Centralized Resource Allocation Algorithm Research Articles

Resource Allocation for D2D Cellular Networks With QoS Constraints: A DC Programming- Based Approach

Device-to-device (D2D) communications provide efficient ways to increase spectrum utilization ratio with reduced power consumption for proximity wireless applications. In this paper, we investigate resource allocation strategies for D2D communications underlaying cellular networks. To be specific, we study the centralized resource allocation algorithm for controlling transmit powers of the underlying D2D pairs in order to maximize the weighted sum-rate while guaranteeing the quality of service (QoS) requirements for both D2D pairs and cellular users (CUs). A novel DC (difference of convex function) programming-based method, called alternative DC (ADC) programming, is introduced to effectively solve this complicated resource allocation problem. Through updating each D2D pair’s power alternatively, the QoS requirement for each D2D pair can be solvable and incorporated systematically to the introduced ADC programming framework. The simulation results show that the introduced ADC programming achieves the highest weighted sum-rate compared to the state-of-the-art methods while ensuring that the QoS of each D2D pair and CU are satisfied.

Joint offloading decision and resource allocation for mobile edge computing enabled networks

Mobile edge computing (MEC) based solutions are essential and of great significance for a wide range of promising 5G wireless big data services such as remote healthcare systems and AR/VR games. Present research in this area focuses on the downlink resource allocation scenarios from MEC servers to user equipments (UEs). This paper considers a multi-user MEC-enabled wireless communication system, where UEs suffer limited communication and computation resources. To achieve higher energy efficiency and the better experience for UEs, we aim to maximize the number of offloaded tasks for all UEs in uplink communication while maintaining the computation resources of MEC at an acceptable level. The formulated problem is an NP-hard mixed-integer nonlinear programming problem and it is a challenge to solve it efficiently. As such, an efficient low-complexity heuristic algorithm is proposed, which provides a near-optimal solution with a low time cost. The results show that the proposed scheme achieves the higher number of successful offloaded tasks than the existing centralized resource allocation algorithm (CRAA) and centralized decision and resource allocation algorithm that UEs with the largest saved energy consumption accepted first (CAR-E) under different scenarios. Moreover, the relationship between the optimal transmission power and the computation resource of MEC is investigated. The results obtained in this paper can be extended to design a novel framework of communication, computation and smart coded caching MEC networks.

Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities.

With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution.

Discrete Power Control and Transmission Duration Allocation for Self-Backhauling Dense mmWave Cellular Networks

Wireless self-backhauling is a promising solution for dense millimeter wave (mmWave) small cell networks, the system efficiency of which, however, depends upon the balance of resources between the backhaul link and access links of each small cell. In this paper, we address the discrete power control and non-unified transmission duration allocation problem for self-backhauling mmWave cellular networks, in which each small cell is allowed to adopt individual transmission duration allocation ratio according to its own channel and load conditions. We first formulate the considered problem as a non-cooperative game ${\mathcal {G}}$ with a common utility function. We prove the feasibility and existence of the pure strategy Nash equilibrium (NE) of game $ {\mathcal {G}}$ under some mild conditions. Then, we design a centralized resource allocation algorithm based on the best response dynamic and a decentralized resource allocation algorithm (DRA) based on control-plane/user-plane split architecture and log-linear learning to obtain a feasible pure strategy NE of game ${\mathcal {G}}$ . For speeding up convergence and reducing signaling overheads, we reformulate the considered problem as a non-cooperative game ${{\mathcal {G}}'}$ with local interaction, in which only local information exchange is required. Based on DRA, we design a concurrent DRA to obtain the best feasible pure strategy NE of game ${\mathcal {G}'}$ . Furthermore, we extend the proposed algorithms to the discrete power control and unified transmission duration allocation optimization problem. Extensive simulations are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.

Erratum to: Joint subcarrier and power allocation for downlink cellular OFDMA systems by intercell interference limitation

This paper addresses resource allocation for sum throughput maximization in a sectorized two-cell downlink orthogonal frequency-division multiple-access (OFDMA) system impaired by multicell interference. It is well known that the optimization problem for this scenario is NP-hard and combinational, which is here converted to a novel sum throughput maximization problem in cellular OFDMA systems based on the intercell interference limitation. Then, three subclasses of this new problem are solved. By the first subclass, on the assumption that subcarrier allocation parameters are fixed, an algorithm for optimal power allocation is obtained. However, the optimal resource allocation requires an exhaustive search, including the optimal power allocation which cannot be implemented in practice due to its high complexity. By the second subclass, the problem is reduced to a single cell case where the intercell interference in each subcarrier is limited to a certain threshold. Based on the solution of the single cell problem, a distributed resource allocation scheme with the aim of small information exchange between the coordinated base stations is proposed. By the third subclass, the centralized resource allocation for two adjacent cells as a general problem is solved. Here, the algorithm allocates simultaneously the subcarriers and the power of the considered two cells while the resource allocation parameters of both cells are coupled mutually. The present study shows that distributed and centralized resource allocation algorithms have much less complexity than the algorithm used in the exhaustive search and can be used in practice as efficient multicell resource allocation algorithms. Simulation results illustrate the performance improvements of the proposed schemes in comparison to the schemes with no intercell interference consideration.

