User Selection Method Research Articles

A power method based user selection for reconfigurable intelligent surface (RIS) assisted multiuser massive MISO systems

Reconfigurable intelligent surfaces (RISs) have recently emerged as effective solutions for providing massive connectivity. However, an effective RIS user selection method that can support massive devices remains undeveloped. In this study, a power method (PM)-based RIS user selection algorithm for RIS-assisted massive multiple-input multiple-output systems is proposed. It is proven that minimizing the condition number (CN) of the aggregated downlink channel matrix results in the same performance as maximizing the sum rate. Based on this analysis, a PM-based RIS user selection algorithm that utilizes a CN as an RIS user selection metric is proposed here. Although the complexity of conventional user selection algorithm is proportional to the square of the number of base station (BS) antennas ( M 2 ), that of the proposed algorithm is linearly proportional to the number of BS antennas ( M ). Simulation results demonstrate that, under a practical RIS control link, the proposed PMUS algorithm achieves better performance and lower complexity than the conventional user selection algorithm.

Performance analysis of NOMA enabled multi-user co-operative IoT network with SWIPT protocol

This study investigates the performance of user equipments (UEs) and IoT devices in a multi-user (MU) multiple-input single-output (MISO) configuration based non-orthogonal multiple-access (NOMA) system. The proposed model includes a base station surrounded by many users and a number of dispersed IoT devices. In the considered MU IoT network scenario, power allocation coefficients are set to ensure that both the relaying UE and the targeted IoT device experience increased performance. To assist the intended IoT device with weak channel conditions, an optimal user selection method is developed that picks a UE with comparatively better reception, and thereby the selected UE acts as relay forward node utilizing the simultaneous wireless information and power transfer protocol. The characterization of a key performance metric, i.e., the outage probability is derived by adopting a two-step procedure. First, quadratic forms of signal to interference plus noise ratio (SINR) at the relaying UE and the signal-to-noise ratio (SNR) and SINR at the intended IoT device are constructed. Then, using the indefinite quadratic form approach, we arrive at closed-form expressions for the outage probability (OP) of intended IoT device under linearly dependent beamformers. To validate the analytical expressions provided in this study, simulations are performed. We demonstrate that by utilizing the proposed user selection technique in MU-MISO-NOMA system decreases the OP of the intended IoT device by 40% on average. Furthermore, utilizing multiple transmit antenna elements decreases the OP of the intended IoT device by approximately 10%.

Trusted user selection for fusion of multimodal cognition in self-organizing pattern of mobile crowdsensing

Mobile Crowd Sensing is mainly aimed at registered users of the sensing platform, which inevitably has the problem of insufficient users. In addition, the existing methods mostly use single-mode data for user quality assessment. Aiming at the existing limitations, this paper proposes a trusted user selection method that integrates multimodal cognition under the self-organization pattern. Firstly, recruit users based on the group effect and center effect in social networks. Then, Convolutional Neural Network and Doc2Vec are used to extract multimodal information features and calculate the user's task achievement degree. Finally, establish the reputation similarity network by considering the interaction between users under the self-organization pattern, and select trusted users. By selecting high-quality trusted users, we can save platform expenditure and improve sensing quality. Experiments were conducted on the simulation dataset. Under the best conditions, the reputation value of the selected sensing users increased by 12%, the user utility increased by 14%, and the data quality increased by 28%.

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.

Energy efficient power allocation with joint antenna and user selection in massive MIMO systems

The problem of maximizing energy efficiency (EE) is considered in this paper. The system in question is a massive multiple-input multiple-output (MIMO) system that transmits information simultaneously in the uplink and downlink. To maximize EE, an optimal antenna selection strategy is used to send information. Furthermore, we propose an optimal user selection method and then obtain their optimal allocated power. The optimization problem of the system has two constraints, including power limitations in the uplink and downlink. Due to the non-convexity and being constrained of the optimization problem, in order to obtain the optimal power of users, it must first be converted to a convex problem form and then turned into an unconstrained problem using the Lagrange dual function method. The bisection algorithm is utilized to select the optimal set of active antennas for sending information. The cross-entropy algorithm with the proposed algorithm is used simultaneously to select the set of users and determine their optimal power values. The results of computer simulations show that the proposed selection schemes of antennas and users significantly improve the system performance.

