Radio Resource Allocation Problem Research Articles

A three-level slicing algorithm in a multi-slice multi-numerology context

Fifth generation (5G) mobile networks feature a new concept called Bandwidth Part (BWP) that permits applying various multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier spacing, also coined numerologies, in the same band. With flexible numerology, the sought-after purpose is achieving lower latency for the Ultra Reliable Low Latency Communications (URLLC) service. However, the lucrative multiple numerology gains might not stand because of the ensuing Inter-Numerology Interference (INI) problems. In this paper, we assess the feasibility, as well as the profitability, of multi-numerology in a multi-slice setting. In such a context, it is paramount to study the radio resource allocation problem at the Radio Access Network (RAN) level. To that aim, we propose a three-level slicing algorithm that efficiently selects the BWP serving the URLLC users from a set of BWPs using different numerologies, designs a guard band among these BWPs to reduce INI without wasting radio resources, and attributes a bandwidth to each slice depending on multiple parameters such as the users’ Key Performance Indicators (KPIs), channel conditions, INI, and the incurred monetary cost. This work considers the three 5G services, namely the enhanced Mobile BroadBand (eMBB) service, the URLLC service and the massive Machine-Type Communications (mMTC) service. The proposed algorithm combines various tools from Game Theory, Deep-Q learning Networks as well as savvy heuristics. The performance evaluation demonstrates the high efficiency of our solution in terms of latency and throughput compared to well-known approaches in the State-of-the-Art.

On optimization formulations for radio resource allocation subject to common transmission rate

We study a radio resource allocation problem in mobile communication systems. As the distinct characteristic of this problem, a common data transmission rate is used on all channels allocated to a user. Because the channels differ in their quality, for each user the achievable rate varies by channel. Thus allocating more channels does not necessarily increase the total rate, as the common rate is constrained to be the lowest one supported by the allocated channels. Radio resource allocation subject to the common-rate constraint is of practical relevance, though little attention has been paid to modeling and solving the problem. We take a mathematical optimization perspective with focus on modeling. We first provide a complexity analysis. Next, several integer linear programming (ILP) formulations for the problem, including compact as well as non-compact models, are derived. The bulk of our analysis consists in a rigorous comparative study of their linear programming (LP) relaxations, to reveal the relationship between the formulations in terms of bounding. Computational experiments are presented to illustrate the numerical performance in bounding and LP-assisted problem solving. Our theoretical analysis and numerical results together serve the aim of setting a ground for the next step of developing model-based and tailored optimization methods.

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.

Regularization Strategy Aided Robust Unsupervised Learning for Wireless Resource Allocation

Unsupervised learning (UL) is widely used in the wireless resource allocation problems due to its lower computational complexity and better performance compared with traditional optimization algorithms. Since wireless resource allocation problems usually have several constraints, primal-dual learning based UL framework are widely adopted. However, the primal-dual learning approach has the problem of oscillation around the constraint threshold while training and there may be serious constraint violations when deployment. In addition, although the output of the neural network can also be restricted to the feasible region by the penalty function method, the optimality of such training methods cannot be guaranteed. In this paper, we combine the primal dual learning method with the penalty function method and propose a regularized unsupervised learning (RUL) framework to enhance the robustness of the primal-dual learning based UL framework. In the proposed RUL framework, we use regularization techniques to improve the robustness of primal-dual learning by reducing the risk of constraint violations while training. A quadratic penalty term is introduced into the Lagrangian function of the wireless optimization problem where the constraints can be equivalent to equality constraints to form its augmented Lagrangian function. In the simulation, we give a simple point to point power optimization problem as an example to show that the proposed RUL can improve the robustness of constraint convergence, and can also accelerate training speed.

