Resource Allocation for Wireless Fading Relay Channels: Max-Min Solution

Resource allocation is investigated for fading relay channels under separate power constraints at the source and relay nodes. As a basic information-theoretic model for fading relay channels, the parallel relay channel is first studied, which consists of multiple independent three-terminal relay channels as subchannels. Lower and upper bounds on the capacity are derived, and are shown to match, and thus establish the capacity for the parallel relay channel with degraded subchannels. This capacity theorem is further demonstrated via the Gaussian parallel relay channel with degraded subchannels, for which the synchronized and asynchronized capacities are obtained. The capacity-achieving power allocation at the source and relay nodes among the subchannels is partially characterized for the synchronized case and fully characterized for the asynchronized case. The fading relay channel is then studied, which is based on the three-terminal relay channel with each communication link being corrupted by a multiplicative fading gain coefficient as well as an additive Gaussian noise term. For each link, the fading state information is assumed to be known at both the transmitter and the receiver. The source and relay nodes are allowed to allocate their power adaptively according to the instantaneous channel state information. The source and relay nodes are assumed to be subject to separate power constraints. For both the full-duplex and half-duplex cases, power allocations that maximize the achievable rates are obtained. In the half-duplex case, the power allocation needs to be jointly optimized with the channel resource (time and bandwidth) allocation between the two orthogonal channels over which the relay node transmits and receives. Capacities are established for fading relay channels that satisfy certain conditions.     

Centralized and Distributed Algorithms for Routing and Weighted Max-Min Fair Bandwidth Allocation

Given a set of demands between pairs of nodes, we examine the traffic engineering problem of flow routing and fair bandwidth allocation where flows can be split to multiple paths (e.g., MPLS tunnels). This paper presents an algorithm for finding an optimal and global per-commodity max-min fair rate vector in a polynomial number of steps. In addition, we present a fast and novel distributed algorithm where each source router can find the routing and the fair rate allocation for its commodities while keeping the locally optimal max-min fair allocation criteria. The distributed algorithm is a fully polynomial epsilon-approximation (FPTAS) algorithm and is based on a primal-dual alternation technique. We implemented these algorithms to demonstrate its correctness, efficiency, and accuracy.      

An Algorithm for Optimal Resource Allocation in Cellular Networks with Elastic Traffic

In this letter we propose a power allocation iteration which optimizes the weighted aggregate performance of a single-hop network. We show that the proposed iteration is a competitive alternative to conventional gradient iterations in terms of convergence and computational effort.     

面向MTC应用的计算资源柔性分配

基于机器类通信(MTC)业务的速率需求和计算需求,研究了对移动通信网络计算资源分配方法,给出了计算资源分配模型,提出了一种基于组合优化的计算资源分配算法来解决计算资源受限的问题。仿真结果表明提出的次优化算法与传统的轮询方式相比可以获得约10%的增益。     

LTE-Advanced中继系统中一种公平的下行资源分配机制

LTE-Advanced( LTE-A)系统引入中继后,带来了如提高频谱效率、拓展网络覆盖等优点,但是也给资源分配带来了更多的挑战.针对LTE-A中继系统的下行资源分配问题,在资源足够使用的情况下,提出一种在eNB和中继功率受限且用户数据速率限制的条件下,以权衡系统吞吐量和用户公平性为目标的资源分配机制.通过仿真验证表明,该算法较好地权衡了系统吞吐量和用户公平性,并且能满足用户速率要求.     

Fair Radio Resource Allocation for MIMO OFDM-based Multicast Systems

This paper investigates the problem of resource allocation in a multiple-input multiple-output (MIMO) OFDM-based system, wherein multiple multicast groups exist. Multicasting is a transmission technique which enables a transmitter to communicate via a single wireless link with multiple receivers simultaneously. Moreover, the presence of multiple antennas in both transmitter and receiver enhances significantly the system spectral efficiency. MIMO technology along with multicasting offers major advantages to wireless systems. However, optimum exploitation of these technologies adds significant complexity to the system which makes very difficult any possible practical implementation. Another important issue of such systems is their capacity to ensure to all users a certain level of QoS. To that end, we propose a low complexity fair resource allocation algorithm aiming at ensuring a certain amount of resources to all users when multicasting is applied. Validation of the proposed solution is achieved through extensive simulation and it is compared to other multicast schemes for MIMO systems which exist in literature. Numerical results and complexity analysis show the feasibility of the proposed algorithm.     

Optimal Resource Allocation for Two-Way Relay-Assisted OFDMA

This paper studies the resource allocation problem for the relay-assisted orthogonal frequency-division multiple-access (OFDMA)-based multiuser system. A new transmission protocol, named hierarchical OFDMA, is proposed to support two-way communications between the base station (BS) and each mobile user (MU) with or without an assisting relay station (RS) in “relay” or “direct” mode, respectively. In particular, the recently discovered two-way relaying technology, based on the principle of network coding, is applied to MUs in relay mode with two possible relay-operations, namely, decode-and-forward (DF) and amplify-and-forward (AF). By applying convex optimization techniques, efficient algorithms are developed for optimal allocation of transmit resources such as power levels, bit rates, and OFDM subcarriers at the BS, RSs, and MUs. Simulation results show that substantial system throughput gains are achievable by the proposed two-way relaying and optimal resource allocation schemes over the traditional one-way relaying and fixed resource allocation schemes for relay-assisted OFDMA-based wireless networks.      

