Employing temporal channel correlation in user selection for MIMO broadcast systems

Taking the time varying nature of wireless channels into account, two user selection schemes with lower complexity are developed for multiple-input multiple-output broadcast （MIMO BC）systems. According to the relationship between coherence time and Doppler frequency, an information frame is divided into several segments. At the beginning of each segment, the user selection is carded out with the greedy selection algorithm. In the simplified user selection algorithms employing the temporal correlation （SUSTC）, the selection results are applied for all the remaining slots in each segment. But in the improved simplified user selection algorithms employing the temporal correlation（ISUSTC）, at the remaining slots, users are kept with favorable channel conditions selected at the previous slot, and other users are updated from the candidate pool to communicate simultaneously. Simulations show that compared with the greedy user selection method, the proposed algorithms can reduce the selection complexity with a little sum capacity loss.

Joint User Scheduling and Antenna Selection in Distributed Massive MIMO Systems with Limited Backhaul Capacity

Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station(BS).In a distributed massive MIMO system,the capacity of fiber backhaul that links base station and remote radio heads is usually limited,which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink.To solve this problem,we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity.Three sub-optimal iterative algorithms with the objective of sumrate maximization are proposed for the joint optimization of antenna selection and user scheduling,either based on greedy fashion or Frobenius-norm criteria.Convergence and complexity analysis are presented for the algorithms.The provided Monte Carlo simulations show that,one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.

USER SELECTION FOR SINR-BASED UPLINK MULTIUSER MIMO SYSTEMS

To avoid the exhaustive search, we propose a fast user selection algorithm for Signal-to-Interference-plus-Noise-Ratio (SINR)-based multiuser Multiple-Input Multiple-Output (MIMO) systems with Alamouti Space-Time Block Code (STBC) transmit scheme. A locally optimal selection criterion is proposed at first. Then, the incremental selection approach is applied, which selects one among the residual available users to maximize the minimum user SINR step by step. Simulation results show that the fast algorithm gains over 90% of the diversity benefit achieved by the exhaustive search selection, and that the fast algorithm has much lower computational burden than the exhaustive search one, for the scenario where the number of all the available users is much greater than that of the selected users.

Cooperative Anti-Interference Spectrum Sharing with Secondary User Selection

In this paper, we propose a cooperative anti-interference spectrum sharing strategy with secondary user selection where the secondary system can gain spectrum access along with the primary system. Specifically, secondary user and are selected to transmit the primary and secondary signal through different bandwidth in the second transmission slot which occupies fraction of the time. Thus, the primary and secondary systems will not interfere with each other. We study the joint optimization of time and bandwidth allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system to achieve its target rate. Simulation results confirm efficiency of the proposed spectrum sharing strategy, and the significant performance improvement of the cognitive system.

Joint User Selection and Resource Allocation for Fast Federated Edge Learning

By periodically aggregating local learning updates from edge users, federated edge learning (FEEL) is envisioned as a promising means to reap the benefit of local rich da?ta and protect users'privacy. However, the scarce wireless communication resource greatly limits the number of participated users and is regarded as the main bottleneck which hin?ders the development of FEEL. To tackle this issue, we propose a user selection policy based on data importance for FEEL system. In order to quantify the data importance of each user, we first analyze the relationship between the loss decay and the squared norm of gradi?ent. Then, we formulate a combinatorial optimization problem to maximize the learning effi?ciency by jointly considering user selection and communication resource allocation. By problem transformation and relaxation, the optimal user selection policy and resource alloca?tion are derived, and a polynomial-time optimal algorithm is developed. Finally, we deploy two commonly used deep neural network (DNN) models for simulation. The results validate that our proposed algorithm has strong generalization ability and can attain higher learning efficiency compared with other traditional algorithms.

Secure Transmissions in Wireless Multiuser Networks Using Message Correlation

Due to the openness of wireless multiuser networks,the private information transmitted in uplink or downlink is vulnerable to eavesdropping.Especially,when the downlink transmissions use nonorthogonal multiple access(NOMA)techniques,the system further encounters interior eavesdropping.In order to address these security problems,we study the secret communication in multiuser networks with both uplink and downlink transmissions.Specifically,in uplink transmissions,the private messages transmitted in each slot are correlated,so any loss of the private information at the eavesdropper will prevent the eavesdropper from decoding the private information in later time slots.In downlink transmissions,the messages are correlated to the uplink information.In this way,any unexpected users who lose the expected user’s uplink information cannot decode its downlink information.The intercept probability is used to measure security performance and we analyze it in theory.Finally,simulation results are provided to corroborate our theoretical analysis.

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(CSD).Low computational cost algorithms for user and an-tenna 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(ISF)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 0(1)for RUS and DOSUS,and 0(M^2N)for suS which are less than the other conventional user selection methods.

