Semi-orthogonal User Selection Research Articles

Dynamic Scheduling and Power Allocation with Random Arrival Rates in Dense User-Centric Scalable Cell-Free MIMO Networks

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency.

Numerical Evaluation of the MU-MIMO Beamforming Performance with User Selection Algorithm

This paper presents the numerical evaluation of the ZF beamforming algorithm using the user selection in the multiuser multiantenna (MU-MIMO) downlink system. Two user selection algorithm – semiorthogonal user selection and greedy user selection are numerically evaluated based on the open source MIMO channel model. The sum rate performance depending on number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. The selection of users is based on the orthogonality of the channels among selected users. For MIMO channel simulation the QUADRIGA channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder using user selection (G ZF) outperforms the ZF precoder with random user selection in spectral efficiency. The greedy user selection in spatially correlated channel has advantage to semi-orthogonal user selection. It isobserved that as the increasing the number of served users used for selection the greedy user selection gives better performance than semi-orthogonal algorithm.

User Selection Methods for Overcoming Growing Number of Served Users in Massive MIMO Systems

Massive MIMO is dominant in the current wireless communication systems. Massive MIMO system uses a massive number of antennas to serve multi-users simultaneously. The growth of served users in the system will increase the interference between them and affect the system’s performance. To maintain the qualified service for the growing number of users, user selection techniques can be used to separate users into groups to be served well. This paper proposes three user selection methods named Mean Step User Selection (MSUS), Second Null User Selection (SNUS), and Interference Threshold User Selection (ITUS) methods. These three user selection methods aim to serve users with the best possible performance. The performance of these proposed user selection methods will be evaluated and compared with the performance of the system without selection and with the system using other user selection methods such as Random User Selection (RUS), Semi-orthogonal User Selection (SUS), and Inter-Channel Interference Based Selection (ICIBS) methods. The simulation shows that the proposed MSUS, SNUS, and ITUS methods provide an improvement in the spectral efficiency of 75.39%, 92.13%, and 153.71% when compared with the system without the selection method. The proposed user selection methods also improve performance compared to other user selection methods.

Effective User Clustering and Power Control for Multiantenna Uplink NOMA Transmission

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

Cylindrical Massive MIMO System with Low-Complexity Angle-Based User Selection for High-Altitude Platform Stations

High-altitude platform stations (HAPSs) are recognized as a promising technology for coverage extension in the sixth generation (6G) mobile communications and beyond. The purpose of this study is to develop a HAPS system with a coverage radius of 100km and high capacity by focusing on the following two aspects: array antenna structure and user selection. HAPS systems must jointly use massive multiple-input multiple-output (mMIMO) and multiuser MIMO techniques to increase their capacity. However, the coverage achieved by a conventional planar array antenna is limited to a circular area with a radius of only tens of kilometers. A conventional semi-orthogonal user selection (SUS) scheme based on the orthogonality of channel vectors achieves high capacity, but it has high complexity. First, this paper proposes a cylindrical mMIMO system to achieve an ultra-wide coverage radius of 100km and high capacity. Second, this paper presents a novel angle-based user selection (AUS) scheme, where a user selection problem is formulated as a maximization of the minimum angular difference between users over all user groups. Finally, a low-complexity suboptimal algorithm (SA) for AUS is also proposed. Assuming an area with a 100km radius, simulation results demonstrate that the proposed cylindrical mMIMO system improves the signal-to-interference-plus-noise ratio by approx. 12dB at the boundary of the area, and it achieves approx. 1.5 times higher capacity than the conventional mMIMO which uses a planar array antenna. In addition, the results show that the proposed AUS scheme improves the lower percentiles in the system capacity distribution compared with SUS and basic random user selection. Furthermore, the computational complexity of the proposed SA is in the order of only 1/4000 that of SUS.

Sum-Rate Improvement in Massive MIMO System with User Grouping and Selection, and Antenna Scheduling Scheme

In this paper, we have proposed user grouping method and new user selection techniques in TDD Massive (MIMO) systems. Using K-means clustering algorithm we separate the users into different groups. Upon user groups are formed users are selected from different groups with Semi-Orthogonal (SO) and random user selection criteria. We use two user selection method to select users from different groups, those are-(i) Users are selected in intra-group those are SO with each other along with it also SO with other group’s users, (ii)-Users are selected from inter-group those are SO with each other. For antenna scheduling we used maximum channel gain measure in both the user selection situation. Semi-Orthogonal user selection (SOU) and antenna scheduling with zero forcing precoding demonstrated maximum system sum-rate that is seen in results. In table-1 appraised the computational cost of our modified SOU algorithm. The proposed algorithm’s efficiency is explored through the simulations.

