User Grouping Problem Research Articles

User Grouping in Multiuser Satellite MIMO Downlink With Fairness Consideration

In this letter, a multi-user satellite MIMO downlink including a multi-beam GEO satellite and multiple user terminals (UTs) is considered. In order to make every UT get the service fairly, all UTs need to be divided into multiple groups so that UTs in the same group can be served simultaneously, and different groups can be served sequentially in different time slots. The user grouping problem is investigated in this letter with the objective of maximizing the average channel capacity per time slot, and a genetic based user grouping (GBUG) algorithm is proposed. Simulations show that the average channel capacity obtained by GBUG is larger than that of the existing algorithms in the ZF and MMSE precoding system.

Joint Power and User Grouping Optimization in Cell-Free Massive MIMO Systems

To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division duplex (TDD) are considered to serve users on different time resources and the same frequency resource. In addition, when quality of service (QoS) requirements are considered, widely-used max-min power control is no longer applicable. We derive the minimum power constraints under diverse QoS requirements considering user grouping. Based on the analysis, we formulate the joint power and user grouping problem under QoS constraints, aiming at minimizing the total transmit power. A generalized benders decomposition (GBD) based algorithm is proposed, where the primal problem and master problem are solved iteratively to approach the optimal solution. Simulation results demonstrate that by user grouping, the number of users served in cell-free MIMO systems can be as much as the number of APs without increasing the complexity of channel estimation and decoding. Furthermore, with the proposed user grouping strategy, the power consumption can be reduced by 2–3 dB compared with the reference user grouping strategy, and by 7 dB compared with the total transmit power without grouping.

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC.

Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme.

Resource Allocation in Wireless Powered IoT Networks

In this paper, efficient resource allocation for the uplink transmission of wireless powered IoT networks is investigated. We adopt LoRa technology as an example in the IoT network, but this work is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov Decision Process (MDP) is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.

Distributed Sequential Coalition Formation Algorithm for Spectrum Allocation in Underlay Cognitive Radio Networks

In this paper, we study the resource allocation problem, including the joint consideration of user grouping and power allocation, of an, underlay cognitive radio (CR) network employing non-orthogonal multiple access (NOMA), which is referred to as the NOMA-CR network. Based on the non-transferable utility coalition formation game theory, the so-called distributed sequential coalition formation (DSCF) algorithm is leveraged to solve the user grouping problem, where multiple cognitive users within a group can access the primary user's spectrum band while satisfying a minimum rate requirement of the primary user. The simulation results show that a near-optimal performance can be achieved by implementing the proposed DSCF as well as the optimal power allocation algorithms. Furthermore, the results validate that NOMA-CR can significantly outperform the traditional orthogonal multiple access CR, in terms of throughput. Finally, it is shown that by applying a novel adaptive trellis turbo coded modulation technique to the underlay NOMA-CR network, a significant throughput gain can be achieved.

Deep-Learning-Aided Cross-Layer Resource Allocation of OFDMA/NOMA Video Communication Systems

In previous study, deep learning and autoencoder have been applied for data detection of NOMA systems, rather than the resource allocation of OFDMA/NOMA systems. In previous work, we proposed the use of non-deep-learning-based cross-layer resource allocation for OFDMA/NOMA video communication systems. In this paper, we apply a deep neural network and supervised learning to an OFDMA subcarrier assignment and NOMA user grouping problem in downlink video communication systems. The resource allocation results from our previous work are used as training data at the training stage. At the testing stage, we propose a conversion algorithm to map the result of the sigmoid activation function (values between [0,1]) of the output layer of the DNN to either zero (unassigned) or one (assigned), in order to meet two hard constraints. The PSNR performance is very close (within 0.2dB) to that but has lower complexity, due to the non-iterative approach used in the testing stage of the DNN.

Optimal User Grouping for Downlink NOMA

In this letter, we study the user grouping for the downlink nonorthogonal multiple access system. The optimal user grouping problem is formulated to maximize the sum rate. Due to the combinatorial nature, the computational complexity to find the optimal solution is generally very high. To address the complexity issue, we propose faster optimal and suboptimal schemes for the user grouping problem. We then obtain the useful and important insight of the optimal user grouping. Numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal algorithm works very close to the optimal algorithm.

