Minimum Rate Requirement Research Articles

Game-based resource allocation in heterogeneous downlink CR-NOMA network without subchannel sharing

This paper explores how to manage resources in a two-tier, diverse cognitive radio network using a sophisticated technique called non-orthogonal multiple access. In this setup, secondary base stations (BSs), equipped with cognitive capabilities, act as bridges between the main BS and the secondary users (SUs). The main goal is to maximize the throughput of the secondary network by efficiently relaying information to capable SUs without the knowledge of their channel characteristics. To achieve this, we set out to solve a complex problem involving maximizing throughput while staying within transmission power limits, meeting minimum data rate requirements, and controlling interference power. Our approach introduces a new SU clustering method based on fuzzy logic, paired with a hierarchical learning method inspired by the well-known UCB1 algorithm to allocate power effectively to these clusters. We frame this joint optimization of SU cluster formation and power allocation as a cooperative multi-armed bandit game. Through extensive simulations, we demonstrate the effectiveness of our proposed methods. We also explore how different parameters affect the performance of the system, shedding light on the intricate workings of this network architecture.

Green Rate‐Splitting Multiple Access for Multicell and Rate Fairness Maximization

ABSTRACTThis paper investigates the green design of rate‐splitting multiple access (RSMA) for multicell, multiantenna downlink communication systems to maximize rate fairness under quality of service (QoS) constraints. An optimization framework is developed to minimize the weighted sum power consumption by jointly optimizing transmit beamformers, power allocation, and common/private rate split while ensuring per‐user minimum rate requirements. The algorithm design explicitly considers the rate fairness among users via a newly proposed green fairness index. The nonconvex transceiver design problems are transformed into tractable forms and efficiently solved by applying tools from alternating optimization and successive convex approximation. Extensive simulations demonstrate that the proposed green RSMA optimization framework can effectively balance the throughput among cell‐edge and cell‐center users, hence combating the near‐far effect. Simulation results show that at 20‐dB SNR, green RSMA achieves over 20% and 50% sum rate improvement compared with NOMA and OMA, respectively. Moreover, green RSMA maintains high fairness among users, even when the channel conditions are disparate. When the weakest user is located 120 m from the base station, green RSMA exhibits a 21.2% higher fairness index than NOMA, showcasing its ability to ensure equitable service quality across users. The framework also achieves substantial energy efficiency gains, consuming less power while providing higher data rates than baseline schemes. These results demonstrate the effectiveness of green RSMA in enhancing spectral efficiency, user fairness, and energy efficiency in multicell, multiantenna communication systems, making it a promising solution for sustainable and high‐performance wireless networks.

Ubiquitous learning models for 5G communication network utility maximization through utility-based service function chain deployment

The main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications and effective ubiquitous learning models. The aim is to ensure differentiated network performance for a wide range of services while maximizing the collaborative revenue of infrastructure operators and wireless virtual operators. To achieve this, a utility-based service function chain deployment strategy is introduced, tailored to the specific characteristics of Ubiquitous Learning based 5G-RAN-Architecture. Consideration is given to the virtual operator's maximum tolerable end-to-end latency, minimum service rate requirements, and the infrastructure operator's constraints on computing and link resources. It also looks at how different deployment scenarios for service function chains affect network performance and creates a utility model using a business framework. The ultimate objective is to optimize the collective revenue of infrastructure operators and virtual operators. The approach leverages genetic algorithms and Matlab's Linprog function for iterative problem-solving. The graph clearly shows that the SFC deployment algorithm in this study uses less infrastructure resources for Front Haul links. This implies that the algorithm successfully reduces the load on Front Haul lines, which in turn lowers the cost of SFC deployment and makes it easier to deploy more SFCs across the infrastructure. This work contributes to the evolution of 5G wireless communications and its seamless integration with ubiquitous learning models.

