User Grouping Algorithm Research Articles

Adaptive multibeam hopping in geo satellite networks with non-uniformly distributed ground users

This paper designs a novel low-complexity user-cluster grouping algorithm for adaptive beam hopping in geostationary satellite networks equipped with multibeam phased-array antennas. Each beam serves a cluster of users, and the challenge is to design a beam-hopping pattern where no beams are simultaneously serving nearby user clusters. We develop a line search procedure to identify near-optimum groupings for heterogeneous traffic demands. We provide a necessary condition to determine the boundaries of the line search space. Our approach employs exclusion regions around critical user clusters in congested areas, iterates a sequential congestion-based grouping algorithm, and applies a group-member-swapping procedure. It provides max-min fairness for ground users. Extensive numerical studies have shown that our user grouping algorithm produces near-optimum beam-hopping schedules with low outage probability. It achieves an improvement of up to 13dB in the worst-case signal-to-interference and noise ratio, and doubles the zero-outage data rate, compared to benchmark approaches.

Energy efficiency enhancement in millimetre-wave MIMO-NOMA using three layer user grouping and adaptive power allocation algorithm

Massive multi-input multi-output (MIMO) is realized as the principal technology in the emerging fifth generation communication network system. Hybrid structure uplink communication is considered for the MIMO Non-orthogonal multiple access (MIMO-NOMA) system’s beam forming and power efficiency improvement through the novel three-layer user grouping. In the three-layer user grouping, the K-means algorithm is adopted in the initial layer for grouping users among different clusters and rectifying clustering errors in the third layer. The second layer used the agglomerative nesting (AGNES) algorithm for merging smaller clusters based on the channel correlation and angles of arrival similarity. The beam selection is carried out to minimize the intrusion of defined beam elements and to overcome beam overlapping problems. The non-convex optimization of the power allocating problem is modified as a convex problem by introducing a Quadratic transform (QT) to minimize each user’s data rate requirement. The algorithm of coati optimization is proposed to iteratively optimize the power allocation problem. The simulation results show that our proposed methodology goes beyond the existing schemes in terms of energy efficiency beyond the maximum power and achievable sum rate can be achieved.

Subcarrier-users nomination process for downlink NOMA system

The non-orthogonal multiple access (NOMA) characteristics of 5G radio access allow for more efficient resource distribution. NOMA improves spectral efficiency by allowing Power domain users to transmit power concurrently with other users of time and spectrum resources. The selection of the subcarrier and the nomination of users to the subcarrier is of the utmost importance to maximize spectral efficiency fairness among the users and improve the data rate of weak users in the downlink NOMA system. The least gain subcarrier First-threshold-based Adaptive user grouping algorithm (LGSF-TBAUGA) has been devised for this purpose. This algorithm maintains a significant channel gain differential based on a threshold, thus avoiding successive interference cancellation (SIC) process imperfections. The computational complexity of the proposed technique is calculated and compared to other approaches. The power coefficients of the selected users are used to allocate power to them. The proposed method improves the weak user data and overall system sum rates. As a result, the spectrum efficiency and user fairness index have been greatly enhanced compared to some existing algorithms.

Channel correlation based user grouping algorithm for nonlinear precoding satellite communication system

Channel correlation based user grouping algorithm for nonlinear precoding satellite communication system

A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.

Energy Efficient Multi User Spatial Modulated NOMA for mmWave Communications

In this paper, we propose a downlink multi-user (MU) generalized spatial modulation (GSM) aided non-orthogonal multiple access (MU-GSM-NOMA) transmission technique for mmWave communications. Given a set of users, a user grouping algorithm is presented to partition the users into different clusters, and each of them is served by a dedicated uniform linear array (ULA) of antennas. The ULAs generate analog beams to steer the amplitude and phase-modulated symbols by employing the principles of NOMA. Further, the analog beams carry additional spatial information bits encoded in GSM for a particular cluster user. We propose simple digital combiners for receiver processing based on matched filtering, for which we have characterized the theoretical guarantee of the perfect detection of spatially modulated analog beams. Also, the work proposes an optimization algorithm to allocate power to the users, ensuring stable successive interference cancellation for a specific case of two users in each cluster. Finally, we present the numerical results, which demonstrate the outperformance of our proposed approach over the existing ones in terms of average sum rate and energy efficiency.

