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

In the multiuser multiple-input multiple-output (MU-MIMO) system, to reduce the influence of channel correlation on system performance, the base station (BS) should select the appropriate subset of user equipments (UEs) according to their channel state information (CSI). Due to a lack of channel reciprocity, the downlink CSI needs to be fed back to the BS in frequency division duplexing (FDD) mode. Some scholars have exploited kinds of deep neural networks (DNNs) for sensing and recovering CSI. However, user selection after all the CSI is reconstructed by DNNs will bring a great time delay. In this paper, we propose a deep learning-based CSI feedback scheme called US-CsiNet. Based on adversarial autoencoder (AAE), US-CsiNet can explicitly cover user schedule information while representing CSI. At the UE side, the encoder of US-CsiNet maps the CSI into codewords of which part are feature information for user schedule. Then the BS applies these partial codewords to separate the UEs into different groups and select active UEs. Finally, the decoder of AAE reconstructs the CSI of these active UEs. US-CsiNet can not only simplify the user selection process but also guarantee the accuracy of CSI reconstruction. The simulation results show that the proposed approach outperforms maximum channel gain (MCG) user selection algorithms and achieves the nearly same performance with semiorthogonal user selection (SUS) which needs full CSI of all users at the BS.

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. However, the calculation amount of the optimal algorithm is too large to implement. 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. Simulation results show that the proposed user selection algorithm achieves higher sum rate capacity than the SUS (Semiorthogonal User Selection) algorithm. As the result of a discussion on the calculation complexity, 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.

This paper investigates the zero-forcing (ZF) beamforming transmit strategy in the multiple-antenna multiuser downlink systems. We consider the case of mobile users equipped with multiple antennas. Although the capacity of such systems can be achieved by dirty paper coding (DPC), DPC is extremely difficult and challenging to implement. Thus, simple but suboptimal linear beamforming techniques like ZF beamforming can be deployed. However, the number of users that can be served using this strategy is limited by the number of transmit antennas at the base station. The solution for this limitation is user selection (scheduling), which also exploits multiuser diversity. Therefore, user selection can be used to enhance the throughput of the system. Recently, it has been shown that ZF beamforming strategy with user selection is asymptotically optimal for a large number of users. In this paper, a semi-orthogonal user selection (SUS) algorithm is extended to the system with multiple-antenna mobile users. This algorithm aims to select users, which are semi-orthogonal. The optimal ZF beamforming matrices are obtained and it is shown that the optimal ZF beamforming throughput is related to the eigenvalues of the user channels. Therefore, SUS is aimed to select the proper group of users based on maximizing the channel eigenvalues and therefore improving the optimal ZF beamforming throughput of the system.

This paper focuses on the design of user selection algorithm for Unitary Beamforming (UBF) in multi-user multiple-input multiple-output (MIMO) systems. A long step user selection (LSUS) algorithm is proposed, and is verified to be rather robust when combined with the Givens rotation-based iterative optimization UBF. For most of existing user selection algorithms, only the best user is chosen at each selection, we regard the step length as one. However, because members of the optimal user set must both have high channel gains and low inter-user interference, if only one user is considered each step, the user that will be selected at next step can not be guaranteed to have both high channel gain and low inter-user interference with the just selected users. Therefore, an algorithm that two users are considered at each selection is proposed in this paper, the step length is regarded as two. It will in some extent help find the favorable user set and finally lead to a higher capacity. Moreover, Gram-Schmidt orthogonalization is used in our proposed algorithm to select the favorable users and at the same time generate the initial unitary beamforming matrix according to the channel state information (CSI). Two other beamforming schemes are simulated as references to demonstrate the good performance of the proposed algorithm even under low signal-to-noise-ratio (SNR) scenario.