Joint Channel Estimation and User Grouping for Massive MIMO Systems

This paper addresses the problem of joint downlink channel estimation and user grouping in massive multiple-input multiple-output (MIMO) systems, where the motivation comes from the fact that the channel estimation performance can be improved if we exploit additional common sparsity among nearby users. In the literature, a commonly used group sparsity model assumes that users in each group share a uniform sparsity pattern. In practice, however, this oversimplified assumption usually fails to hold, even for physically close users. Outliers deviated from the uniform sparsity pattern in each group may significantly degrade the effectiveness of common sparsity, and hence bring limited (or negative) gain for channel estimation. To better capture the group sparse structure in practice, we provide a general model having two sparsity components: commonly shared sparsity and individual sparsity, where the additional individual sparsity accounts for any outliers. Then, we propose a novel sparse Bayesian learning (SBL)-based framework to address the joint channel estimation and user grouping problem under the general sparsity model. The framework can fully exploit the common sparsity among nearby users and exclude the harmful effect from outliers simultaneously. Simulation results reveal substantial performance gains over the existing state-of-the-art baselines.

Hypergraph matching for MU-MIMO user grouping in wireless LANs

This paper investigates the user grouping problem of downlink wireless local area networks (WLANs) with multi-user MIMO (MU-MIMO). Particularly, we focus on the problem of whether single user transmit beamforming (SU-TxBF) or MU-MIMO should be utilized, and how many users and which users should be in a multi-user (MU) group. We formulate the problem for maximizing the system throughput subject to the multi-user air time fairness (MU-ATF) criterion. We show that hypergraphs provide a suitable mathematical model and effective tool for finding the optimal or close to optimal solution. We show that the optimal grouping problem can be solved efficiently for the case where only SU-TxBF and 2-user MU groups are allowed in the system. For the general case, where any number of users can be assigned to groups of different sizes, we develop an efficient graph matching algorithm (GMA) based on graph theory principles. We evaluate the proposed algorithm in terms of system throughput using an 802.11ac emulator, which is created using collected channel measurements from an indoor environment and simulated channel samples for outdoor scenarios. We show that our GMA achieves at least 93% of the optimal system throughput in all considered test cases.

Lightweight User Grouping with Flexible Degrees of Freedom in Virtual MIMO

Virtual MIMO (Multiple Input Multiple Output) groups multiple single-antenna mobile devices to form an antenna array, offering higher degrees of freedom and improved spatial diversity gain as a real MIMO does, yet with much lower costs. In this paper, we focus on the user grouping problem in uplink transmission from multiple single-antenna users to one multiple-antenna base station. State-of-the-art solutions mostly target two single-antenna users, solving a pairing problem. Having more than two uplink users in a grouping has yet to be addressed. Intuitively, a higher number of users in a VMIMO group offers better spectrum efficiency, and thus more throughput gains could be expected; the group dynamics however becomes higher too, making fairness harder to be achieved with reasonable computation overhead. To address these challenges, we present a novel solution that decomposes the VMIMO user grouping into two steps. We lighten the computations in user grouping by using instantaneous signal to noise ratio (SNR) as selection criteria, and combining it with proportional fairness for larger groups of users. Lightweight computation in using instantaneous SNR in our solution allows faster grouping and feasible scheduling for a large number of users, as well as fast decision on the efficiency of the number of users in each group. We have evaluated our solution under different network configurations, and the results demonstrate that it achieves much higher data throughput as compared to existing solutions and also well preserves fairness.

OFDM-IDMA with User Grouping

A generalized version of orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) referred to as grouped OFDM-IDMA (G-OFDM-IDMA) is introduced in this paper. By dividing users into groups and transmitting each group's data only on some (as opposed to all) subcarriers, G-OFDM-IDMA can have much lower decoding complexity compared with conventional OFDM-IDMA while preserving the bit error probability (BEP) performance and the bandwidth efficiency. The user grouping problem is formulated into an integer linear programming problem whose suboptimal solution is proposed and compared with the lower bound. The optimization complexity issue is also addressed. Simulations are carried out to test the performance of G-OFDM-IDMA under various system configurations. It is observed that up to 80% complexity could be saved when the conventional OFDM-IDMA is substituted by G-OFDM-IDMA configured according to the suboptimal grouping solution.