Users' grouping algorithm for fairness improvement of NOMA‐based multi‐beams satellite networks intended for 5G

AbstractNon‐Orthogonal Multiple‐Access (NOMA)‐based Multibeam Satellite Networks (MBSNs) are intended for 5G implementation, and 5G networks set the requirement for both high system capacity as well as high system reliability to all users, which is achieved via high system's fairness. The NOMA network requires a user grouping algorithm (UGA), and most existing UGAs for 2‐users NOMA‐MBSNs, favor the system's capacity maximization with little regard to the system's fairness. Thus, this paper proposes a UGA that maximizes the fairness of 2‐users NOMA‐MBSNs. The algorithm seeks to group 2M users into M‐pairs satisfying three requirements, (i) a minimum channel‐gain margin; (ii) high channel‐correlation coefficients; and (iii) there is high fairness amongst the resulting pairs, in term of their respective channel‐correlation coefficients, to increase the users' fairness of the system. To achieve this, the 2M users are split into two sets of M‐far and M‐near users based on users' channel gains for pairing far and near users with high channel‐correlation coefficients. It will turn it into a maximization Bipartite Matching problem (BPMP) of channel‐correlation coefficients. The BPMP is addressed by means of the Hungarian Method (HM) to ensure high fairness amongst resulting pairs regarding their channel‐correlation coefficients. The proposed UGA demonstrates superior fairness compared to existing UGAs.

Energy-Efficient Resource Allocation for NOMA HetNets in Millimeter Wave Communications

The integration of millimeter-wave (mmWave) and non-orthogonal multiple access (NOMA) has attracted academic and industrial interest as a promising enabling approach due to their massive bandwidth support and greater spectral efficiency. In this paper, we consider NOMA heterogeneous network (HetNet) in mmWave communications, consisting of the macro-cell and small cell tiers connected through the wireless backhaul. Following this, we propose a user grouping algorithm to significantly simplify the user clustering process where each cluster consists of highly correlated users to suppress the inter-cluster interference. We then propose a hybrid analog/digital precoding scheme at the macro base station. The goal is to maximize the system’s total energy-efficiency (EE) in NOMA HetNet by jointly optimizing the transmission of hybrid precoding, power allocation, and bandwidth partition. Next, we formulate an optimization problem within the proposed framework under the minimum data rate and total maximum transmission power constraints. Due to the non-convexity nature of the formulated problem, it is challenging to obtain the optimal solution. As a result, we transform the original problem into a quasi-convex equivalent by analyzing its structure. Then we propose energy-efficient distributed power allocation algorithms to optimize the resource allocation of macro-cell and small cells. Finally, we exhibit the simulation results to demonstrate the proposed algorithm’s performance gains. The simulation results reveal that the proposed algorithmic approach surpasses state-of-the-art and traditional mmWave orthogonal multiple access systems in both capacity and EE.

Edge-Assisted Massive Video Delivery Over Cell-Free Massive MIMO

Massive Multiple-Input Multiple-Output (MIMO), with its spatial multiplexing and channel hardening, has the potential to provide high capacity and reliability for massive video services. In spite of this, massive MIMO in the physical layer does not fully showcase its abilities in video applications unless it is specifically designed to do so. In this paper, we consider a cell-free massive MIMO system as an edge node for scheduling massive streams, and thus the standard server-to-client transmission is split into server-to-edge and edge-to-client. When the server-to-edge transmission is ideal, edge schedules massive streams to alleviate user conflicts in cell-free massive MIMO. Moreover, we propose a novel edge-to-client grouping algorithm for assigning the streams with severe interference to different time slots. The proposed algorithm achieves an innovative.7-approximation ratio while keeping the group sizes within a certain range. When the server-to-edge transmission is non-ideal, we design a special transmission framework called Aggressive, wherein the server sends the next video chunk when the current chunk reaches the edge rather than the client. Thus, the proposed framework saves considerable server-to-edge latency compared with the traditional framework. Simulation results show that the proposed user grouping algorithm improves the achievable rate by around 18% and the Aggressive framework improves the average bitrate.

Message Passing-Based mmWave MIMO-NOMA With User Grouping and Power Allocation

In this paper, we investigate a downlink non-orthogonal multiple access (NOMA) transmission scheme for hybrid millimeter-wave (mmWave) system and study its user grouping and power allocation. In order to maximize the weighted sum-rate (WSR) of the system, we first propose a user grouping algorithm based on min-sum message passing (MSMP) strategy under the limited number of radio frequency (RF) chains. Then, with the zero-forcing (ZF) digital precoding, we propose a power allocation scheme for multiple-RF-chain NOMA (MRFC-NOMA) structure based on a non-convex transformation method, and design a low-complexity algorithm. The simulation results show that the proposed joint user grouping and power allocation method with lower complexity can achieve better WSR performance than the traditional schemes.