A Power Allocation Algorithm Based on Cooperative Game Theory in Multi-cell OFDM Systems

A centralized resource allocation algorithm in multi-cell OFDM systems is studied, which aims at improving the performance of wireless communication systems and enhancing user’s spectral efficiency on the edge of the cell. The proposed resource allocation algorithm can be divided into two steps. The first step is sub-carrier allocation based on matrix searching in single cell and the second one is joint power allocation based on cooperative game theory in multi-cell. By comparing with traditional resource allocation algorithms in multi-cell scenario, we find that the proposed algorithm has lower computational complexity and good fairness performance.

Iterative Resource Allocation for Maximizing Weighted Sum Min-Rate in Downlink Cellular OFDMA Systems

This paper considers the downlink of a cellular orthogonal frequency division multi-access (OFDMA) system, in which multiple base stations (BSs) are coordinated by a centralized resource allocation algorithm. We address the problem of maximizing the weighted sum of the minimal user rates (WSMR) of coordinated cells subject to a total power constraint at each BS, in terms of jointly optimizing coordinated BSs' subcarrier and power allocation. In particular, the solution of this problem corresponds to a resource allocation that guarantees similar rates to all users in each cell. An iterative algorithm is proposed to optimize the subcarrier allocation and the power allocation alternatively, so that the WSMR keeps increasing until convergence. In each iteration, the subcarrier allocation is updated by solving a mixed integer linear program for each cell, while the power allocation is updated by solving a successive set of convex optimization problems with a duality-based numerical algorithm. The effectiveness of the algorithm is illustrated by numerical experiments.

Joint Subchannel, Rate and Power Allocation in OFDMA-Based Cognitive Wireless Mesh Network

In this paper, the problem of resource allocation in an Orthogonal Frequency Division Multiple Access-based Cognitive Wireless Mesh Network (CWMN) is addressed. The objective is to maximize the total utilities in a CWMN, which is defined as any increasing, concave and twice differentiable function of the end-to-end flow rate, by jointly allocating each link's rate, power and subchannels under the constraints of multiple primary users' Interference Temperature and multiple access interference. First, a centralized resource allocation algorithm is developed based on the Column Generation approach, and shown to be optimal. So it can perform as a criterion for designing other algorithms. Secondly, considering the applicability of algorithm in distributed system, a near-optimal distributed algorithm is proposed, which allocates subchannel based on routing information at first, and then jointly allocates the resource of rate and power. Finally, the simulation results validate the centralized and distributed algorithms, and show that better performance can be achieved than the conventional algorithm.

Cooperation Strategies and Resource Allocations in Multiuser OFDMA Systems

Cooperative communication and orthogonal frequency-division multiplexing (OFDM) technology are both promising candidates for next-generation wireless communication systems. In this paper, we investigate cooperation strategies and resource-allocation algorithms for multiuser cooperative orthogonal frequency-division multiple-access (OFDMA) systems. There are two major contributions of this paper. First, we propose a novel cooperation strategy for the OFDMA system. When there are two users in the system, the strategy achieves the capacity region upper bound for decode-and-forward (DF) cooperation. When there are multiple users in the system, the strategy can achieve near upper-bound performance in certain network topologies. Moreover, the strategy always outperforms those that are previously proposed for cooperative OFDMA systems. The second contribution is that we develop novel centralized resource-allocation algorithms for cooperative OFDMA systems. The advantages of the algorithms are that they not only simultaneously solve relay, power, and subcarrier allocation but also have complexity that increases only linearly with the number of subcarriers in the systems.

Distributed resource allocation for DS-CDMA-based multimedia ad hoc wireless LANs

Power control in direct sequence code division multiple access (DS-CDMA) systems and power/rate allocation in multirate DS-CDMA based networks is an open and interesting research area which has attracted much attention. However, with a few exceptions, most researchers have emphasized centralized resource allocation algorithms for cellular systems where the base station keeps track of the requirements of the various users and is thus responsible for the management of network resources. Ad hoc wireless local area networks (WLANs), on the other hand, are generally configured as peer-to-peer networks with no centralized hub or controller. Thus resource allocation has to be conducted in a distributed fashion. We address the issue of distributed resource management for multirate DS-CDMA based multimedia WLANs by (1) presenting a distributed resource allocation protocol, known as distributed resource negotiation protocol (DRNP) that builds on the RTS/CTS bandwidth reservation mechanism provided by IEEE 802.111, and provides quality of service (QoS) guarantees through distributed control of resources in DS-CDMA based multimedia WLANs and (2) investigating the performance of various resource allocation schemes within the context of DRNP, in terms of network wide metrics such as overall throughput and blocking rates.