An Efficient Method for Combining Multi-User MIMO Tomlinson-Harashima Precoding With User Selection Based on Spatial Orthogonality

Combining multi-user multiple-input and multiple-output (MU-MIMO) Tomlinson-Harashima precoding (THP) with semiorthogonal user selection (SUS) yields both space diversity and multi-user diversity benefits; however, it entails a significant computational effort. To overcome this limitation while retaining system-level performance, this paper proposes an efficient method for combining MU-MIMO THP with SUS. The proposed method is inspired by the fact that the ordering calculations in both THP and SUS are generally based on spatial orthogonality. Particularly, the ordering result of SUS is fully reused for the ordering in THP, thereby eliminating the ordering calculation in the latter. Through a system-level performance evaluation, the proposed method is compared to a traditional method with the ordering process, thereby demonstrating the effectiveness of the proposed method. Numerical results obtained via computer simulations show that although the proposed method achieves almost the same system capacity as the traditional MU-MIMO THP method with ordering, the computational complexity of the proposed method decreases substantially with an increase in the number of antennas.

Performance of Data-Rate Analysis of Massive MIMO System Using User Grouping

The major challenges in massive MIMO networks are to increase the system throughput and capacity with low complexity and reliability of the wireless communication system. The computational complexity and system capacity of massive MIMO (M-MIMO) systems depend on adequate user grouping, user selection, and antenna scheduling methods. Additionally, the interference of the M-MIMO system is reduced when used suitable signal processing schemes. In this paper, we proposed a novel circular user grouping technique to reduce system complexity. Later, semi-orthogonal user selection with three different combinations of user selection schemes and norm-based antennas scheduling method is used to achieve higher system capacity in M-MIMO systems. Moreover, simple linear zero-forcing and minimum mean square error signal processing schemes are used to minimize the user’s interference. The simulation results show that our proposed algorithm provides very low computational complexity, reduce the interference and higher the system capacity compare to the traditional M-MIMO system.

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.

Zero-Forcing Oriented Power Minimization for Multi-Cell MISO-NOMA Systems: A Joint User Grouping, Beamforming, and Power Control Perspective

Future wireless communication systems have been imposed high requirement on power efficiency for operator's profitability as well as to alleviate information and communication technology (ICT) global carbon emission. To meet these challenges, the power consumption minimization problem for a generic multi-cell multiple input and single output non-orthogonal multiple access (MISO-NOMA) system is studied in this work. The associated joint user grouping, beamforming (BF) and power control problem is a mixed integer non-convex programming problem, which is tackled by an iterative distributed methodology. Towards this end, the near-optimal zero-forcing (ZF) BF is leveraged, wherein the semiorthogonal user selection (SUS) strategy is applied to select BF users. Based on these, the BF vectors and BF users are determined for each cell using only local information. Then, two distributed user grouping strategies are proposed. The first one, called channel condition based user clustering (CCUC), performs user grouping in each cell based on the channel conditions. This is conducted independently of the power control part and has low computational complexity. Another algorithm, called power consumption based user clustering (PCUC), uses both the channel conditions and inter-cell interference information to minimize each cell's power consumption. In contrary to CCUC, PCUC is optimized jointly with the power control. Finally, with the obtained user grouping and BF vectors, the resultant power allocation problem is optimally solved via an iterative algorithm, whose convergence is mathematically proven given that the problem is feasible. We perform Monte-Carlo simulation and numerical results show that the proposed resource management methods outperform various conventional MISO schemes and the non-clustered MISO-NOMA strategy in several aspects, including power consumption, outage probability, energy efficiency, and connectivity efficiency.

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.