Joint mode selection and resource allocation to flexibly guarantee quality of service for downlink MISO-NOMA systems

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) detection, knownas SIC NOMA, enjoys higher spectral efficiency (SE), but suffers increased processing complexity and additional error propagation compared to Non-SIC NOMA,in whichthe receivers directly detect their own signalsfrom thesuperposed signalwithout SIC detection. We propose a joint Mode Selection and Power Allocation (MSPA) scheme that can flexibly realize different kinds of tradeoffs while guaranteeing minimum rate requirements (MRRs) for two-user MISO-NOMA downlink. Specifically, we first obtain rate region boundaries that guarantee two users’ MRRs for two candidatemodes: maximum-ratio transmission (MRT)/SIC NOMA and minimum mean square error beamforming (MMSE-BF)/Non-SIC NOMA. We then construct an expression of power allocation factors (transmit power factors) that can satisfy the two users’ MRRs for MRT/SIC NOMA (MMSE-BF/Non-SIC NOMA) by introducing a parameter λ. Finally, we achieve outer boundary of the union ofthe two modes' rate regionsby mode selection and derive expected users' rates on it. Numerical results demonstrate that the proposed MSPA can improve SE and flexibly achieve different kinds of tradeoffs by adjusting λ, while guaranteeing the two users’ MRRs simultaneously for high signal to noise ratio (SNR) scenario and alternately guaranteeing the two users’ MRRs for low SNR scenario.

Energy Efficiency Optimization for a V2X Network with NOMA-CoMP Enabled

With the increase in user density, it is a challenging problem to achieve higher energy efficiency in vehicle-to-everything (V2X) networks, where frequent handovers occur as users continuously pass through dense cell boundaries. In this paper, we investigate this energy efficiency problem in a highly dense V2X network. To alleviate inter-cell interference and reduce unnecessary handovers, we propose a user-centric network architecture and utilize the coordinated multiple point (CoMP) transmission scheme to enable multiple roadside access points (APs) simultaneously to service one mobile vehicle. Moreover, non-orthogonal multiple access (NOMA) is adopted to enable multiple users to multiplex within one sub-channel and improve system spectrum reuse. Accordingly, the energy efficiency optimization problem is formulated as system achievable rate per joule, with the constraints of the AP transmit power budget, vehicle minimum rate requirements, and the maximum number of CoMP APs serving each vehicle. To solve the optimization problem, a semi-dynamic CoMP AP clustering algorithm is proposed to select a group of CoMP APs to serve a vehicle user by considering the vehicle–AP channel coefficients and the vehicle user’s achievable energy efficiency. Then, with the selected CoMP AP clusters, a bidirectional selection algorithm is applied via NOMA to select multiple vehicle users multiplexing within one sub-channel and perform sub-channel power allocation. Numerical simulations are presented to verify the effectiveness of the proposed algorithms, and the results show the improvements offered by the proposed algorithms compared to current benchmarks.

The peer effect in adverse selection: Evidence from the micro health insurance market in Pakistan

AbstractThe peer effect may amplify adverse selection in social networks, hampering the sustainable operation of microinsurance. This paper uses data from a micro health insurance program in Pakistan to test for the peer effect in renewal decisions and the role it plays in amplifying adverse selection within social networks. The paper finds evidence supporting that insurance renewal decisions are similar among peers in the same network, and the peer effect is stronger among households of the same risk type than households of different risk types, indicating that the heterogeneous peer effect acts as an amplifier for adverse selection. The paper provides policy implications for effective ways to mitigate the peer effect and adverse selection, based on the results of heterogeneity analyses. The policy recommendation is to enforce a minimum group enrollment rate requirement of at least 60% for large groups to mitigate the peer effect.

Framework for fast and low-complexity deployment of UAVs-assisted communication

Unmanned Aerial Vehicles (UAVs) can work as aerial base stations along with the ground base stations to enhance capacity and provide ubiquitous coverage in 5G and beyond communication systems. UAVs have numerous features that distinguish them from the traditional fixed ground network infrastructure. These features include more flexibility, lower costs, faster integration, and better channel conditions with users owing to the UAVs maneuverability and adaptability to varying network conditions. This paper addresses UAVs positioning and radio resource management, which are two critical design issues that greatly impact the system performance and user experience in UAVs-assisted communication scenarios. Our objective is maximizing the achieved sum rate of a system consisting of a ground base station and a number of UAVs operating as aerial base stations under constraints on the minimum rate requirements of users, the capacity of wireless backhaul links between the UAVs and the ground base station, and the power budgets of the base stations. In doing so, we optimize the UAVs locations, the users-to-base stations association, the access link channels assignment, and the power allocation. The problem is a mixed-integer non-convex optimization, which is NP-hard to solve, and consequently we propose a fast low-complexity algorithm that achieves high performance compared to the common block coordinate descent-based solution approach and at much less execution time.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