Radio Resource Allocation for 5G Network Using Deep Reinforcement Learning

Abstract: Resource allocation is a critical task in 5Gnetworks that determines how network resources are assigned to different devices and services. Traditional methods rely on predefined rules or heuristics, which may not always be optimal. Deep reinforcement learning (DRL)is a promising approach for radio resource allocation in 5Gnetworks as it can learn to optimize resource allocation based on feedback from the network. In DRL, an agent learns to make decisions based on rewards and penalties received from the environment. In radio resource allocation, the agent would learn to allocate resources, such as frequency bands and power levels, to different devices and services to maximize some performance metric, such asthroughput or energy efficiency. The main challenge in applying DRL to radio resource allocation is designing an appropriate reward function that incentivizes the agent to improve the performance metric while avoiding undesirable behavior. Additionally, the radio resource allocation problem is complex, requiring the agent to consider many variables and constraints, such as channel conditions, interference, and QoS requirements. To address this, researchers have proposed various techniques such as hierarchical RL, multi-agent RL, and curriculum learning. Despite the challenges, DRL has shown promising results inradio resource allocation for 5G networks. It has outperformed traditional methods in some scenarios, especially when network conditions are dynamic and unpredictable. However, further research is necessary to explore the scalability and robustness of DRL-based approaches in practical 5G networks. In this method we suggest an algorithm for voice and data carriers in sub-6 GHz and millimeter wave (mmWave) frequencies respectively. The mmWave ranges between 30GHz to 300GHz

Throughput Maximization for a Multicarrier Cell-Less NOMA Network: A Framework Based on Ensemble Metaheuristics

Data collection and manipulation from real environments are two tasks that, in a well-organized way, can support the next generation of wireless networks (NGWNs) in resource allocation-related tasks such as spectrum frequency and transmission power. Due to the complexity of NGWNs, several metaheuristics have been proposed to help in these operations. However, a large number of these studies present solutions using only one algorithm or procedure, i.e., the research uses just one metaheuristic to resolve the radio resource allocation problem. This fact tends to lead to a loss of performance because, in this way, there is no considerable variability in the search strategies for better solutions. To tackle this problem, we propose a framework that maximizes the total throughput of an NGWN that implements the multi-carrier cell-less non-orthogonal multiple access (MC-CL-NOMA) architecture. The framework executes this activity using scenario information to feed an ensemble metaheuristic method. The empirical results show that the framework presents better performance than utilizing one metaheuristic individually. Moreover, the proposed method outperforms algorithms applicable in future 6G NOMA scenarios.

Deep neural network based resource allocation in D2D wireless networks

The increased complexity of future 5G wireless communication networks presents a fundamental issue for optimal resource allocation. This continuous, constrained optimal control problem must be solved in real-time since the power allocation should be consistent with the instantly evolving channel state. This paper emphasizes the application of deep learning to develop solutions for radio resource allocation problems in multiple-input multiple-output systems. We introduce a supervised deep neural network model combined with particle swarm optimization to address the issue using heuristic-generated data. We train the model and evaluate its ability to anticipate resource allocation solutions accurately. The simulation result indicates that the trained DNN-based model can deliver the nearoptimal solution.

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after realizing the importance of digital twin technology in building the metaverse. However, when utilizing digital twin technology to construct a metaverse, there remains limited research on how to allocate multibase station resources. This research translates a multibase station wireless resource allocation problem into an integer linear programming constraint model when virtual service providers construct a metaverse. In addition, the optimal VCG reverse auction (OPT-VCGRA) mechanism is designed to maximize social welfare and solve the problem of IoT devices competing for base station wireless resources. Specifically, the problem of the optimal allocation of wireless channel resources and payment rule based on the Vickrey–Clarke–Groves mechanism is solved to achieve optimal allocation and calculation of payment prices. Since the optimal allocation problem is NP-hard, this paper also designs a metaverse digital twin resource allocation and pricing (MDTRAP) mechanism based on monotonic allocation and key value theory. The mechanism sends the resource allocation results of multiple base stations to IoT devices and calculates the price payment when building a metaverse in the real world. This paper shows that both auction mechanisms have incentive compatibility and individual rationality properties. Through experiments, this paper compares the two mechanisms in terms of social welfare, the number of winners, and the overall payment. The MDTRAP mechanism performs similarly to the OPT-VCGRA mechanism in terms of social welfare, the number of winners, and channel utilization but is far superior to the OPT-VCGRA mechanism in terms of execution time and total payment. The trustful experiment also verified the truthfulness of the MDTRAP mechanism. The experimental results show that the MDTRAP mechanism can be used to solve the resource allocation problem of multiple base stations to IoT devices when building a metaverse in the real world and can effectively maximize social welfare.

