The application of compressed sensing (CS) theory to non-orthogonal multiple access (NOMA) systems has been investigated recently. As described in this paper, we propose a quality-of-service (QoS)-aware, low-complexity, CS-based user selection and power allocation scheme with adaptive resource block selection for downlink NOMA systems, where the tolerable interference threshold is designed mathematically to achieve a given QoS requirement by being relaxed to a constrained l1 norm optimization problem. The proposed scheme adopts two adaptive resource block (RB) selection algorithms that assign proper RB to user pairs, i.e. max-min channel assignment and two-step opportunistic channel assignment. Simulation results show that the proposed scheme is more effective at improving the user rate than other reference schemes while reducing the required complexity. The QoS requirement is approximately satisfied as long as the required QoS value is feasible.

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.

In this paper, we propose a novel user selection scheme based on jointly combining channel gain (CG) and signal to interference plus noise ratio (SINR) to improve the sum-rate as well as to reduce the computation complexity of multi-user massive multi-input multi-output (MU-massive MIMO) downlink transmission through a block diagonalization (BD) precoding technique. By jointly considering CG and SINR based user sets, sum-rate performance improvement can be achieved by selecting higher gain users with better SINR conditions as well as by eliminating the users who cause low sum-rate in the system. Through this approach, the number of possible outcomes for the user selection scheme can be reduced by counting the common users for every pair of user combinations in the selection process since the common users of CG-based and SINR-based sets possess both higher channel gains and better SINR conditions. The common users set offers not only sum-rate performance improvements but also computation complexity reduction in the proposed scheme. It is shown by means of computer simulation experiments that the proposed scheme can increase the sum-rate with lower computation complexity for various numbers of users as compared to conventional schemes requiring the same or less computational complexity.

Multiuser massive multi-input multi-output (MU massive MIMO) is considered as a promising technology for the fifth generation (5G) of the wireless communication system. In this paper, we propose a low-complexity joint antenna and user selection scheme with block diagonalization (BD) precoding for MU massive MIMO downlink channel in the time division duplex (TDD) system. The base station (BS) is equipped with a large-scale transmit antenna array while each user is using the single receive antenna in the system. To reduce the hardware cost, BS will be implemented by limited number of radio frequency (RF) chains and BS must activate some selected transmit antennas in the BS side for data transmitting and some users' receive antennas in user side for data receiving. To achieve the reduction in the computation complexity in the antenna and user selection while maintaining the same or higher sum-rate in the system, the proposed scheme relies on three complexity reduction key factors. The first key factor is that finding the average channel gains for the transmit antenna in the BS side and the receive antenna in the user side to select the best channel gain antennas and users. The second key factor called the complexity control factor ξ(Xi) for the antenna set and the user set limitation is used to control the complexity of the brute force search. The third one is that using the assumption of the point-to-point deterministic MIMO channel model to avoid the singular value decomposition (SVD) computation in the brute force search. We show that the proposed scheme offers enormous reduction in the computation complexity while ensuring the acceptable performance in terms of total system sum-rate compared with optimal and other conventional schemes.

We consider user selection schemes for multi-user MIMO systems with linear precoding. In this work, we apply two user selection schemes based on the orthogonality between the propagation channel of MSs. Indoor transmission experiments are carried out under several scenarios and the performances of user selection schemes are evaluated. It is shown that the transmission performance is improved and the user selection schemes are remarkably affected by the path loss between MSs.

In a multi-user multiple-input multiple-output (MU-MIMO) system that adopts zero-forcing (ZF) as a precoder, the best selection is the combination of users who provide the smallest trace of the inverse of the channel auto-correlation matrix. Noting that the trace of the matrix is closely related to the determinant, we search for users that yield the largest determinant of their channel auto-correlation matrix. The proposed technique utilizes the determinant row-exchange criterion (DREC) for computing the determinant-changing ratio, which is generated whenever a user is replaced by one of a group of pre-selected users. Based on the ratio computed by the DREC, the combination of users providing the largest changing ratio is selected. In order to identify the optimal combination, the DREC procedure is repeated until user replacement provides no increase in the determinant. Through computer simulations of four transmit antennas, we show that the bit error rate (BER) per signal-to-noise ratio (SNR) as well as the sum-rate performance provided by the proposed method is comparable to that of the full search method. Furthermore, using the proposed method, a partial replacement of users can be performed easily with a new user who provides the largest determinant.

