Joint Resource Allocation Algorithm Research Articles

Jointly Optimize Partial Computation Offloading and Resource Allocation in Cloud-Fog Cooperative Networks

Fog computing has become a hot topic in recent years as it provides cloud computing resources to the network edge in a distributed manner that can respond quickly to intensive tasks from different user equipment (UE) applications. However, since fog resources are also limited, considering the number of Internet of Things (IoT) applications and the demand for traffic, designing an effective offload strategy and resource allocation scheme to reduce the offloading cost of UE systems is still an important challenge. To this end, this paper investigates the problem of partial offloading and resource allocation under a cloud-fog coordination network architecture, which is formulated as a mixed integer nonlinear programming (MINLP). Bring in a new weighting metric-cloud resource rental cost. The optimization function of offloading cost is defined as a weighted sum of latency, energy consumption, and cloud rental cost. Under the fixed offloading decision condition, two sub-problems of fog computing resource allocation and user transmission power allocation are proposed and solved using convex optimization techniques and Karush-Kuhn-Tucker (KKT) conditions, respectively. The sampling process of the inner loop of the simulated annealing (SA) algorithm is improved, and a memory function is added to obtain the novel simulated annealing (N-SA) algorithm used to solve the optimal value offloading problem corresponding to the optimal resource allocation problem. Through extensive simulation experiments, it is shown that the N-SA algorithm obtains the optimal solution quickly and saves 17% of the system cost compared to the greedy offloading and joint resource allocation (GO-JRA) algorithm.

Resource Allocation for Cognitive Satellite-HAP-Terrestrial Networks With Non-Orthogonal Multiple Access

Cognitive radio and non-orthogonal multiple access are promising to improve the spectrum efficiency for the satellite-high altitude platform (HAP)-terrestrial networks. To achieve the optimal system performance and guarantee the quality of service, we formulate a constrained mixed integer nonlinear programming problem to maximize the sum rate of secondary network for the considered NOMA-enabled cognitive satellite-HAP-terrestrial network, while considering the constraints of the quality of service of primary users, the maximum power of HAP, and the number of secondary users in a NOMA group. To tackle the non-convex optimization problem, we decouple it into subchannel assignment and power assignment subproblems. Then, a greedy heuristic algorithm is proposed to assign subchannels to secondary users. Moreover, to solve the power allocation subproblem, we further design a power allocation algorithm by utilizing successive convex approximation, dual decomposition, and subgradient methods. On this basis, an iterative joint resource allocation algorithm is designed. Numerical simulations and comparisons with the orthogonal multiple access scheme are provided to verify the effectiveness of the proposed scheme.

Joint Resource Allocation Algorithm for Energy Harvest-Based D2D Communication Underlaying Cellular Networks Considering Fairness

In this paper, we aim to maximize the minimum data volume for the energy harvest (EH)-based D2D communication underlaying cellular network. The formulated problem is a mixed integer nonlinear programming (MINLP), which we split into two sub-problems to solve separately. We first employ the Successive Convex Approximation (SCA) method to solve the time and power allocation, while putting the spectrum multiplexed pairs as fixed. Then, we develop a fair allocation mechanism to allocate spectrum resources. Numerical results show that our developed algorithm outperforms other benchmark schemes in terms of the minimum and overall data volumes.

Dynamic Manager Selection Assisted Resource Allocation in URLLC With Finite Block Length for 5G-V2X Platoons

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

Resource Allocation for TDD Cell-Free Massive MIMO Systems

In this paper, we investigate a joint resource allocation algorithm in a time-division duplex (TDD)-based cell-free massive MIMO (CFMM) system, which has great potential to improve spectrum efficiency and throughput. Because the throughput of the system is a bottleneck due to the sharing of the pilot, we attempted to alleviate pilot contamination. We propose a pilot assignment approach called user-distance-ordering-based pilot assignment (UDOPA) based on the distance between users and the center, which can be calculated by the K-means method. Then, using an access point (AP) selection algorithm, only the APs having a major impact on the macro diversity gain of a user are selected as the serving APs. In contrast to the existing AP selection algorithms, users with the same pilot are not allowed to share the same serving AP in the proposed AP selection algorithm, which also significantly reduces the complexity of data processing. Finally, a modified max–min power control scheme with teaching–learning-based optimization (TLBO) is proposed to further improve the performance of the systems and guarantee the minimum user rate. Simulation results show that the proposed joint resource allocation scheme can effectively enhance CFMM systems’ performance.