Combined User and Antenna Selection in Massive MIMO Using Precoding Technique

Background and Objectives: We propose here joint semi-orthogonal user selection and antenna selection algorithm based on precoding scheme. Methods: The focus of this proposed algorithm is to increase the system sumrate and decrease the complexity. We select and schedule users from a large number of users based on semi-orthogonality condition among them. Here, we select only the maximum channel gain antennas to maximize the system sumrate. Subsequently, the user selection and antenna selection have been scheduled in an adequate manner in order to obtain maximum system sumrate. We calculate the system sumrate for two scenarios: firstly, by considering the interference and secondly without considering the interference. We achieve maximum system sumrate at MMSE and lowest at without precoding while considering the interference. However, when we do not consider the interference we obtain lowest sumrate at MMSE and maximum at without precoding. Results and Conclusion: Here, we apply the precoding scheme to increase the system sumrate and we obtain approximately 20% to 35% higher system sumrate compared to without precoding, when interference is considered. Thus, we achieve higher sumrate in our proposed algorithms compared to other existing work.

On Achievable Multiuser Diversity Gain of Limited-Feedback-Based Block Diagonalization in Interference-Limited MIMO Broadcast Channels

The scaling law of sum spectral efficiency achieved by limited-feedback-based block diagonalization with semi-orthogonal user selection in an interference-limited multiple-input–multiple-output broadcast channel is investigated. An interference-limited regime is realized by assuming a large transmit power. An analysis of the distribution of the channel quality indicator of scheduling makes it possible to characterize the limiting behavior of the spectral efficiency. From the limiting behavior, it is shown that the achievable multiuser diversity gain can be significantly increased as the system approaches the interference-limited regime from large user regimes.

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

The capacity of a massive MIMO cellular network depends on user and antenna selection algorithms, and also on the acquisition of perfect Channel State Information (CSI). Low computational cost algorithms for user and antenna selection significantly may enhance the system capacity, as it would consume a smaller bandwidth out of the total bandwidth for downlink transmission. The objective of this paper is to maximize the system sum-rate capacity with efficient user and antenna selection algorithms and linear precoding. We consider in this paper, a slowly fading Rayleigh channel with perfect acquisition of CSI to explore the system sum-rate capacity of a massive MIMO network. For user selection, we apply three algorithms, namely Semi-orthogonal user selection (SUS), Descending Order of SNR-based User Scheduling (DOSUS), and Random User Selection (RUS) algorithm. In all the user selection algorithms, the selection of Base Station (BS) antenna is based on the maximum Signal-to-Noise Ratio (SNR) to the selected users. Hence users are characterized by having both Small Scale Fading (SSF) due to slowly fading Rayleigh channel and Large-Scale Fading (LSF) due to distances from the base station. Further, we use linear precoding techniques, such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Ratio Transmission (MRT) to reduce interferences, thereby improving average system sum-rate capacity. Results using SUS, DOSUS, and RUS user selection algorithms with ZF, MMSE, and MRT precoding techniques are compared. We also analyzed and compared the computational complexity of all the three user selection algorithms. The computational complexities of the three algorithms that we achieved in this paper are O(1) for RUS and DOSUS, and O(M2N) for SUS which are less than the other conventional user selection methods.

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.

Sum-Rate Performance of Massive MIMO Systems in Highly Scattering Channel with Semi-Orthogonal and Random User Selection

The sum-rate performance of a massive MIMO system is dependent on the adequate user and antenna selection techniques. It is best when all the selected users are mutually orthogonal (MOU) and antennas are selected based on maximum channel gain. In the case, when number of antennas in massive MIMO is approximately hundreds, the computational complexity cost in joint user and antenna selection is very high. Semi-orthogonal user selection (SUS) is preferred to reduce interference and complexity cost. In highly scattering Rayleigh fading channel random user selection (RUS) technique can be used because of small loss of system sum-rate compared toSUSandMOUtechniques. In this paper we propose a joint user and antenna selection algorithm where users are scheduled using semi-orthogonality measure and antenna selection is based on maximum channel gain. In addition, an algorithm for joint user and antenna selection is reported here with RUS for user selection and maximum channel gain for antenna selection. We explore the system sum-rate performance of a massive MIMO system using these algorithms for the case of a highly scattering Rayleigh fading channel. We consider various precoding techniques MMSE, ZFBF, and MRT and a range of SNR. We also explore the effect of spatial and multiuser diversity on system sum-rates using proposed algorithms. The sum-rate obtained by algorithms-1 is greater than the algorithm-2 about 1–3% but second one is much simpler.