Spectral Efficiency Optimization of Uplink Millimeter Wave MIMO-NOMA Systems

In this paper, we considered uplink communication, focusing on the improvement of spectral efficiency (SE) for millimeter wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Firstly, we proposed an adaptive cluster head selection algorithm. Then, a channel-aligned analog beamforming scheme was designed based on the selected cluster heads. After that, the user grouping algorithm was designed based on the user-equivalent channel correlation. Subsequently, the power allocation problem was transformed from a nonconvex problem to a convex one using the quadratic transformation (QT) method considering all relevant constraints. Finally, the optimal user power allocation and digital beamforming design was obtained by iteratively optimizing the power and digital beamforming. Simulation results show that our proposed scheme can achieve a higher SE than existing methods.

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.

An Improved Modelling of User Clustering for Small Cell Deployment in Heterogeneous Cellular Network

Users in practical cellular geographical areas are found to be non-uniformly distributed. Small cell (SC) deployments in heterogeneous user distribution in a cellular geographical area help to meet high data rate user demands for multimedia data communications in hot spots. SCs help to offload traffic burden from the macro cell (MC) base station, and also cater the data traffic need for the edge users where signal strength from the MC base station (BS) is very weak. For deployments of SCs along with the central MC BS (hence called HetNet) in such spatial heterogeneous user distribution, effective user grouping or clustering algorithm is required for appropriate and satisfactory service coverage. We call it service grouping or clustering of users to be put under a SC for data transmission and reception. It does not disturb the spatial positions of users in clustered non-uniform distribution. Efficient grouping or clustering of users and then deploying a SC at optimal location enhances the performance of the HetNet. It is found that the K-means algorithm used for such grouping of users to position SCs is not efficient. A novel and improved user grouping algorithm is proposed in this paper which performs much better compared to the k-means algorithm. The proposed algorithm of modelling of user clustering results in increase in the number of users under SCs, increase in more offloading of data traffic from MC BS thereby increasing data throughput of MC users. The algorithm also increases in the energy efficiencies of the SCs which is considered as one important performance metric. A doubly stochastic poison process (DSPP), also called Cox process, is assumed here for simulation of non-uniform user distributions. We consider Rayleigh distributed small scale fading model, large scale fading factor representing shadow fading, and users’ geographical distances from BSs to evaluate users’ data rates.

Joint User Grouping and Beam Selection for Beamspace mmWave Multi-User MIMO System

Lens antenna arrays based beamspace millimeter-wave multi-user massive MIMO (B-MIMO) can significantly improve energy efficiency without significant loss of spectral efficiency (SE), which is considered as a highly expected technology for future wireless communication networks. Motivated by the requirement of reducing SE loss by efficient user grouping (UG) and beam selection (BS), a joint user grouping and beam selection algorithm is studied. Specifically, a local search based BS algorithm is proposed by proving the convexity of the BS problem after relaxing the constraints. Moreover, a deep learning based UG algorithm is proposed by equivalently transforming the UG problem into a classification problem. The results of theoretical analysis and numerical simulation show that the proposed algorithms can be more closely with the performance of the exhaustive search algorithm and the complexity is lower than that of the traditional algorithms.

Improved IDMA for Multiple Access of 5G

Due to its good performance and low complexity, IDMA is believed to be an important technique for future radio access (FRA). However, its performances are highly affected by the interleaver design. In this paper we propose two contributions to improve the performance of the IDMA. First, we propose a new interleaver design, called "NLM interleaver", which improves the computational complexity, reduces the bandwidth consumption and the memory requirements of the system, provides the quasi-orthogonal spreading codes and interleavers with a high security and offers infinite sets of codes and interleavers based on only one parameter. Second, we propose a new user grouping algorithm based on the correlation function to improve the resources (Codes, Interleavers). All users are divided into several equal-size groups where each group's data transmitted at the same time, over the same frequencies and the same interleaver. The simulation results indicate that the proposed scheme can achieve better performances compared to the existing algorithms.