Adaptive Power Allocation for Wireless-Powered FD-NOMA System With Cooperation Versus Non-Cooperation

The paper studies a wireless-powered full-duplex non-orthogonal multiple access (NOMA) system with one base station and two groups of near and far users, where the near users can be regarded as the self-energy recycling relays to assist the far users. In particular, the locations of the near and far users are modeled as the uniformly distributed and homogeneous Poisson Point Process respectively. From the beneficial perspective of the near users, we propose a novel power allocation scheme, which can utilize the partial channel state information to switch the transmission mode dynamically between cooperative NOMA and non-cooperative NOMA. Further, since the users’ locations have a significant influence on our proposed power allocation scheme, three different user selection strategies namely random near user and random far user (RNRF), optimal near user and nearest far user (ONNF), and optimal near user and farthest far user (ONFF) are investigated to carry out NOMA pairing. Firstly, the RNRF allows all of users to communicate with the base station by a fair opportunity compared to the others strategies, and then a two-stage user selection method is proposed by considering the actual states of near users to determine an optimal near user who can maximize the outage performance of far user for ONNF and ONFF strategies. In addition, outage probabilities, diversity orders, and system throughputs are derived to characterize the performance of three strategies. Finally, numerical results are shown to verify that not only our proposed power allocation scheme can improve the quality of service of near users but also user selection strategies can fully exploit multi-user diversity in comparison to the existing work.

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.

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.

Multiuser Selection Criteria for MIMO-NOMA Systems With Different Detectors

Non-orthogonal multiple access (NOMA) is an effective way to improve the spectrum efficiency of multiple-input multiple-output (MIMO) systems, where this advancement can be fully exploited by employing a proper user selection strategy in the multiuser MIMO system. However, for multiuser scenarios, information-theoretic throughput may not be properly used as the performance indicator since the constraints at the receiver (e.g., suboptimal detectors) are not taken into consideration. In this paper, we adopt the error probability (EP) as the performance metric, which is equivalent to the practical throughput efficiency and can take the critical constraints into consideration. We analyze the pair-wise error probability (PEP) performance of different detectors and propose two kinds of user selection methods to minimize the PEP. To facilitate the detection performance evaluation, we further investigate the multiuser diversity of the proposed selection methods and derive a tight lower PEP bound for the case with randomly selected users. From the analytical and numerical results, we demonstrate that the proposed multiuser selection methods can suppress the inter-user interference effectively and achieve better performance than that of the state-of-the-art method.

Low Complexity User Selection and Power Allocation for Uplink NOMA Beamforming Systems

In this paper, we develop user selection and power allocation methods for NOMA systems equipped with multi-antenna to enhance the sum capacity of the uplink. The system has the base station (BS) with $$ N $$ antenna supporting 2N users in the same spectrum resource simultaneously, and successive interference cancellation (SIC) is applied at the BS. Because the superposition of multiple users in transmission within the same frequency resource block leads to the interference among users, we derive a user set selection algorithm and a suboptimal power control to mitigate the interference effect and to maximize the sum capacity. The user set selection algorithm is first applied by comparing the designed indicator which balances the factors affecting the sum capacity of the uplink. Second, the derived suboptimal power assignment algorithm is utilized with power control factors where four cases are evaluated. The simulation results show that the proposed schemes can significantly improve the sum capacity than the existing methods.

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.

On Secure QoS-based NOMA Networks with Multiple Antennas and Eavesdroppers over Nakagami-m Fading