A New Approach to User Scheduling in Massive Multi-User MIMO Broadcast Channels

In this paper, a new two-phase-feedback-based user-scheduling-and-beamforming method is proposed for multi-user multiple-input multiple-output downlink in the context of two-stage beamforming. The key ideas of the proposed method are: 1) to use a set of orthogonal reference beams and construct a cone around each reference beam to select “nearly-optimal” semi-orthogonal users based only on channel quality indicator feedback; and 2) to apply post-user-selection beam design with zero-forcing beamforming (ZFBF) based on channel state information (CSI) feedback only from the selected users. It is proven that the proposed scheduling-and-beamforming method is asymptotically optimal as the number of users increase. Furthermore, the proposed scheduling-and-beamforming method almost achieves the performance of the existing semi-orthogonal user selection with ZFBF that requires full CSI for all users, with a significantly reduced amount of required channel information which is even less than that required by random beamforming.

Hybrid secure beamforming and vehicle selection using hierarchical agglomerative clustering for C-RAN-based vehicle-to-infrastructure communications in vehicular cyber-physical systems

The architecture of C-RAN-based soft-defined vehicular networks can support increasing demands of various applications in the future vehicular cyber-physical systems, but lacks strong and practical security–enhancing mechanisms in the physical layer. In this article, we propose a hybrid beamforming and vehicle-selection framework for vehicle-to-infrastructure communications to broadcast high-speed confidential messages. A one-dimensional roadway scenario having multiple lanes is considered, and the concept of secure region and interference-selection region is, respectively, developed for each roadside unit. We formulate an optimization problem of maximizing secrecy sumrate with the constraint of orthogonality-based vehicle selection. The original problem is then divided into two subproblems subtly. In terms of the first subproblem, we, by exploiting the orthogonality assumption, obtain a corresponding secure beamformer for each vehicle located in the secure region to reduce the interference and confidential information leakage. Based on secure beamformers, the second subproblem is changed to be a vehicle-selection problem, which is then solved by adopting hierarchical agglomerative clustering method and implemented by two proposed novel algorithms blending vehicle selection with beamformer generation. Simulation results verify the effectiveness of the proposed algorithms in the respect of secrecy sumrate compared with the conventional zero-forcing beamforming using semi-orthogonal user selection algorithms. Furthermore, simulations show how our proposed algorithms resist against eavesdropping from collusive vehicles located in the same secure region by flexibly altering the size of secure region and interference-selection according to the traffic density.

Power minimization for cooperative MIMO-OFDM systems with individual user rate constraints

We propose a continuous rate and power allocation algorithm for multiuser downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with coordinated multipoint (CoMP) transmission that guarantees to satisfy individual rate target across all users. The optimization problem is formulated as a total transmit power minimization problem subject to per-user rate targets and per-antenna power constraints across multiple cooperating base stations. While the per-antenna power constraint leads to a more complex optimization problem, it is a practical consideration that limits the average transmit antenna power and helps to control the resulting high peak powers in OFDM. Our proposed algorithm uses successive convex approximation (SCA) to transform the non-convex power minimization problem and dynamically allocate power to co-channel user terminals. We prove that the transformed power minimization problem is convex and that our proposed SCA algorithm converges to a solution. The proposed algorithm is compared with two alternative approaches: (1) iterative waterfilling (IWF) and (2) zero-forcing beamforming (ZFB) with semi-orthogonal user selection. Simulation results highlight that the SCA algorithm outperforms IWF and ZFB in both medium- and low-interference environments.

Joint user selection and rate adaptation scheme in a coordinated multipoint transmission system for power minimization

In a coordinated multipoint transmission system with centralized architecture for saving power consumption, total power metric is minimized while completely using the backhaul capacity and maintaining the minimum target data rate. The problem is formulated as a mixed integer optimization problem, which is difficult to solve. To overcome this problem, a joint user selection and rate adaptation scheme is developed based on the water-filling rate adaptation with the given user set and the power saving criterion with the allocated rates. Numerical results demonstrate that compared with the norm-based and semi-orthogonal user selection algorithms, the proposed algorithm can significantly reduce the total power consumption. The proposed algorithm can also achieve near-optimal performance compared with the performance achieved by the exhaustive search-based method. In addition, the computational complexity of the proposed algorithm is reduced by heuristic iteration and search scope shrinking.