A branch-and-price approach to a variant of the cognitive radio resource allocation problem

Radio-frequency portion of the electromagnetic spectrum is a scarce resource. Cognitive radio (CR) technology has emerged as a promising solution to overcome the spectrum scarcity bottleneck. Through this technology, secondary users (SUs) sense the spectrum opportunities free from primary users (PUs), and opportunistically take advantage of these (temporarily) idle portions, known as spectrum holes. In this correspondence, we consider a variant of the cognitive radio resource allocation problem posed by Martinovic et al. in 2017. The distinguishing feature of this version of the problem is that each SU, due to its hardware limitations, imposes the requirement that the to-be-aggregated spectrum holes cannot be arbitrarily far from each other. We call this restriction as the Maximal Aggregation Range (MAR) constraint, and refer to this variant of the problem as the MAR-constrained hole assignment problem. The problem can be formalized as an NP-hard combinatorial optimization problem. We propose a novel binary integer linear programming (ILP) formulation to the problem. The number of constraints in this formulation is the number of spectrum holes plus the number of SUs. On the other hand, the number of binary decision variables in the formulation can be prohibitively large, as for each legitimate spectrum allocation to each SU, one variable is needed. Due to this difficulty, the resulting 0-1 ILP instances may not even be explicitly describable, let alone be solvable by an ILP solver. We propose a branch-and-price (B&P) framework to tackle this challenge. This framework is in fact a branch-and-bound procedure in which at each node of the search tree, we utilize the so-called (delayed) column generation technique for solving the LP relaxation of the corresponding subproblem. As evidenced by the numerical results, the LP relaxation bounds are very tight. This allows for a very effective pruning of the search space. Compared to the previously suggested formulations, the proposed technique can require much less computational effort.

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization.

Online Teaching Wireless Video Stream Resource Dynamic Allocation Method considering Node Ability

At present, wireless network technology is advancing rapidly, and intelligent equipment is gradually popularized, which rapidly developed the mobile streaming media business. All kinds of mobile video applications have enriched people’s lives by carrying huge traffic randomly. Wireless networks (WNs) are facing an unprecedented burden, which allocates very important wireless video resources. Similarly, in WNs, the network status is dynamic and the terminal is heterogeneous, which causes the traditional video transmission system to fail to meet the needs of users. Hence, Scalable Video Coding (SVC) has been introduced in the video transmission system to achieve bit rate adaptation. However, in a strictly hierarchical traditional computer network, the wireless resource allocation strategy usually takes throughput as the only way to optimize the target, and it is terrible to make more optimizations for scalable video transmission. This article proposed a cross-layer design to enable information to be transmitted between the wireless base station and the video server to achieve joint optimization. To improve users’ satisfaction with video services, the wireless resource allocation problem and the video stream scheduling problem are jointly considered, which keep the optimization space larger. Based on the proposed architecture, we further study the design of wireless resource allocation algorithms and rate-adaptive algorithms for the scenario of multiuser transmission of scalable video in the Long-Term Evolution (LTE) downlink. Experimental outcomes have shown substantial performance enhancement of the proposed work.