WiFi 5/6 relies on a key feature, Multi-User Multiple-In-Multiple-Out (MU-MIMO), to offer high-volume network throughput and spectrum efficiency. MU-MIMO uses a user selection algorithm, based on each user's channel state information (CSI), to schedule transmission opportunities for a group of users to maximize the service quality and efficiency. In this paper, we discover that such algorithm creates a subtle attack surface for attackers to subvert user selection in MU-MIMO, causing severe disruptions in today's wireless networks. We develop a system, named MU-MIMO user selection strategy inference and subversion (MUSTER), to systematically study the attack strategies and further to seek efficient mitigation. MUSTER is designed to include two major modules: (i) strategy inference, which leverages a new neural group-learning strategy named MC-grouping via combining Recurrent Neural Network (RNN) and Monte Carlo Tree Search (MCTS) to reverseengineer a user selection algorithm, and (ii) user selection subversion, which proactively fabricates CSI to manipulate user selection results for disruption. Experimental evaluation shows that MUSTER achieves a high accuracy rate around 98.6% in user selection prediction and effectively launches the attacks to disrupt the network performance. Finally, we create a Reciprocal Consistency Checking technique to defend against the proposed attacks to secure MU-MIMO user selection.

Background: Cell Free massive MIMO, containing a very large number of distributed access points (APs), which is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell-free M-MIMO is maximum when selecting optimal access points (AP) from the large number of APs. The linear precoding methods of zeroforcing (ZF) and minimum mean square error (MMSE) are utilized in this study because they are devoid of self-interference and so improve the system capacity. Objective: The objective of this study is to maximize the system data rate in a cell-free M-MIMO network. Method: To maximize the system data rate, the maximum channel gain-based Access Point Selection (MCGAPS), Distance based Access Point Selection (DAPS), and Random-Access Point Selection (RAPS) algorithms are used to pick access points (APs) in a cell-free M-MIMO network. Because the MCGAPS algorithm selects those APs with the highest channel gain, the system’s rate is improved. Result & Discussion: The DAPS algorithm is used to choose the closest APs to the user. The APs were randomly chosen using RAPS. Random user selection (RUS) algorithm schedules the same number of users. Conclusion: It is observed that the DAPS and RUS algorithms jointly improve the system rate significantly in cell-free massive MIMO system compared to the other proposed algorithms.

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.

In downlink multiuser multiple-input multiple-output (MIMO) systems, system performance highly depends on the reliability of downlink channel state information (CSI) at the base station (BS). In frequency division duplexing, the most practical solution is to have downlink CSI from the users fed back to the BS. Most work on this feedback design has assumed independent block fading channels. However, this paper proposes a new differential feedback scheme using the observation that the channel realizations are usually temporally correlated. The sum-rate loss assuming differential feedback is analyzed for a system with the number of users K equal to the number of transmit antennas M. When K >; M, a user selection algorithm based on an approximated signal-to-interference plus noise ratio (SINR) estimation is proposed. In simulation results, the proposed differential feedback scheme increases the sum-rate compared to previous differential feedback schemes. Moreover, the proposed user selection algorithm outperforms semi-orthogonal user selection in the moderate signal-to-noise ratio (SNR) region, despite requiring less feedback information. In low mobility channels, utilizing the channels' time correlation during quantization is shown to play a bigger role in determining sum-rate performance than multiuser diversity for most SNR regimes when a practical number of users is considered.

In this paper, we propose a new low-complexity user selection algorithm for multi-user downlink channels. We only consider the scenario that linear beamforming, such as zero-forcing beamforming, is employed at the base station (BS). In this scenario, the number of concurrent active users is limited by the number of transmit antennas at the BS. Thus, there arises the problem how to select the optimal set of active users that can maximize the downlink channel capacity based on the channel state information (CSI) of all users. However, the optimal solution to this problem is an exhaustive search of all possible combinations of the users, which has prohibitive computational complexity. We propose a sub-optimal greedy user selection algorithm based on the norm and correlation between the channel matrices of different users. Our new algorithm requires low computational complexity and provides nearly optimal performance. Simulation results are presented to illustrate the good performance of our algorithm.

In this paper, a user selection problem for multiuser multiple-input multiple-output systems in the general wireless environment with full-rank and rank-deficient channel users is investigated. It is demonstrated that the maximum number of simultaneously supportable users is elastic, and the spatial resources occupied by each user can be squeezed in the rank-deficient environment. Therefore, a user selection algorithm is then proposed with relatively low computational overhead. The data rate requirement distinctions of different services are considered in the user selection criterion, and the resources occupied by real-time service users are primarily squeezed since they do not need extremely high data rate. Numerical results show that the effective throughput and the number of selected users achieved by the proposed user selection algorithm are larger than that of exiting researches.