MIMO-MC-CDMA中基于拉氏算子的资源分配算法

提出一种应用于多输入多输出MC-CDMA系统的功率、码道自适应分配算法。基于接收端的信道反馈信息,发送端在总发射功率受限的条件下通过多用户间功率和码道的分配最大化系统吞吐量。算法通过限制用户的最大和最小码道数来实现用户之间的带宽公平性。算法最终归结为一个约束优化问题并利用拉格朗日乘子法进行求解。提出一种用于加快拉格朗日乘子收敛速度的搜索算法,计算机仿真验证了算法的有效性。     

Active Queue Management for Fair Resource Allocation in Wireless Networks

This paper investigates the interaction between end-to-end flow control and medium access control (MAC)-layer scheduling on wireless links. We consider a wireless network with multiple users receiving information from a common access point; each user suffers fading and a scheduler allocates the channel based on channel quality but is subject to fairness and latency considerations. We show that the fairness property of the scheduler is compromised by the transport-layer flow control of transmission control protocol (TCP) New Reno. We provide a receiver-side control algorithm, CLAMP, that remedies this situation. CLAMP works at a receiver to control a TCP sender by setting the TCP receiver's advertised window limit, and this allows the scheduler to allocate bandwidth fairly between the users.     

一种支持公平交易的网格计算经济模型

针对网格计算中的资源管理，提出了一种具有公平交易属性的计算经济模型。通过价格监管、公平调度、信誉度和运行监测等方法对日用品市场模型进行控制和约束，从而使得网格资源的市场交易变得稳定、有序且高效。仿真实验结果表明，和传统经济模型比较，模型在任务完成时间、系统吞吐量和稳定性等方面都有所提高。     

Joint Computing and Communication Resource Allocation for Satellite Communication Networks with Edge Computing

Benefit from the enhanced onboard pro-cessing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising te...     

Intelligent Energy-Efficient Resource Allocation for Multi-UAV-Assisted Mobile Edge Computing Networks

The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC) has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements, especially in some infrastructure-limited areas or some emergency scenarios. However, the multi-UAVassisted MEC network remains largely unexplored. In this paper, the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users. By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs' trajectories, user association, and subchannel assignment, the average long-term sum of the user energy consumption minimization problem is formulated. To address the problem involving both discrete and continuous variables, a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed, named HDRT algorithm,where deep Q network(DQN) and deep deterministic policy gradient(DDPG) are invoked to process discrete and continuous variables, respectively. Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.     

机会频谱接入中的最优资源分配

感知无线电(CR)网络中的机会频谱接入(OSA)可以有效提高频谱的利用率。研究了OSA中的信道分配和功率控制问题,通过将Overlay模式和Underlay模式相结合,求解了次用户吞吐量的最优化问题。在满足干扰温度约束的前提下,优化目标为所有次用户的带权速率之和,同时可提供QoS支持和最小速率保障。此优化问题可转化为二部图最大带权匹配问题并使用匈牙利方法进行求解。仿真结果显示,采用混合模式的信道分配和功率控制算法较单一模式可以获得更大的频谱资源利用率。     

Fair Adaptive Cross-Layer Resource Allocation Scheme for IEEE 802.16 Broadband Wireless Networks

In a WiMAX network, the Medium Access Control (MAC) protocol deals with resource allocation to different types of traffic. The key components that ensure Quality of Service (QoS) guarantees in a WiMAX network include Call Admission Control (CAC), Bandwidth and Burst allocation. In this Paper, a Cross-layer framework is designed to efficiently allocate resources to various classes of traffic. CAC and Bandwidth allocation are dealt in the MAC layer, while Burst allocation in the PHYsical layer. The predominant goal of this work is to reduce delay and Information Element (IE) overheads by efficiently utilizing the available frame space. The History based CAC (HCAC) proposed in this paper deals with call acceptance based on the Contention Window (CW) values. The History based Bandwidth Allocation (HBA) scheme deals with allocating bandwidth based on Consumption and Equity measures. The proposed tightly coupled Delay Tolerance based Scheduler (DTS) and Bucket based Burst Allocator (BBA) allocate resources by prioritizing flows with least delay tolerance. It is seen that the proposed schemes offer better performance in contrast to the existing benchmarked schemes in terms of Throughput, Average Delay and Packet Loss Ratio (PLR).

