and water-filling algorithm during each user selection step, and hence, significantly reduces the computational time. If the maximum number of supportable users is K, the calculation results show that the capacity-based algorithm has 4K times the complexity of the proposed algorithm. Furthermore, the simulation results demonstrate that the volume-based algorithm displays better capacity performance than the SUS algorithm, and the sum-rate capacity of the volume-based algorithm is comparable with that of the capacity-based algorithm but with much less computational complexity.

Transmit Antenna Subset Selection for Downlink MIMO Systems in Multicell Environments

This letter proposes a statistical cooperative scheme for transmit antenna subset selection in multiple-input multiple-output (MIMO) multicell systems. The proposed scheme is practical in that it only utilizes the statistical information for the adjacent links. However, it provides almost 90 percent of the average mutual information of the full cooperative scheme which requires a large amount of the instantaneous information and joint cooperation between base stations. Furthermore, the proposed scheme shows substantial performance enhancements over the competitive scheme in which the effects of intercell interference generated by the antenna selection are not considered.

On the Performance of a Two-User MIMO Downlink System in Heavy Traffic

A multiple-input multiple-output (MIMO) downlink system in which data is transmitted to two users over a common wireless channel is considered. The channel is assumed to be fixed for all transmissions over the period of interest and the ratio of anticipated average arrival rates for the two users, also known as the relative traffic rate, is the system design parameter. A packet-based traffic model is considered where data for each user is queued at the transmit end. A queueing analog for this system leads to a coupled queueing system for which a simple policy is known to be throughput-optimal under Markovian assumptions. Since an exact expression for the performance is not available, as a measure of performance (in heavy traffic), a diffusion approximation is established. This diffusion process is a two-dimensional (2-D) Semimartingale Reflecting Brownian Motion (SRBM) living in the positive quadrant of 2-D space