An optimization framework for RIS-based energy-efficient multi-cell NOMA systems

Non-orthogonal multiple access (NOMA) is a highly promising multiple access strategy for wideband communication systems due to its high spectral efficiency. However, in multi-cell multi-band systems, interference issues can arise from resource sharing among multiple users, which limits the potential of NOMA. To tackle these issues, in this paper, we study a NOMA system where reconfigurable intelligent surfaces (RIS) are employed across multiple cells with multiple users clusters. Specifically, we propose an optimization framework to maximize the system energy efficiency (EE) through jointly designing the transmission beamformer at each base station, the RIS phase shifts and the corresponding binary selection matrix indicating which elements of the RIS should be used to serve a specific cell. Our optimization framework takes into account the minimum rate requirements of users, the successive interference cancellation (SIC) decoding-based rate constraint, fairness among users as well as the maximum allowed transmission power. The resulting non-convex problem is solved by using an alternating optimization algorithm, wherein semidefinite programming (SDP), successive convex approximation (SCA), and penalty-based difference-of-convex (DC) programming are used to reach a solution. Simulation results show that the proposed system outperforms conventional orthogonal access in terms of EE performance, the achievable rate, and power consumption. Also, the results revealed the critical transmission power level for NOMA beyond which the EE starts decaying. Moreover, the proposed NOMA system shows a limited EE performance compared to the conventional orthogonal access as the number of clusters exceeds a certain limit.

Power Control With QoS Guarantees: A Differentiable Projection-Based Unsupervised Learning Framework

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users’ quality-of-service (QoS) constraints, guaranteeing constraint satisfaction becomes a fundamental challenge. In this paper, we propose a novel unsupervised learning framework to solve the classical power control problem in a multi-user interference channel, where the objective is to maximize the network sum-rate under users’ minimum data rate or QoS requirements and power budget constraints. Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued. The first is called Deep Implicit Projection Network (DIPNet), and the second is called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a differentiable convex optimization layer to implicitly define a projection function. On the other hand, DEPNet uses an explicitly-defined projection function, which has an iterative nature and relies on a differentiable correction process. DIPNet requires convex constraints; whereas, the DEPNet does not require convexity and has a reduced computational complexity. To enhance the sum-rate performance of the proposed models even further, Frank-Wolfe algorithm (FW) has been applied to the output of the proposed models. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate but also achieve zero constraint violation probability, compared to the existing DNNs. The proposed solutions outperform the classic optimization methods in terms of computation time complexity.

Near-Field Integrated Sensing and Communications

A near-field integrated sensing and communications (ISAC) framework is proposed, which introduces an additional distance dimension for both sensing and communications compared to the conventional far-field system. In particular, the Cramer-Rao bound for the near-field joint distance and angle sensing is derived, which is minimized subject to the minimum communication rate requirement of each user. Both fully digital antennas and hybrid digital and analog antennas are investigated. For fully digital antennas, a globally optimal solution of the ISAC waveform is obtained via semidefinite relaxation. For hybrid antennas, a high-quality solution is obtained through two-stage optimization. Numerical results demonstrate the performance gain introduced by the additional distance dimension of the near-field ISAC over the far-field ISAC.

Collaborative Management of Resource Allocation and Precoding for Dual-Mode Networks

A sub-6GHz (μWave) and millimeter wave (mmWave) dual-mode network can deliver high data rates while maintaining dependable coverage. This paper proposes a novel resource allocation and precoding algorithm based on collaborative sensing in the dual-mode network. We formulated the collaborative optimization problem of resource allocation and precoding, which maximizes the sum of data rates while meeting minimum acceptable rate requirements and ensuring proportional fairness. Considering the spatial similarity between mmWave channel and μWave channel, the prior information of mmWave channel predicted by μWave band is generated to process sensing weight (i.e., subchannel allocation weight of Markov algorithm), which realizes fast dual-mode network resource allocation. Furthermore, a low complexity two-stage precoding scheme for dual-mode networks is proposed based on μWave spatial information. Low-complexity analog precoding is realized in the first stage based on μWave spatial information. In the second stage, inspired by the correlation between minimum mean square error (MMSE) and mutual information, this paper proposes an alternating optimization algorithm to find a local weighted sum-rate (WSR) optimum with low complexity. Compared with other heuristic algorithms, simulation results demonstrate the apparent advantages of the proposed collaborative sensing optimization algorithm. Most importantly, it can be seen that we can achieve a performance that approaches an upper bound.

Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks

In this letter, we study the deployment of Unmanned Aerial Vehicle mounted Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV deployment problem as a user satisfaction maximization problem, that is, maximizing the proportion of served ground users (GUs) that meet a given minimum data rate requirement. We propose an interference-aware deployment (IAD) algorithm for serving arbitrarily distributed outdoor GUs. The proposed algorithm can alleviate the problem of overlapping coverage between adjacent UAV-BSs to minimize inter-cell interference. Therefore, reducing co-channel interference between UAV-BSs will improve user satisfaction and ensure that most GUs can achieve the minimum data rate requirement. Simulation results show that our proposed IAD outperforms comparative methods by more than 10% in user satisfaction in high-density environments.

Resource Allocation Design for Spectral-Efficient URLLC Using RIS-Aided FD-NOMA System

This paper investigates an unconventional RIS-aided non-orthogonal multiple access (NOMA) system to achieve spectral-efficient full-duplex (FD) ultra-reliable low-latency communication (URLLC) systems. In particular, we focus on sum-rate maximization under finite-block length coding for the considered system by jointly optimizing power allocation at each user and beamforming at the RIS, while ensuring that the minimum rate, delay, and reliability requirements are satisfied. To solve the formulated joint optimization problem, we proposed an alternate optimization framework that solves the sub-problems of power allocation and beamforming alternatively based on general convex approximations. Numerical results validate that the proposed FD scheme achieves 90% and 40% better rate performance than its counterpart orthogonal and half-duplex schemes, respectively. Moreover, the exploitation of RIS-aided network for the considered system provides rate performance amelioration up to 40-50% and 56-80% compared to the random phase-shifting at the RIS and the system without RIS, respectively.

Fair Energy-Efficient Resource Optimization for Green Multi-NOMA-UAV Assisted Internet of Things

Owing to the advantages of better air-ground channel and higher flexibility, unmanned aerial vehicle (UAV) has been widely applied in the field of Internet of Things (IoT) to increase the communication coverage. However, the UAV with limited energy is facing severe energy shortage when serving more and more IoT devices. In this paper, we propose a non-orthogonal multiple access (NOMA) based green multi-UAV assisted IoT system to increase user capacity while improving the energy utilization of each UAV. Considering the limited energy budget of each UAV, we formulate a fair energy-efficient resource optimization problem under the constraints of maximum transmit power of each UAV, minimum communication rate requirement of each user and UAV mobility. By alternately optimizing the communication scheduling, transmit power allocation and UAV trajectory with the Dinkelbach method and the successive convex approximation (SCA), the energy-efficient fairness can be achieved between the UAVs by maximizing the minimum energy efficiency of the UAVs. The simulation results indicate the fair energy efficiency between the UAVs can be obtained through the alternative resource optimization, and the proposed multi-NOMA-UAV assisted IoT can get higher energy efficiency than the traditional orthogonal multiple access (OMA)-UAV assisted IoT.

Efficient Battery Usage in Wireless-Powered Cell-Free Systems With Self-Energy Recycling

This paper investigates wireless-powered cell-free systems, in which the users send their uplink data signal while simultaneously harvesting energy from network nodes and user terminals – including the transmitting user terminal itself – by performing self-energy recycling. In this rather general setting, a closed-form lower bound of the amount of harvested energy and the achieved signal-to-interference-plus-noise ratio expressions are derived. Then, to improve the energy efficiency, we formulate the problem of minimizing the users' battery energy usage while satisfying minimum data rate requirements. Due to the non-convexity of the problem, a novel alternating optimization algorithm is proposed, and its proof of convergence is provided. Finally, numerical results show that the proposed method is more efficient than a state-of-art algorithm in terms of battery energy usage and outage rate.