     

Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network

Proportional fair resource allocation plays a critical role to balance the spectrum efficiency and fairness for cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to the lack of cooperation between cognitive radio (CR) network and primary network, channel state information between CR base station (CRBS) and primary user (PU) could not be estimated precisely. Therefore, the interference of CRBS–PU couldn’t be computed precisely and chance-constrained programming is adopted to formulate the resource allocation problem. In this work, we study the proportional fair resource allocation problem based on chance-constrained programming for cognitive OFDM network. The objective function maximizes the spectral efficiency of cognitive OFDM network over subcarrier and power allocation. The constraint conditions include the interference constraint of PU with the target probability requirement and the proportional fair rate requirement of CR users. In order to solve the above optimization problem, two steps are taken to develop hybrid immune optimization algorithm (HIOA). In the first step, the probabilistic interference constraint condition is transformed as an uncertain function which is computed by a generalized regression neural network (GRNN). In the second step, we combine immune optimization algorithm and GRNN to develop HIOA. Simulation results demonstrate that HIOA yields higher spectral efficiency while the probabilistic interference constraint condition and the proportional fair rate constraint condition could be satisfied very well.     

Max-Min Fair Pre-DFT Combining for OFDM Systems With Multiple Receive Antennas

For orthogonal frequency-division multiplexing (OFDM) systems equipped with multiple receive antennas, conventional pre-discrete Fourier transform (DFT) combining cannot balance the signal-to-noise ratio (SNR) performance of all subcarriers. This degrades the whole system performance. In this paper, we propose the use of the max-min fair criterion for pre-DFT combining to solve the problem. Semidefinite relaxation (SDR) is employed to approximate the solution. We also present a simple way to effectively reduce the amount of computation without sacrificing much of the performance. Simulation results show that, for both uncoded and coded OFDM systems, the proposed max-min fair pre-DFT combining solved via SDR can outperform conventional pre-DFT combining under various multipath channel environments with affordable computational complexity.     

Load-Balancing Based Cross-Layer Elastic Resource Allocation in Mobile Cloud

The paper proposes a hybrid mobile cloud computing system, in which mobile applications can use different resources or services in local cloud and remote public cloud such as computation, storage and bandwidth. The cross-layer load-balancing based mobile cloud resource allocation optimization is proposed. The proposed approach augments local cloud service pools with public cloud to increase the probability of meeting the service level agreements. Our problem is divided by public cloud service allocation and local cloud service allocation, which is achieved by public cloud supplier, local cloud agent and the mobile user. The system status information is used in the hybrid mobile cloud computing system such as the preferences of mobile applications, energy, server load in cloud datacenter to improve resource utilization and quality of experience of mobile user. Therefore, the system status of hybrid mobile cloud is monitored continuously. The mathematical model of the system and optimization problem is given. The system design of load-balancing based cross-layer mobile cloud resource allocation is also proposed. Through extensive experiments, this paper evaluates our algorithm and other approaches from the literature under different conditions. The results of the experiments show a performance improvement when compared to the approaches from the literature.

     

Resource Allocation for Downlink Cellular OFDMA Systems—Part I: Optimal Allocation

In this pair of papers (Part I and Part II in this issue), we investigate the issue of power control and subcarrier assignment in a sectorized two-cell downlink OFDMA system impaired by multicell interference. As recommended for WiMAX, we assume that the first part of the available bandwidth is likely to be reused by different base stations (and is thus subject to multicell interference) and that the second part of the bandwidth is shared in an orthogonal way between the different base stations (and is thus protected from multicell interference). Although the problem of multicell resource allocation is nonconvex in this scenario, we provide in Part I the general form of the global solution. In particular, the optimal resource allocation turns out to be “binary” in the sense that, except for at most one pivot-user in each cell, any user receives data either in the reused bandwidth or in the protected bandwidth, but not in both. The determination of the optimal resource allocation essentially reduces to the determination of the latter pivot-position.      

Fair Delay Optimization-based Resource Allocation Algorithm for Video Traffic over Wireless Multimedia System

The major issue related to the realization of wireless multimedia system is the design of suitable medium access control (MAC) protocol. The design challenge is to maximize the utilization of the limited wireless resources while guaranteeing the various quality of service requirements for all traffic classes especially for the stringent real-time constraint of real time variable bit rate (rt-VBR) video service. In this paper a novel resource allocation algorithm for video traffic is proposed. The proposed allocation algorithm aims to provide fair delay for video packets by minimizing the delay difference among transmitted video packets. At the same time it adaptively controls the allocated resources (bandwidth) for video traffic around the corresponding average bit rate, and has the ability of controlling the quality of service (QoS) offered for video traffic in terms of packet loss probability and average delay. A minimized control overhead of only two bits is needed to increase the utilization efficiency. Simulation results show that the proposed algorithm achieves very high utilization and provides nearly fair delay among video packets. Its efficiency is also investigated under traffic integration condition with voice and data traffic to show that the QoS offered to video traffic does not change in the presence of the highest priority voice traffic while data traffic increases the channel utilization to 98% by using the remaining bandwidth after voice and video traffic while a good QoS is offered to voice and data traffic.