Socially Aware V2X Networks With RIS: Joint Resource Optimization

Inspired by the promising potentialof reconfigurable intelligent surface (RIS)-based transmission, this paper investigates the RIS-aided vehicle-to-everything (V2X) communication system with the social trust, where multiple vehicle-to-infrastructure (V2I) links share the spectrum with vehicle-to-vehicle (V2V) links. Subject to the reliability constraint of V2V links, the sum V2I link capacity is maximized, through jointly optimizing the power allocation, RIS reflection coefficients and spectrum allocation. The formulated mix-integer and non-convex problem is effectively converted into three subproblems, which can be iteratively solved by our proposed three-stage joint resource allocation and RIS optimization (JRARO) algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm, and that the RIS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity.

Power Minimization-Based Joint Resource Allocation Algorithm for Target Localization in Noncoherent Distributed MIMO Radar System

In this article, an optimization scheme integrating power allocation, bandwidth assignment and radar node selection (PABARNS) is developed to improve power utilization efficiency (PUE) of noncoherent distributed multiple-input–multiple-output (MIMO) radar system for target localization. The basis of the proposed scheme is to minimize the total transmit power subject to a predetermined target localization accuracy requirement, where the target localization accuracy is evaluated with the Crámer–Rao lower bound. Subsequently, the PABARNS scheme results in a mixed nonlinear Boolean-nonconvex optimization problem. In view of the unique feature of the optimization model, a two-step technique based on the heuristic algorithm and alternating direction method of multipliers method is presented to solve the underlying optimization problem, with merit of that it is more workable than other existing approaches when the power and bandwidth lie in any bound set. Finally, the simulation reveals the higher accuracy and lower complexity of the developed algorithm compared with the benchmarks, as well as the superiority of the PABARNS scheme compared with over other existing schemes in the view of the achievable PUE.

Effective Throughput Maximization of NOMA With Practical Modulations

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effective throughput</i> , which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes.

Point-to-Multipoint Coherent Architecture with Joint Resource Allocation for B5G/6G Fronthaul

With the increasing wireless data and massive interconnections in B5G/6G, demands for an intelligent, flexible, and cost-effective fronthaul network have emerged to provide services for a ubiquitous mobile society. This calls for disruptive innovations in both the architecture and transmission technology. In this article, we introduce a point-to-multipoint coherent architecture as a promising solution, and analyze its advantages and challenges compared to other fronthaul solutions. Based on this architecture, we propose a novel joint resource allocation framework that enables the fronthaul to intelligently self-manage and self-optimize in real time. In addition, we propose a low-complexity joint resource allocation algorithm for different scenarios in this framework. Simulations show that the proposed framework and algorithm can effectively utilize the limited resources and improve the performance of the fronthaul.

A Joint Resource Allocation Algorithm for D2D Communication

The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission rates. The result is that, cellular networks must technically evolve to support such higher rates, to be equipped with greater capacity, and to increase the spectral and energy efficiency. Compared with 4G technology, the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times longer. Therefore, this new generation of networks has adopted a heterogeneous and ultra-dense architecture, where different technological advances are combined such as device-to-device (D2D) communication, which is one of the key elements of 5G networks. It has immediate applications such as the distribution of traffic load (data offloading), communications for emergency services, and the extension of cellular coverage, etc. In this communication model, two devices can communicate directly if they are close to each other without using a base station or a remote access point. Thus, eliminating the interference between the D2D and cellular communication in the network. The interference management has become a hot issue in current research. In order to address this problem, this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource assignment. Simulation results show that the proposed algorithm effectively improves the system performance and reduces the interference as compared with existing algorithms.

A Low Complexity Algorithm of Dynamic Resource Allocation for Downlink Multi-Carrier NOMA Systems

This paper investigates the sub-channels and power allocation issue in the multi-carrier NOMA communication system. To maximize the sum rate of the system, we design a fast dynamic resource allocation (FDRA) algorithm. The algorithm can quickly identify the interference nodes with poor channel conditions, and reduces the search complexity of the algorithm. By jointly optimizing sub-carrier and power resource allocation, the proposed scheme can take full advantage of the channel capacity, and maximize the system performance while ensuring the quality of service (QoS) of each user. Simulation results show that when compared with the joint resource allocation (JRA) algorithm and the genetic algorithm (GA), the FDRA algorithm can achieve the best performance on the complexity and the sum rate of the system. Thus, the proposed algorithm can provide a fast and efficient resource allocation scheme for the downlink multi-carrier NOMA systems.