Optimal Energy Efficiency Resource Allocation Strategy for Cognitive Clustering Network under PUEA Attack

5G has pushed the use of radio spectrum to a new level,and cognitive clustering network can effectively improve the utilization of radio spectrum,which is a feasible way to solve the growing demand for wireless communications.However,cognitive clustering network is vulnerable to PUEA attack,which will lead to the degradation of system detection performance,thereby reducing the energy efficiency.Aiming at these problems,this paper investigates the optimal energy efficiency resource allocation scheme for cognitive clustering network under PUEA attack.A cooperative user selection algorithm based on selection factor is proposed to effectively resist PUEA user attack and improve detection performance.We construct the energy efficiency optimization problem under multi-constraint conditions and transform the nonlinear programming problem into parametric programming problem,which is solved by Lagrangian function and Karush-Kuhn-Tucker condition.Then the sub-gradient iterative algorithm based on optimal energy efficiency under PUEA attack is proposed and its complexity is analyzed.Simulation results indicate that proposed method is effective when subjected to PUEA attacks,and the impact of different parameters on energy efficiency is analyzed.

Lower bound capacity based scheduling in MU-MIMO system

The current existed scheduling schemes which are generally derived from the antenna selection algorithms can improve the sum-rate of MU-MIMO system to some extent.However,these schemes can not make the best of multi-user diversity gain,thus incurring capacity loss.To overcome these disadvantages,in this paper,a novel user scheduling scheme based on the lower bound capacity of MU-MIMO broadcast channel is proposed for the multi-user zero-forcing precoding system.In the proposed scheme,the base station will schedule those users that can achieve the maximal lower bound capacity of MU-MIMO system by certain optimal criterion.Simulation results are exhibited to indicate that the proposed scheme can provide performance improvement than other existed scheduling schemes.

User selection strategies for multiuser MIMO systems with block diagonalization

Block diagonalization （BD） is an efficient precoding technique that eliminates inter-user interference in downlink multiple-input multiple-output （MIMO） systems. User selection strategies applied to multiuser MIMO systems with BD are investigated in this article. To enhance the capacity of multiuser MIMO systems, an equivalent capacity maximum （ECM） user selection strategy is proposed with low computational complexity. Considering both the factors of channel correlations and channel conditions, the proposed strategy can select a group of users to serve for maximizing the total throughput. Simulation results indicate that, for various channel conditions, proposed ECM strategy gains a better performance compared with traditional user selection strategies, and achieves a near optimal throughput as the exhaustive search.

Efficient user selection for multi-cell multi-user MIMO systems with limited backhaul

Multi-cell multi-user multiple-input multiple-output （MC-MU-MIMO） is a promising technique to eliminate inter-user interference and inter-cell cochannel interference in wireless telecommunication systems. As the large number of users in the system and the limited number of simultaneously supportable users with MC-MU-MIMO, it is necessary to select a subset of users to maximize the total throughput. However, the fully centralized user selection algorithms used in single cell system, which will incur high complexity and backhaul load in multi-cell cooperative processing （MCP） systems, are not suitable to MC-MU-MIMO systems. This article presents a two cascaded user selection method for MCP systems with multi-cell block diagonalization. In this paper, a local optimal subset of users, which can maximize the local sum capacity, is first chosen by the greedy method in every cooperative base station in parallel. Then, all the cooperative base stations report their local optimal users to the central unit （CU）. Finally, the global optimal users, which can maximize the global sum capacity of MCP systems, are selected from the aggregated local optimal users at the CU. The simulation results show that the proposed method performs closely to the optimal and centralized algorithm. Meanwhile, the complexity and backhaul load are reduced dramatically.

User selection based on user-union and relative entropy in mobile crowdsensing

A critical issue in mobile crowdsensing(MCS) involves selecting appropriate users from a number of participants to guarantee the completion of a sensing task. Users may upload unnecessary data to the sensing platform, leading to redundancy and low user selection efficiency. Furthermore, using exact values to evaluate the quality of the user-union will further reduce selection accuracy when users form a union. This paper proposes a user selection method based on user-union and relative entropy in MCS. More specifically, a user-union matching scheme based on similarity calculation is constructed to achieve user-union and reduce data redundancy effectively. Then, considering the interval-valued influence, a user-union selection strategy with the lowest relative entropy is proposed. Extensive testing was conducted to investigate the impact of various parameters on user selection. The results obtained are encouraging and provide essential insights into the different aspects impacting the data redundancy and interval-valued estimation of MCS user selection.

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.

Joint User and Antenna Selection for Multiuser MIMO Downlink with Block Diagonalization

User selection is necessary for multiuser multiple-input multiple-output(MIMO) downlink systems with block diagonalization(BD) due to the limited free spatial transmit dimensions.The pure user selection algorithms can be improved by performing receive antenna selection(RAS) to increase sum rate.In this paper,a joint user and antenna selection algorithm,which performs user selection for sum rate maximization in the first stage and then performs antenna selection in the second stage,is proposed.The antenna selection process alternately drops one antenna with the poorest channel quality based on maximum determinant ranking(MDR) from the users selected during the first stage and activates one antenna with the maximum norm of projected channel from the remaining users.Simulation results show that the proposed algorithm significantly outperforms the algorithm only performing user selection as well as the algorithm combining user selection with MDR receive antenna selection in terms of sum rate.