Semi-orthogonal User Selection Algorithm Research Articles

OPTIMAL PAIR-WISE SUS ALGORITHM AS PROPORTIONAL FAIR ALGORITHM FOR MU-MIMO-OFDM SYSTEMS

In this paper, we research proportional fairness of the optimal pair-wise semi-orthogonal user selection (SUS) algorithm used in the MU-MIMO-OFDM system compliant with IEEE 802.11ac standard. Zero-forcing beamforming (ZFBF) is applied in the system as a precoding technique in order to cancel user interferences. Two different scenarios are considered: homogeneous and heterogeneous. Beside the proportional fairness, a throughput performance of the MU-MIMO-OFDM system is analyzed for both scenarios too. Simulation results show that the optimal pair-wise SUS algorithm is an excellent solution to realize the trade-off between the proportional fairness among users and the achieved system throughput.

Influence of optimal pair-wise SUS algorithm on MU-MIMO-OFDM system performance

In this paper we proposed a new user scheduling algorithm, named as optimal pair-wise semi-orthogonal user selection (SUS), for multiuser multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) system. Multiuser interferences are canceled applying zero-forcing beamforming (ZFBF) technique with presumption that channel state is perfectly known at transmit side. Simulated throughput and error results demonstrate advantage gain achieved in system performance realised through applying the proposed scheduling algorithm.

Hybrid MU-MIMO Precoding Based on K-Means User Clustering

Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) systems have been extensively investigated over the last few years from both theoretical and practical perspectives. The low complexity Linear Precoding (LP) schemes for MU-MIMO are already deployed in Long-Term Evolution (LTE) networks; however, they do not work well for users with strongly-correlated channels. Alternatives to those schemes, like Non-Linear Precoding (NLP), and hybrid precoding schemes were proposed in the standardization phase for the Third-Generation Partnership Project (3GPP) 5G New Radio (NR). NLP schemes have better performance, but their complexity is prohibitively high. Hybrid schemes, which combine LP schemes to serve users with separable channels and NLP schemes for users with strongly-correlated channels, can help reduce the computational burden, while limiting the performance degradation. Finding the optimum set of users that can be co-scheduled through LP schemes could require an exhaustive search and, thus, may not be affordable for practical systems. The purpose of this paper is to present a new semi-orthogonal user selection algorithm based on the statistical K-means clustering and to assess its performance in MU-MIMO systems employing hybrid precoding schemes.

An improved Semi-orthogonal User Selection algorithm based on condition number for multiuser MIMO systems

In Multiple-Input Multiple-Out (MIMO) systems, the user selection algorithm plays an important role in the realization of multiplexing gain. In this paper, an improved Semi-orthogonal User Selection algorithm based on condition number is proposed. Besides, a new MIMO precoding scheme is designed. The proposed SUS-CN (SUS with condition number) algorithm outperforms the SUS algorithm for the selection of users with better matrix inversion property, thus a higher information rate for selected user pair is achieved. The designed MIMO precoding matrix brings benefits of the power equality at transmitted terminals, the limited dynamic range of the power over time, and a better power efficiency. The simulation results give the key insights into the impact of the different condition number value and users on the sum-rate capacity.

Simplified Semi-Orthogonal User Selection for MU-MIMO Systems with ZFBF

In this letter, we simplify the well-known semi-orthogonal user selection (SUS) algorithm for the multi-user multiple-input multiple-output (MU-MIMO) system with zero-forcing beamforming (ZFBF). Compared with the original SUS algorithm, the proposed user selection algorithm achieves the same performance, but with significantly less complexity. Simulation results show that the proposed algorithm can make a good tradeoff between performance and complexity for the MU-MIMO system with ZFBF.

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.

A low-complexity beamforming-based scheduling to downlink OFDMA/SDMA systems with multimedia traffic

In this paper, we propose a low-complexity beamforming-based scheduling scheme utilizing a semi-orthogonal user selection (SUS) algorithm in downlink orthogonal frequency division multiple access (OFDMA)/space division multiple access (SDMA) systems to support multimedia traffic. One of the challenges in the multi-dimensional (space, time, and frequency) radio resource allocation problem for OFDMA/SDMA systems is its high complexity, especially to simultaneously satisfy the quality of services (QoS) requirements for various traffic classes. In the literature, the SUS algorithm is usually applied to the single-class traffic environment, but extending the SUS algorithm to the multimedia environment is not straightforward because of the need to prioritize the real-time (RT) users and the non-real-time (NRT) users. To solve this problem, we propose the concept of urgency value to guarantee the fairness of the NRT as well as the best effort (BE) users while satisfying the delay requirement for the RT users. Simulation results show that, when traffic load is greater than 0.5, the proposed scheduling algorithm can improve the fairness performance by more than 100% over the most recently proposed algorithms.

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.

An Improved User Selection Algorithm in Multiuser MIMO Broadcast with Channel Prediction

In multiuser MIMO-BC (Multiple-Input Multiple-Output Broadcasting) systems, user selection is important to achieve multiuser diversity. The optimal user selection algorithm is to try all the combinations of users to find the user group that can achieve the multiuser diversity. Unfortunately, the high calculation cost of the optimal algorithm prevents its implementation. Thus, instead of the optimal algorithm, some suboptimal user selection algorithms were proposed based on semiorthogonality of user channel vectors. The purpose of this paper is to achieve multiuser diversity with a small amount of calculation. For this purpose, we propose a user selection algorithm that can improve the orthogonality of a selected user group. We also apply a channel prediction technique to a MIMO-BC system to get more accurate channel information at the transmitter. Simulation results show that the channel prediction can improve the accuracy of channel information for user selections, and the proposed user selection algorithm achieves higher sum rate capacity than the SUS (Semiorthogonal User Selection) algorithm. Also we discuss the setting of the algorithm threshold. As the result of a discussion on the calculation complexity, which uses the number of complex multiplications as the parameter, the proposed algorithm is shown to have a calculation complexity almost equal to that of the SUS algorithm, and they are much lower than that of the optimal user selection algorithm.