On Joint Offloading and Resource Allocation: A Double Deep Q-Network Approach

Multi-access edge computing (MEC) is an important enabling technology for 5G and 6G networks. With MEC, mobile devices can offload their computationally heavy tasks to a nearby server which can be a simple node at a base station, a vehicle or another device. With the increasing number of devices, slices and multiple radio access technologies, the problem of task offloading is becoming an increasingly complex problem. Thus, traditional approaches experience limitations while machine learning algorithms emerge as promising methods. In this paper, we consider binary and partial offloading problems and aim to jointly find optimal decisions for offloading and resource allocation which maximize the number of computed bits while minimizing the energy consumption. This allows improved usage of uplink transmit power and local CPU resources. We propose the Deep Reinforcement Learning for Joint Resource Allocation and Offloading (DJROM) algorithm that uses the double deep Q-network approach and models UEs as agents. We compare the proposed approach with two other machine learning based techniques, namely, multi-agent deep Q-learning (MARL-DQL) and multi-agent deep Q network (MARL-DQN) under fixed and mobile scenarios. Our results show that, DJROM scheme enhances the efficiency better than the other compared algorithms.

RB Allocation Scheme for eMBB and URLLC Coexistence in 5G and Beyond

In this paper, we studied resource allocation in a dynamic multiplexing scenario of two 5G services, namely, enhanced mobile broadband (eMBB) and ultrareliable low-latency communication (URLLC). Considering that eMBB services focus on high data rates while URLLC is very strict in terms of latency and reliability, we formulate a joint eMBB/URLLC resource allocation problem. To solve the formulated problem, in particular, we propose a joint resource allocation algorithm that can satisfy both eMBB user rate and URLLC interrupt probability requirements. We propose an RB allocation scheme that takes into account the needs of eMBB and URLLC users. The program consists of two phases as follows: (1) establish an RB allocation for eMBB users and (2) assign the minislot for URLLC users and calculate the transmission power of URLLC users that ensures the reliability constraint. We also realize and verify the algorithm through the simulation.

Joint Resource Allocation and Trajectory Optimization for Multi-UAV-Assisted Multi-Access Mobile Edge Computing

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has been considered as a promising approach to offering extensive coverage and massive computing capacities for Internet of Things (IoT). In this letter, we propose a novel multi-UAV-assisted multi-access MEC model by allowing each IoT user to offload task bits to multiple MEC servers deployed at UAVs simultaneously for parallel computing, which can effectively reduce the energy consumption of users and UAVs. The weighted sum energy consumption of UAVs and users is minimized by jointly optimizing the bit allocation, transmit power, CPU frequency, bandwidth allocation and UAVs' trajectories. Due to the non-convexity of the formulated problem, it is decomposed into two subproblems and a joint resource allocation and trajectory design algorithm is proposed by alternative optimization. Simulation results show that our proposed algorithm with multiple radio access outperforms the fixed trajectory, fixed bandwidth allocation and the single access schemes.

Robust joint resource allocation algorithm for hierarchical underwater acoustic communication networks based on outage probability minimization

AbstractThis study investigates a joint resource allocation (channel selection (CS) and power allocation (PA)) problem with multiple channels, users, constraints, and uncertainties in the underwater acoustic communication networks (UACNs). A hierarchical joint resource allocation model is formulated considering different user requirements. This model aims to minimize the maximum outage probability of lower users while satisfying the outage requirements of upper users. This study presents a robust distributed power allocation (RDPA) algorithm to solve complex PA problems with nonconvex mixed integers and uncertainties in UACNs. The residual energy of nodes is considered in the proposed RDPA algorithm to balance their energy, thereby extending the working time of the entire network. The proposed robust CS algorithm considers the actual and potential interferences of all neighboring nodes to decrease the interferences of nodes and the entire network, thereby reducing the outage probability of the network. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Cooperative Virtual Connectivity Control in Uplink Small Cell Network: Towards Optimal Resource Allocation

Nowadays, the application of soft computational techniques in power systems has been increasing with good acceptance under the machine learning umbrella. In this paper, first we presented a deep reinforcement learning approach to find the optimal configuration for HetNet systems. We used a huge quantity of radial configurations from a test system for training purposes. We also studied the joint carrier/power allocation problem in a multi-layer hierarchical networks to achieve the optimal power efficiency in addition to ensure the quality-of-experience for all subscribers. The proposed approach utilizes an adaptive Load Balancing model that aims to obtain “almost optimal” fairness among all servers from the key performance indicator viewpoints. Contrary to current model-based energy efficiency methods, we proposed a Joint Resource Allocation, Energy Efficiency and Flow Control algorithm to solve conventional non-convex and hierarchical optimization problems. Also, referred to SLA-based continuous resource allocation, we generalized the proposed algorithm to Joint Flow/Power Control, and Throughput Optimization algorithm to achieve the optimal energy efficiency beside of guaranteeing user throughput constraints. The simulation results exhibit that the proposed flow-controlled power optimization approach using the capability of network topology adjustment, is able to considerably enhance the energy efficiency compared to the conventional schemes.

