Joint Radio Resource Allocation Research Articles

Joint Radio and Computational Resource Allocation for NOMA-Based Mobile Edge Computing in Heterogeneous Networks

A joint radio and computational resource allocation problem is investigated for non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) in heterogeneous networks (HetNets). The aim is to minimize the energy consumption of all users under the task execution latency constraint with partial task offloading. Due to the non-convexity of the formulated problem, we propose an iterative algorithm by decomposing the optimization problem into two subproblems, where the computational resource allocation is a convex optimization problem, and the uplink power allocation is solved by the sequential convex programming. Simulation results reveal that the partial offloading is superior to local computing-only and full offloading-only and NOMA-based MEC outperforms the orthogonal multiple access-based MEC in HetNets.

Joint Radio Resource Allocation and Control for Vehicle Platooning in LTE-V2V Network

To improve safety and efficiency of transportation system, an effective approach is vehicle platooning, in which a group of vehicles maintains a pre-defined moving pattern by minimizing tracking error of each vehicle. As a networked control process, platooning control relies on the cooperative awareness messages (CAM) periodically exchanged in the vehicle platoon. In meeting communication and control expectations, we propose to jointly optimize the radio resource allocation in LTE-vehicle-to-vehicle (V2V) network for the CAM transmission and control parameters of vehicle, in order to minimize the tracking error while guaranteeing the reliability requirements of V2V communication and string stability of platoon. To solve the formulated problem, we develop a sub-optimal solution. First, we present a tracking error based scheduling criterion and then obtain a radio resource allocation scheme by bipartite graph matching. Then, control parameters of vehicle are adapted to minimize the tracking error by heuristic gradient descent based method. Simulation results show that the proposed scheme can reduce the tracking error as well as ensure the platoon stability compared to the existing schemes.

Joint Radio and Computational Resource Allocation in IoT Fog Computing

The current cloud-based Internet-of-Things (IoT) model has revealed great potential in offering storage and computing services to the IoT users. Fog computing, as an emerging paradigm to complement the cloud computing platform, has been proposed to extend the IoT role to the edge of the network. With fog computing, service providers can exchange the control signals with the users for specific task requirements, and offload users’ delay-sensitive tasks directly to the widely distributed fog nodes at the network edge, and thus improving user experience. So far, most existing works have focused on either the radio or computational resource allocation in the fog computing. In this work, we investigate a joint radio and computational resource allocation problem to optimize the system performance and improve user satisfaction. Important factors, such as service delay, link quality, mandatory benefit, and so on, are taken into consideration. Instead of the conventional centralized optimization, we propose to use a matching game framework, in particular, student project allocation (SPA) game, to provide a distributed solution for the formulated joint resource allocation problem. The efficient SPA-(S,P) algorithm is implemented to find a stable result for the SPA problem. In addition, the instability caused by the external effect, i.e., the interindependence between matching players, is removed by the proposed user-oriented cooperation (UOC) strategy. The system performance is also further improved by adopting the UOC strategy.

Joint radio resource and power allocation using Nash bargaining game for H‐CRAN with nonideal fronthaul links

AbstractHeterogeneous cloud radio access network (H‐CRAN) is introduced as a novel network architecture that takes the full rewards of both heterogeneous cellular network (HCNs) and cloud radio access network (CRAN). In H‐CRAN architecture, the remote radio heads of small cells (SCs) are overlaid within the coverage of macro cell and linked to the baseband unit pool through nonideal fronthaul connections. To improve the spectral efficiency of this network, the SCs are developed to reuse the same resources used by the macro cell. However, this will introduce inter‐tier interference between macro cell and SCs and intra‐tier interference among SCs that need to be efficiently managed. In this paper, the joint problem of radio resource and power allocation in H‐CRAN is formulated using cooperative Nash bargaining game. This optimization problem is designed to maximize the total throughput of SCs, while considering the fairness between user equipments (UEs) in each SC, interference protection requirements for macro UEs, outage probability requirements, and the capacity limitation of nonideal fronthaul connections. Then, by using Lagrangian dual decomposition, the near‐optimal bargaining allocation strategy for radio resources and power of each SC UE is determined. Simulation results illustrate that the performance of the suggested algorithm outperforms the other comparable algorithm in terms of the SCs' achieved throughput and fairness. Moreover, the effect of nonideal fronthaul capacity constraint and the interference protection limit of macro UEs on the performance of the suggested scheme are examined.