The objective of this paper is to study the performance of a multicell vector perturbation (MVP) precoding technique under practical situations in a network multiple-input multiple-output (MIMO) scheme employing joint transmission. The conventional perturbation strategy that minimizes the total power is considered, and the power at each base station (BS) is properly scaled to enforce per-BS power constraints. In our scenario, we consider multiple-antenna users and use block diagonalization (BD) as the linear front-end precoder for VP. The sum rate for the MVP in the case of uniformly distributed input and an asymptotic upper bound on the sum rate at high signal-to-noise ratios (SNRs) are derived. In addition, using the asymptotic upper bound on the individual user rates, we propose a proportionally fair (PF) user scheduling algorithm of lower complexity and better performance compared with the benchmark fair semiorthogonal user selection (SUS) algorithm. As opposed to the PF-SUS, the proposed PF scheduling algorithm requires no predefined correlation threshold. Furthermore, we study the impact of backhaul delay on the performance of both VP and BD by deriving bounds on the sum rate. The numerical results show that MVP in the case of perfect channel state information (CSI) outperforms multicell BD. In the presence of a backhaul delay, the performance of MVP significantly degrades, but the upper bound on the sum rate for MVP is still higher than for multicell BD.

IEEE 802.11ac is a next generation wireless local area network (WLAN) standard employing multi-user multiple-input multiple-output (MU-MIMO) scheme for the first time. Since precoding and user selection schemes influence the performance of MU-MIMO transmission, accurate channel state information (CSI) is required to select users and obtain precoding matrix. In this paper, we evaluate the performance of linear precoding schemes such as channel inversion (CI) and block diagonalization (BD) with limited CSI by using compressed feedback. We also describe the greedy user selection metrics and linear precoding schemes of low complexity. Our results compare the achievable sum-rate between the user selection metrics and provide the packet error rate (PER) performance under the realistic channel models.

In this paper, linear precoding (LP) algorithm has been studied to improve the bit error rate (BER) performance of large scale multiple input multiple output (MIMO) system. A new technique of enhancing the BER performance of linear precoding algorithm without complexity in hardware and mathematical calculations is proposed and it is used on a traditional LP algorithms like a Maximum Ratio Transmission (MRT), a Zero-Forcing (ZF), a Regularized Zero-Forcing (RZF), and a Minimum Mean Square Error (MMSE) for Millimeter wave MU-MIMO communication system. A new technique is also used to mitigate the BER gab problem between (ZF, MMSE) and (RZF, MMSE) linear precoding algorithms. The result shows that with using the new technique, the BER performance of modified LP significantly outperform their traditional LP by 10−5 when using 4 element of BS antenna, 10−6 when using 16 element of BS antenna and mitigate the BER gab problem at different signal to noise ratio (SNR).

In this paper we present a low-complexity user selection algorithm for the downlink of a distributed antenna system (DAS) that achieves an optimum solution for a weighted matching problem. The user selection process is modeled as a linear sum assignment problem (LSAP). The proposed solution consists of two phases. In the first phase, a set of potential users to be scheduled is found by combining two complementary approaches: greedy and minimum-throughput-loss selection. In the second phase, the set of scheduled users is refined by selecting the users that maximize sum throughput. We provide numerical results to confirm the optimality of our user selection algorithm and to compare its performance with existing solutions.

Channel feedback for multi-user (MU)-multiple-input multiple-output (MIMO) has been widely studied and some results have been got with random vector quantization scheme. However, while the low rate fixed codebook feedbacks are adopted, the performance of zero forcing (ZF) MU-MIMO will decrease as the unpredictable inter-user interference is introduced because of quantized channel state information (CSI). To decrease inter-user interference in low rate fixed codebook feedback, an enhanced user selection switch (USS) feedback scheme for ZF MU-MIMO is proposed in this article. In USS feedback, the extra USS information is added after quantized CSI and received signal-to-noise ratio feedback. The USS information indicates inter-user interference and it can be used in user selection procedure to avoid large inter-user interference. Simulation results show that the proposed USS feedback scheme is efficient to solve the problems of unpredictable inter-user interference in conventional feedback scheme with low rate codebook in ZF MU-MIMO.