D2D Assisted Cellular Networks in Licensed and Unlicensed Spectrum: Matching-Iteration-Based Joint User Access and Resource Allocation

Device-to-Device (D2D) communications, which enable direct communication between nearby user devices over the licensed spectrum, have been considered a key technique to improve spectral efficiency and system throughput in cellular networks (CNs). However, the limited spectrum resources cannot be sufficient to support more cellular users (CUs) and D2D users to meet the growth of the traffic data in future wireless networks. Therefore, Long-Term Evolution-Unlicensed (LTE-U) and D2D-Unlicensed (D2D-U) technologies have been proposed to further enhance system capacity by extending the CUs and D2D users on the unlicensed spectrum for communications. In this paper, we consider an LTE network where the CUs and D2D users are allowed to share the unlicensed spectrum with Wi-Fi users. To maximize the sum rate of all users while guaranteeing each user’s quality of service (QoS), we jointly consider user access and resource allocation. To tackle the formulated problem, we propose a matching-iteration-based joint user access and resource allocation algorithm. Simulation results show that the proposed algorithm can significantly improve system throughput compared to the other benchmark algorithms.

Clustering and resource allocation strategy for D2D multicast networks with machine learning approaches

In this paper, the clustering and resource allocation problem in device-to-device (D2D) multicast transmission underlay cellular networks are investigated. For the sake of classifying D2D users into different D2D multicast clusters, a hybrid intelligent clustering strategy (HICS) based on unsupervised machine learning is proposed first. By maximizing the total energy efficiency of D2D multicast clusters, a joint resource allocation scheme is then presented. More specifically, the energy efficiency optimization problem is constructed under the quality of service (QoS) constraints. Since the joint optimization problem is non-convex, we transform the original problem into a mixed-integer programming problem according to the Dinkelbach algorithm. Furthermore, to avoid the high computational complexity inherent in the traditional resource allocation problem, a Q-Learning based joint resource allocation and power control algorithm is proposed. Numerical results reveal that the proposed algorithm achieves better energy efficiency in terms of throughput per energy consumption.

Optimized Power Control in Internet of Vehicles Based on Serial Interference Cancellation and User Matching

With the significant advantages of 5G technique such as higher transmission rate and lower transmission delay, it is more in line with the requirements of the connected vehicle scenario. In addition, the serial interference cancellation (SIC) technology in 5G technology can further improve the communication quality compared to a single one-to-one multiplexing method. Taking the dynamic communication characteristics of Internet of Vehicles into consideration, the robust optimization problem is formulated, which attempts to maximization the total throughput of vehicle users under the changing channel gains. The channel uncertainty is formulated as probability constraints, and they are transformed to the deterministic form to achieve a convex optimization problem. And the Hungarian algorithm is introduced to allocate the available channels to the V2V pairs. With the proposed joint resource allocation algorithm, the maximum throughput can be obtained with the lower algorithm complexity. Also, the robustness against the changing channel gains is guaranteed and the outage probability of users is kept below the pre-given outage threshold.

Joint resource allocation for dynamic cellular‐enabled UAVs communication

Emerging cellular-enabled unmanned aerial vehicles (UAVs) communication poses both opportunities and challenges to the current research of UAV applications. On the one hand, the advanced cellular technologies and authentication mechanisms make significant performance improvements of reliability, security, coverage, and throughput of UAVs possible. On the other hand, the considerably dynamic characteristics of the new aerial users bring some complicated and open issues to the future heterogeneous network. To throw some light on this field, the authors study the joint power allocation and channel reuse problem of uplink transmission in a cellular-enabled UAVs network with full consideration of the rapid channel variations resulting from high mobility of UAVs. Given the diverse requirements of terrestrial cellular users (CUs) and UAVs, i.e. large capacity for CU links and ultra-reliability for UAV links, they formulate the problem as optimising the uplink capacities of CUs with reliable transmission constraint of UAVs. By decoupling the intricate problem into two sub-problems, a joint resource allocation algorithm is proposed, which relies only on the statistical information of dynamic channels to implement, hence is robust. Finally, numerical results are provided to corroborate the anticipated performances of the proposed scheme.