In this paper, we study the physical layer security of a downlink multi-input single-output non-orthogonal multiple access (NOMA) system, in which transmit antenna selection (TAS) scheme, users’ quality of service (QoS) requirements, Nakagami-m fading channel, and user selection solution are considered for secure NOMA transmission. Specifically, we investigate the scenario that a base station (BS) (source) communicates with two legitimate user (LU) clusters with different priority depending on their QoS requirements in the presence of multiple passive eavesdroppers over Nakagami-m fading. To enhance the secrecy performance, we examine a TAS scheme and a user selection method. In this context, prior to transmission, a transmit antenna and a user in high-priority LU cluster are jointly selected to maximize the channel quality of the link from the BS to the selected user. Furthermore, together with the selected high-priority user, a user in low-priority LU cluster, which has the best channel condition, is chosen for performing two-user NOMA. In order to evaluate the secrecy performance, tight and asymptotic closed-form expressions of secrecy outage probability for selected LUs and overall system are derived. Our theoretical results confirmed by Monte–Carlo simulation show that in QoS-based NOMA system, high-priority users cause the higher effects on the overall secrecy performance in comparison with low-priority users. Finally, these results also indicate that our proposed communication protocol achieves the secrecy performance greater than conventional protocol.

Deep Reinforcement Learning Based Intelligent User Selection in Massive MIMO Underlay Cognitive Radios

Cognitive radio (CR) and massive multiple-input multiple-output (MIMO) have attracted much interest recently due to the amazing ability to accommodate more users and improve spectrum utilization. This paper investigates the QoS-aware user selection approach for massive MIMO underlay cognitive radio. Two main CR scenarios are considered: 1) the channel state information (CSI) of the cross channels are available at the secondary base station (SBS), and 2) any CSI of cross-network is unavailable at SBS. For the former, we develop the low-complexity increase-user-with-minimum-power algorithm (IUMP) and decrease-user-with-maximum-power algorithm (DUMP) which both can address the problem of user selection with power allocation. However, the CSI is typically not available in practice. To address the intractable issue, we propose a deep reinforcement learning-based approach, which can enable the SBS to realize efficient and intelligent user selection. The simulation results show that the IUMP and DUMP algorithms have obvious performance advantages over traditional user selection methods. In addition, results also verify that our constructed neural network can efficiently learn the optimal user selection policy in the unknown dynamic environment with fast convergence and high success rate.

User Selection and Multiuser Widely Linear Precoding for One-Dimensional Signalling

Emergence of ultradense networks in 5G communications, Internet of things, and eHealth devices prompts us to develop new communication techniques that can support a large number of low data rate devices. In particular, it has been shown that when the data are real-valued and the observation is complex-valued, widely linear (WL) estimation can be employed in lieu of linear estimation to improve the performance. With these motivations, we study user selection and transmit precoding in multiuser communication systems assuming transmitted signals are one-dimensionally modulated. A closed-form solution for widely linear maximum signal-to-leakage-and-noise ratio precoding is obtained. We also investigate the design of WL maximum ratio transmission, WL zero-forcing, and WL minimum mean square error precoding techniques. Furthermore, to enable an increased number of communication devices, a user selection algorithm compatible with widely linear processing of one-dimensionally modulated signals is proposed. The proposed user selection algorithm can potentially double the number of simultaneously selected users compared to that of conventional user selection methods.

Low‐complexity user selection method in 3D MU‐MIMO systems

The user selection method based on chordal distance is widely used in multiuser multiple-input multiple-output (MU-MIMO) systems with block diagonalisation. It has throughput close to the capacity based method with low complexity. In this Letter, a user selection method with higher throughput and much lower complexity is proposed. To investigate the performance in a more practical channel scenario, 3D MIMO channels are used in simulations. Compared with existing user selection algorithms, the proposed method achieves good throughput performance and the lowest complexity order.

Joint Fronthaul Link Selection and Transmit Precoding for Energy Efficiency Maximization of Multiuser MIMO-Aided Distributed Antenna Systems

We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency (EE) of a multiuser multiple-input multiple-output-aided distributed antenna system. The fronthaul link’s power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints, some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the EE optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Second, a triple-layer iterative algorithm is proposed for solving the approximated EE optimization problem, which is proved to converge to the Karush–Kuhn–Tucker conditions of the smoothened EE optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms are capable of achieving an order of magnitude higher EE than its conventional counterpart operating without considering link selection.