Enhanced User Grouping and Power Allocation for Hybrid mmWave MIMO-NOMA Systems

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider uplink communications with a hybrid beamforming structure and focus on improving the spectral efficiency (SE) and energy efficiency (EE) of mmWave multiple-input multiple-output (MIMO)-NOMA systems with enhanced user grouping and power allocation. It is noted that the optimization of the SE/EE is a challenging task due to the non-linear programming nature of the corresponding problem involving user grouping, beam selection, and power allocation. Our idea is to decompose the overall optimization problem into a mixed integer problem comprised of user grouping and beam selection only, followed by a continuous problem involving power allocation and digital beamforming design. Exploiting the directionality property of mmWave channels, we first propose a novel initial agglomerative nesting (AGNES) based user grouping algorithm by taking advantage of the channel correlations. To avoid the prohibitively high complexity of the brute-force search approach and to address the overlapping beam problem, we propose two suboptimal low-complexity user grouping and beam selection schemes, the two-stage direct AGNES (D-AGNES) scheme and the joint successive AGNES (S-AGNES) scheme. We also introduce the quadratic transform (QT) to recast the non-convex power allocation optimization problem into a convex one subject to a minimum required data rate of each user. The continuous problem is solved by iteratively optimizing the power and the digital beamforming. Extensive simulation results have shown that our proposed mmWave-NOMA design outperforms the conventional orthogonal multiple access (OMA) scenario and the state-of-art NOMA schemes.

Proportional-Fair Resource Allocation for User-Centric Networks

Considering the computational complexity and fronthaul capacity requirements of network-wide centralized optimization for Cell-free (CF) massive multiple input multiple output (MIMO) networks, we focus on user-centric (UC) networks wherein each user is served by a subset of access points (APs) rather than all the APs. We study a proportional-fair resource allocation (RA) problem, including slot resource allocation and precoding design in UC networks over consecutive time-slots. We formulate the proportional-fair (PF) resource allocation problem as a series of weighted sum-rate maximization problems and develop a two-stage heuristic RA scheme to solve the problem. The scheme adopts a user grouping process to decompose resource allocation problem into intra-group orthogonal resource allocation sub-problems. A modularity-based user grouping algorithm upon a constructed weighted digraph is proposed. Then, relying on the grouping results, a parallel distributed PFRA algorithm consisting of user selection process and a Signal to Leakage plus Noise Ratio (SLNR)-based precoding design is proposed to fulfil intra-group resource allocation. Rather than global channel state information (CSI) collected from the whole network, local CSI obtained cooperatively within each AP subset is utilized in the proposed resource allocation scheme. Numerical results confirm that the proposed RA scheme outperforms in throughput while maintaining comparable fairness among users.

Wideband beamspace squint user grouping algorithm based on subarray collaboration

The beam direction constrained problem is one of the important issues to be solved in millimeter-wave (mmWave) wideband communications when serving multi-user with squint beams whose direction varies with frequency. In this paper, we improve the number of simultaneous users served by collaboratively transmitting squint beams among multi-subarray at the base station (BS) end in a downlink multi-user line-of-sight (LoS) scenario, and reduce the interference among co-channel squint beams by a beam domain approach. The optimization problem of maximizing the number of users served in the system by transmitting beams in the two-dimensional beamspace of the planar antenna subarray is proposed and its suboptimal algorithm is given. Finally, the feasibility of the proposed method and the performance of the proposed algorithm are verified by numerical simulations.

Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT

In this paper, we address the problem of energy consumption associated with mixed signal components such as analog-to-digital components in millimeter-wave (mmWave) massive MIMO systems. We employ non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. The simultaneous wireless information and power transmission technology (SWIPT) will be used in mmWave massive Multiple-Input multiple-Output MIMO systems. The utilization of SWIPT contributes to prolonging the battery life of mobile users (MUs) and enhances the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). However, we initially designed a user grouping algorithm based on the affinity propagation clustering algorithm, which preferentially groups the user equipment (UE) based on their channel correlation and distance. Then, we design the analog RF precoder based on the selected user grouping for all beams, followed by a low-dimensional digital baseband precoder design to further mitigate inter-beam interference and maximize the achievable sum-rate for the considered system. Subsequently, we transform the original optimization problem into a joint power allocation and power-splitting maximization problem. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of coupled variables and inter-user interference. To cope with this problem, a decoupled approach is adopted, in which the power allocation and power splitting are separated, and the corresponding sub-problems are solved using the Lagrangian duality method. Simulation results confirm the effectiveness of the proposed method and demonstrate that the proposed method is near-optimal and enjoys higher spectrum and energy efficiency compared with state-of-the-art designs and the conventional SWIPT-enabled mmWave MIMO-NOMA system.

NOMA resource allocation method in IoV based on prioritized DQN-DDPG network

To meet the demands of massive connections in the Internet-of-vehicle communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning network is utilized for resource optimization in NOMA system, where a uniform sampled experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. To this point, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the system sum rate. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. Although the sum rate is improved by only 2%, the computational complexity is reduced by 43% and 64% compared to the exhaustive search algorithm and the optimal iterative power optimization algorithm, respectively.