Resource Allocation for Transmit Hybrid Beamforming in Decoupled Millimeter Wave Multiuser-MIMO Downlink

This paper presents a study on joint radio resource allocation and hybrid precoding in multi-carrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio $SNR\leq 10$ dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0–25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index.

Joint Radio Resource Allocation and Base Station Location Selection in OFDMA Based Private Wireless Access Networks for Smart Grid

This paper studies the base stations deployment problem in orthogonal frequency-division multiple access (OFDMA) based private wireless access networks for smart grid (SG). Firstly, we analyze the differences between private wireless access networks for SG and public cellular access networks. Then, we propose scheduling and power control based algorithms for the radio resource allocation subproblem and K-means, simulated annealing (SA), and particle swarm optimization (PSO) based algorithms for the base station (BS) location selection subproblem and iterate over these two sets of algorithms to solve the target problem. Simulation results show that the proposed method can effectively solve the target problem. Specifically, the combination of power control based resource allocation algorithm and PSO based location selection algorithm is recommended.

Receiver Concepts and Resource Allocation for OSC Downlink Transmission

Voice services over Adaptive Multi-user channels on One Slot (VAMOS) has been standardized as an extension to the Global System for Mobile Communications (GSM). The aim of VAMOS is to increase the capacity of GSM, while maintaining backward compatibility with the legacy system. To this end, the Orthogonal Sub-channels (OSC) concept is employed, where two Gaussian minimum-shift keying (GMSK) signals are transmitted in the same time slot and with the same carrier frequency. To fully exploit the possible capacity gain of OSC, new receiver concepts are necessary. In contrast to the base station, where multiple antennas can be employed, the mobile station is typically equipped with only one receive antenna. Therefore, the downlink receiver design is a very challenging task. Different concepts for channel estimation, user separation, and equalization at the receiver of an OSC downlink transmission are introduced in this paper. Furthermore, the system capacity must be improved by suitable downlink power and resource allocation algorithms. Making realistic assumptions on the information available at the base station, an algorithm for joint power and radio resource allocation is proposed. Simulation results show the excellent performance of the proposed channel estimation algorithms, equalization schemes, and joint radio resource and power allocation algorithms in realistic VAMOS environments.

Nash bargaining solution based joint time‐frequency‐code‐power resource management algorithm for TD‐LTE systems

SUMMARYThis paper presents a joint time‐frequency‐code‐power resource management algorithm based on the Nash bargaining solution in time‐division long term evolution systems. First, a joint radio resource allocation scheme at the time, frequency, code and power domain simultaneously is provided for the time‐division long term evolution system. Second, the proposed algorithm is modeled as a cooperative game under the constraints of each user's minimal rate requirement and available resources, for example, the maximal transmitting power. To reduce the computational complexity, the joint resource allocation algorithm is divided into time‐frequency‐code and power domain resource allocation. Also, we could approach the Pareto optimal rate as closely as possible by iterations. Simulation results show that compared with the other resource allocation algorithms, the proposed algorithm has achieved a good tradeoff between the overall system throughput and fairness among different users. Copyright © 2012 John Wiley & Sons, Ltd.

Joint time-frequency-power resource allocation for low-medium-altitude platforms-based WiMAX networks

Low-medium-altitude platforms (LMAPs) are being actively researched and developed as a key solution to improve the performance and services of emergency communications. In order to provide higher capacity, throughput and quality of service guarantee to territorial users in emergency scenarios, a LMAP-based WiMAX system, AirWiMAX, is presented in this paper. Firstly, a hierarchical AirWiMAX topology is presented. Secondly, a joint radio resource allocation is carried out simultaneously at the time, frequency and power domain for the AirWiMAX downlink. This problem is modelled as a cooperative game in which a fairness criterion is enforced. Simulation results show that compared to the other two typical resource allocation algorithms, that is, the max-rate algorithm and the max–min fairness algorithm, the proposed algorithm achieves a good trade-off between the overall system throughput and the fairness.