This paper proposes an opportunistic feedback and user selection method for a multiuser two-way relay channel (MU-TWRC) in a time-varying environments where a base station (BS) and a selected mobile station (MS), one of K moving MSs, exchange messages during two time slots via an amplify-and-forward relay station. Specifically, under the assumption of perfect channel reciprocity, we analyze the outage probabilities of several channel feedback scenarios, including the proposed scheme. Based on the analysis, the transmission rates are optimized and the optimal user selection method is proposed to maximize the expected sum throughput. The simulation results indicate that, with opportunistic feedback, the performance can be significantly improved compared to that without feedback. Moreover, the performance is nearly identical to that with full feedback, and close to the case of perfect channel state information at BS for low mobility MSs.

The conventional semi-orthogonal user pairing algorithm in uplink virtual MIMO systems can be used to improve the total system throughput but it usually fails to maintain good throughput performance for users experiencing relatively poor channel conditions. A novel user paring algorithm is presented in this paper to solve this fairness issue. Based on our analysis of the MMSE receiver, a new criterion called “inverse selection” is proposed for use in conjunction with the semi-orthogonal user selection. Simulation results show that the proposed algorithm can significantly improve the throughput of users with poor channel condition at only a small reduction of the overall throughput.

This letter presents a novel user scheduling algorithm that provides a maximum sum-rate based on zero-forcing beamforming (ZFBF) in multiple-input multiple-output (MIMO) systems. The proposed technique determines primary user pairs in which the sum-rate exceeds a predetermined threshold. To determine the threshold, we define the maximum-sum-rate criterion (MSRC) derived from the extreme value theory (EVT). Applying the MSRC in ZFBF-based user scheduling, we find that the performance of the proposed method is comparable to that of the exhaustive searching scheme which has a greater computational load. Through computer simulations, we show that the proposed method outperforms the very well-known correlation-based method, semi-orthogonal user selection (SUS), yielding a sum rate that is about 0.57 bps/Hz higher when the transmit SNR is 10 dB with perfect CSI at BS and the numbers of users and transmit antennas in a cell are 100 and 4, respectively.

In this letter, we investigate the Nth-best user selection scheme for amplify-and-forward cooperative systems over Rayleigh fading channels. We deduce the probability density function, the cumulative density function, and the moment generating function of the end-to-end signal-to-noise ratio of the system. Then, the respective closed-form expressions of the average symbol error probability and the outage probability at the destination are derived. The diversity order obtained in the scheme increases with user number but becomes less as the selection sequence number N increases. Simulation results verify the analytical results.

Multiuser -- Multiple Input Multiple Output (MU-MIMO) techniques were proposed to increase spectrum efficiency; a key assumption was that the Mobile Terminals (MTs) were simple with only a few antennas. This paper focuses on the Block Diagonalization algorithm (BD) based on the equal power allocation strategy as a practical MU-MIMO technique. When there are many MTs inside the service area of the access point (AP), the AP must determine, at each time slot, the subset of the MTs to be spatially multiplexed. Since the transmission performance depends on the subsets of MTs, the user selection method needs to use the Channel State Information (CSI) obtained in the physical layer to maximize the Achievable Transmission Rate (ATR). In this paper, we clarify the relationship between ATR with SU-MIMO and that with MU-MIMO in a high eigenvalue channel. Based on the derived relationship, we propose a new measure for user selection. The new measure, the eigenvalue decay factor, represents the degradation of the eigenvalues in null space compared to those in SU-MIMO; it is obtained from the signal space vectors of the MTs. A user selection method based on the proposed measure identifies the combination of MTs that yields the highest ATR; our approach also reduces the computational load of user selection. We evaluate the effectiveness of user selection with the new measure using numerical formulations and computer simulations.

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.

We investigate the MIMO broadcast channels with imperfect channel knowledge due to estimation error and much more users than transmit antennas to exploit multiuser diversity. The channel estimation error causes the interference among users, resulting in the sum-rate loss. A tight upper bound of this sum-rate loss based on zeroforcing beamforming is derived theoretically. This bound only depends on the channel estimation quality and transmit antenna number, but not on the user number. Based on this upper bound, we show this system maintains full multiuser diversity, and always benefits from the increasing transmit power.

In this letter, we propose a limited feedback precoding scheme based upon grassmannian beamforming and user selection for downlink multiuser MIMO systems. Conventional random beamforming scheme only enjoys significant performance gains with a large number of users, which limits its practical application. With proper codebook size the proposed scheme outperforms conventional random beamforming scheme when the number of users is small or moderate.

In this letter, we propose an efficient user selection algorithm aiming to select users with less spatially correlation and meet the user number limit of zero-forcing beamforming in downlink multiuser MIMO systems. This algorithm yields a considerable complexity reduction with only a small loss in performance and it only needs partial users' CSI feedback. Coupled with the algorithm, a null space updating method in O(K2) time and a modified proportional fair scheduling algorithm are also proposed.