Modulation-Specific Multiuser Transmit Precoding and User Selection for One-Dimensional Signaling

Massive deployment of low data rate Internet of things and eHealth devices that require high reliability motivates the development of practical precoding and user selection techniques. In this paper, we show that throughput and communications reliability can be improved by incorporating knowledge of modulation type in the design of the multiuser transmit precoder. The transmission of low data rate one-dimensionally modulated signals in a broadcast channel is considered. The transmit precoding matrix is determined by minimizing the weighted sum of error probabilities of users. Although the proposed minimum probability of error (MPE) precoding problem is nonconvex and highly nonlinear, it is solved by the alternating minimization of two convex subproblems. A reduced-complexity version of convex MPE precoding is then introduced, which exponentially reduces the complexity of the problem. Numerical results show that the proposed precoding techniques significantly improve system performance in broadcast channels. A user selection algorithm, compatible with MPE precoding, is also proposed that selects users if their error probabilities can approach zero. Based on line packing principles in Grassmannian manifolds, it is shown that the number of selected users could potentially be more than the number of transmit antennas, which translates to supporting more simultaneous users in the shared channel compared to existing user selection methods.

Pairing and Power Allocation for Downlink Nonorthogonal Multiple Access Systems

In this paper, a greedy-search-based user pairing and selection method as well as an optimal power allocation approach is proposed to maximize the weighted sum rate of a downlink nonorthogonal multiple access (NOMA) system. The NOMA system consists of one base station and multiple users. By exploiting superposition coding and successive interference cancellation techniques at the receiver, multiple users can be multiplexed into transmit power domain, and then nonorthogonally scheduled for transmission on the same spectrum resources. The proposed greedy-search-based user pairing method has low computational complexity and the power allocation method is shown to be equivalent to a weighted minimum mean square error based problem, which is solved using an iterative procedure. The subproblem in each step of the iteration has a closed-form solution. Also, the convergence of the iteration is proved. Simulation results show that the proposed method can significantly improve the sum rate of the system over the other methods.

Joint Spectrum Sensing and Resource Allocation Scheme in Cognitive Radio Networks with Spectrum Sensing Data Falsification Attack

Spectrum sensing and spectrum sharing are two fundamental issues in a cognitive radio network (CRN). The spectrum sensing data falsification (SSDF) attack imposes bad effects on both spectrum sensing and spectrum sharing. To deal with SSDF attacks and to incentivize secondary users (SUs) to behave well, a joint spectrum sensing and resource allocation (JSSRA) scheme in a CRN is proposed in this paper. The JSSRA problem is formulated as a weighted-proportional-fairness-based resource-allocation optimization problem under the constraint that the primary user (PU) network is sufficiently protected. The problem is decomposed into two subproblems, namely, the resource allocation subproblem and cooperative SU number decision subproblem, which are solved by the Lagrangian dual algorithm and the brute-force algorithm, respectively. Moreover, a user-selection method based on reinforcement learning is presented to select reliable SUs for cooperative spectrum sensing (CSS). In addition, the SU's trust degree is updated according to its behavior in CSS and is used in the sensed resource-allocation process. The key point of our proposed scheme is to make SUs improve the sensing reliability and prevent SUs from behaving maliciously by an incentive mechanism. Comprehensive performance evaluation is conducted by computer simulation. It is shown that the proposed JSSRA scheme deals with the SSDF attack well in cooperative sensing process to improve system robustness and achieves a significant system utility gain in resource allocation.

Decentralized transmit beamforming scheme with zero forcing and user selection for multicell system dynamical systems

Conventional distributed beamforming schemes in multicell system often ignore two practical conditions, (1) the coexistence of intra-cell interference (InCI) and inter-cell interference (ICI), (2) the number of users is larger than the number of base station (BS) antennas. In this paper, we consider these two conditions and analyze the decentralized beamforming of the multicell system from the viewpoint of zero forcing (ZF). A novel user selection method based on ZF algorithm is proposed to divide the users into two categories, i.e. one of the categories is eligible to participate in ZF algorithm and the other is not. The proposed criterion of partitioning users is based on the degree of parallelity between local channels. In addition, each BS utilizes only the local channel state informations (CSIs) to derive its beamforming vector. The simulation results demonstrate that our proposed algorithm can achieve higher rates and are more robust against InCI and ICI than the traditional ones under the different number of transmitting antennas and users.