A Two-Stage Approach of Joint Route Planning and Resource Allocation for Multiple UAVs in Unmanned Logistics Distribution

Unmanned aerial vehicles (UAVs) can serve as means of delivery to enhance the effectiveness and accuracy of logistics distribution in various scenarios, such as emergency items delivery for disaster areas and logistics services for remote areas. This paper focuses on the problem of route planning for UAVs, which is the basis for UAVs to complete distribution tasks. Besides, to enhance the collaboration of UAVs during the delivery mission, this work also studies wireless resource (spectrum, power) allocation problem for multiple UAVs enabled communication networks. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for joint route planning and resource allocation issues within a finite time. To this end, the paths of UAVs are planned by the simulated annealing (SA) method in the first stage. Based on the preplanned paths, a double deep Q network (DDQN) based resource allocation method is proposed to maximize the average sum capacity of UAV-to-UAV (U2U) links and reduce transmission delay while minimizing the interference to UAV-to-infrastructure (U2I) links. Multi-UAV communication networks are reconnected according to the positions of UAVs changing in each time slot. Then each U2U link acting as an agent learns to improve spectrum and power allocation policy with imperfect knowledge of the environment. Simulation results demonstrate that the proposed DDQN-based resource management scheme can achieve higher system communication capacity than a random scheme. Moreover, the successful transmission probability of U2U links obtained by the DDQN-based method is much higher than a Particle Swarm Optimization (PSO) based method.

Joint user association and resource allocation for load balancing in RAN slicing

In this article, we investigate the load balancing in the era of network slicing in radio access network (RAN). In our differentiated coverage RAN slicing scenario, different coverage areas of different slices have an impact on user association with base station (BS) and users’ resources obtained from BSs. Therefore, slices will limit the balance of BSs’ load. Based on this, we mainly construct a performance index to measure load balancing of RAN slicing, namely load balancing utility function. We formulate a joint optimization problem of user association and radio resource allocation for maximizing utility function. To solve this problem, we simplify and decompose it into three sub-problems, user association, slice resource allocation, and user resource allocation. We propose an iterative algorithm that uses Lagrange duality-based, Lagrange multiplier method-based, and uniform distribution methods to solve the sub-problems respectively. Finally, we obtain an approximate optimal solution with low complexity. The simulation results show that our proposed algorithm can effectively improve the load balancing utility compared with the benchmark algorithms, which means it gets the most reasonable user association and the best resource allocation.

RIS-Enhanced WPCNs: Joint Radio Resource Allocation and Passive Beamforming Optimization

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes.

Joint Computation Offloading and Radio Resource Allocation in MEC-Based Wireless-Powered Backscatter Communication Networks

The multi-access edge computing (MEC)-based wireless-powered backscatter communication networks (WP-BackComNets) allow wireless devices (WDs) to offload computation resources to lightweight and widely deployed MEC servers with the assistance of backscatter devices (BDs), which have substantial application prospects for the emerging Internet-of-Things applications. However, the limited battery capacity of WDs is one of the bottlenecks restricting its further development. Reducing the energy consumption and the computation burden of WDs while ensuring the quality-of-service requirements is an urgent issue. To this end, a joint computation offloading and radio resource allocation problem is formulated to minimize the total energy consumption of WDs for an MEC-based WP-BackComNet by jointly optimizing user association, the transmit power and transmission time of WDs, the computational offloading coefficient of each task, and the reflection coefficients of BDs, where the circuit power consumption of BDs, the computational capabilities of WDs, and the task execution delay budgets are considered. To handle this non-convex problem, we propose an efficient algorithm to obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can effectively decrease the energy consumption compared with the benchmarks.