Energy Efficiency in Secure VLC Networks With SLIPT

This letter focuses on maximizing the energy efficiency (EE) for secure visible light communication (VLC) network with simultaneous lightwave information and power transfer (SLIPT) by optimizing the beamforming vectors and direct current (DC) bias under the constraints of the minimal rate requirement, the secure transmission requirement, the minimal energy harvesting (EH) requirement, and total power budget at the transmitters. To solve the non-convex optimization problem, we present a dual-layer iterative algorithm based on Dinkelbach’s and successive convex approximation (SCA) methods. An efficient algorithm is also designed to find a good initial point to accelerate the convergence. Simulation results show three typical phenomena: (1) the EE first remains unchanged and then decreases with the increasing EH requirement; (2) compared with the traditional sum-rate maximization design, our proposed approach achieves notable EE performance gain under the secure constraints; (3) the number of the authorized receivers, as well as the number of the unauthorized receivers, imposes significant impact on the EE performance.

Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications

A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design. The joint design problem is formulated to maximize the system sum rate while satisfying the SIC decoding requirements and users’ minimum data rate requirements. To address this highly complex and coupling non-convex mixed integer nonlinear programming (MINLP), a novel distributed auto-learning graph neural network (AutoGNN) architecture is proposed to alleviate the overwhelming information exchange burdens among base stations (BSs). The proposed AutoGNN can train the GNN model weights whilst automatically optimizing the GNN architecture, namely the GNN network depth and message embedding sizes, to achieve communication-efficient distributed scheduling. Based on the proposed architecture, a bi-level AutoGNN learning algorithm is further developed to efficiently approximate the hypergradient in model training. It is theoretically proved that the proposed bi-level AutoGNN learning algorithm can converge to a stationary point. Numerical results reveal that: 1) the proposed cluster-free NOMA framework outperforms the conventional cluster-based NOMA framework in the multi-cell scenario; and 2) the proposed AutoGNN architecture significantly reduces the computation and communication overheads compared to the conventional convex optimization-based methods and the conventional GNNs with fixed architectures.

Joint Optimization of Radar and Communications Performance in 6G Cellular Systems

Dual functional radar communication (DFRC) is a promising approach that provides a viable solution for the problem of spectrum sharing between communication and radar applications. This paper studies a DFRC system with multiple communication users (CUs) and a radar target. The goal is to devise beamforming vectors at the DFRC transmitter in such a way that the radar received signal-to-clutter-plus-noise-ratio (SCNR) is maximized while satisfying the minimum data rate requirements of the individual CUs. With regard to clutter, we consider two scenarios based on the possibility of clutter removal. Even though the formulated optimization problems are non-convex, we present efficient algorithms to solve them using convex optimization techniques. Specifically, we use duality theory and Karush-Kuhn-Tucker conditions to show the underlying structure of optimal transmit precoders. In the proposed solution, it is observed that there is no need to transmit separate probing signal for the radar detection in both the considered scenarios. This results in reduction in the number of optimization variables in the problem. Moreover, we make use of the asymptotic equivalence between Toeplitz matrices and Circulant matrices to further reduce the complexity of the proposed algorithm. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithms.

Hybrid optimization using lion and dragonfly for enhanced resource allocation in fifth-generation networks

“5G (Fifth Generation) network promises Enhanced Mobile Communications (EMC), Massive Machine Type Communications (MMTC) and Ultra-Reliable Low Latency ygCommunications (URLLC)”. For enhancing communication, resource allocation is a crucial factor that guarantees the partitioning of channels into resource blocks, which were allocated to diverse users. Different methods have been introduced for improving resource scheduling however, they do not reach the optimum solution. This work aims to scrutinize the resource allocation problems in the “hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) systems” that comprise the OMA and NOMA modes. It is used to achieve the EE and SE trade-off with minimal rate requirements of users. In addition, the total degree of freedom includes “user clustering, power allocation, choice of Multiple Access (MA) modes, and subcarrier assignment as well”. Accordingly, the optimization concept is integrated that offers the most noteworthy way of resolving multi-objective issues. In this context, resource allocation with power allotment is considered a vital factor, which is determined as a single-objective issue. Thereby this paper ensures the optimal allocation of power and subcarriers by a novel algorithm termed as Dragon Levy-based Lion Cub Generation Model (DL-LCG). Finally, the presented model is evaluated over existing models regarding cost analysis