Hybrid Radio Resource Management for Time-Varying 5G Heterogeneous Wireless Access Network

In this paper, we explore radio resource management for a time-varying 5G heterogeneous wireless access network that includes multi-RATs such as 5G new radio (NR) and long-term evolution (LTE). To cope with the practical challenges of a centralized approach such as signalling overhead and computational complexity, we decomposed the process of radio resource management into three parts, 1) RAT selection, 2) optimal radio resource allocation, and 3) congestion control. RAT selection is performed by each user device with network assistance, whereas the problem of radio resource allocation and congestion control is formulated as a stochastic optimization problem. Maintaining network stability, the average throughput utility is maximized subject to admission control and resource allocation. By using Lyapunov optimization, this utility maximization problem is decomposed into two subproblems. Radio resource allocation policy implemented at the central controller node allocates resources at each time slot using the Lagrange dual method, whereas the process of congestion control is carried out at user end based on throughput adaptation according to its current channel conditions. The theoretical and simulation results evaluate the performance of our proposed approach under the assumption of network stability. Simulation results related to individual users throughput and queue length, and performance comparison of equal power and adaptive power allocation techniques, are presented to depict the effectiveness of our proposed scheme. Furthermore, our proposed RAT selection scheme performs better than the traditional centralized and distributive mechanisms.

A New Contention-Based PUSCH Resource Allocation in 5G NR for mMTC Scenarios

This letter addresses the uplink radio resource allocation problem in a 5G massive Machine Type Communication (mMTC) scenario, where a large number of MTC devices generate small-sized packets. The current cellular networks are unsuitable for this scenario, due to the limited resources allocated to the Physical Random Access Channel (PRACH) and to the Physical Uplink Shared Channel (PUSCH). To overcome this issue, instead of improving the succeeded access attempts in the PRACH, as widely addressed in literature, we focus on the PUSCH. We present the innovative idea to exploit the unused PUSCH resources to serve also the MTC devices that have failed their access attempt, by assigning them a pool of resources, in a contention-based mode. The simulation results show that the proposed PUSCH Resource Reallocation Algorithm improves significantly any static and dynamic system performance in terms of number of succeeded communications, at the cost a negligible increment of energy consumption.

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data. Despite its advantages in data privacy-preserving, Federated Learning (FL) still has challenges in heterogeneity across UEs' data and physical resources. We first propose a FL algorithm which can handle the heterogeneous UEs' data challenge without further assumptions except strongly convex and smooth loss functions. We provide the convergence rate characterizing the trade-off between local computation rounds of UE to update its local model and global communication rounds to update the FL global model. We then employ the proposed FL algorithm in wireless networks as a resource allocation optimization problem that captures the trade-off between the FL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FL is non-convex, we exploit this problem's structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights to problem design. Finally, we illustrate the theoretical analysis for the new algorithm with Tensorflow experiments and extensive numerical results for the wireless resource allocation sub-problems. The experiment results not only verify the theoretical convergence but also show that our proposed algorithm outperforms the vanilla FedAvg algorithm in terms of convergence rate and testing accuracy.

Machine Learning-Based Resource Allocation Strategy for Network Slicing in Vehicular Networks

The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

Battery-Aware Green Cellular Networks Fed By Smart Grid and Renewable Energy

The increase of energy demand in cellular networks imposes big economic and ecological challenges. Answering to these challenges requires complex energy frameworks that consider Smart Grid, renewable energy, battery systems and employs efficient management of radio as well as energy resources. In this context, lithium batteries present very good performance indicators (e.g., high energy density, large service life and environmental friendliness) but can also have very poor use duration when the energy management system is not suitable. This is linked to the important consequences of radio resource allocation on energy consumption and operational cost. In this article, we study and propose energy and radio allocation mechanisms for cellular networks supplied with hybrid energy sources (grid and renewable). We propose a Battery Aging and Price-Aware (BAPA) algorithm that brings down the grid energy consumption of the operator while including battery degradation constraints. We decompose the problem into three subproblems: radio resource allocation problem, grid energy purchase problem and power allocation problem. We show that our algorithm performs very close to the optimal solution and outperforms a benchmark algorithm, allowing more efficient battery use, energy savings and network operation cost reduction with no impact on the QoS of users. Finally, we provide some insights into how the advancement in base station technology will help reducing the investment cost in future cellular networks.