Gibbs sampling based distributed OFDMA resource allocation

In this article, we present a distributed resource and power allocation scheme for muRip]e-resource wireless cellular networks. The global optimization of multi-cell multi-link resource allocation problem is known to be NP-hard in the general case. We use Gibbs sampling based algorithms to perform a distributed optimization that would lead to the global optimum of the problem. The objective of this article is to show how to use the Gibbs sampling （GS） algorithm and its variant the Metropolis-Hastings （MH） algorithm. We also propose an enhanced method of the MH algorithm, based on a priori known target state distribution, which improves the convergence speed without increasing the complexity. Also, we study different temperature cooling strategies and investigate their impact on the network optimization and convergence speed. Simulation results have also shown the effectiveness of the proposed methods.     

Utility-based bandwidth allocation algorithm for heterogeneous wireless networks

In next generation wireless network (NGWN), mobile users are capable of connecting to the core network through various heterogeneous wireless access networks, such as cellular network, wireless metropolitan area network (WMAN), wireless local area network (WLAN), and ad hoc network. NGWN is expected to provide high-bandwidth connectivity with guaranteed quality-of-service to mobile users in a seamless manner; however, this desired function demands seamless coordination of the heterogeneous radio access network (RAN) technologies. In recent years, some researches have been conducted to design radio resource management (RRM) architectures and algorithms for NGWN; however, few studies stress the problem of joint network performance optimization, which is an essential goal for a cooperative service providing scenario. Furthermore, while some authors consider the competition among the service providers, the QoS requirements of users and the resource competition within access networks are not fully considered. In this paper, we present an interworking integrated network architecture, which is responsible for monitoring the status information of different radio access technologies (RATs) and executing the resource allocation algorithm. Within this architecture, the problem of joint bandwidth allocation for heterogeneous integrated networks is formulated based on utility function theory and bankruptcy game theory. The proposed bandwidth allocation scheme comprises two successive stages, i.e., service bandwidth allocation and user bandwidth allocation. At the service bandwidth allocation stage, the optimal amount of bandwidth for different types of services in each network is allocated based on the criterion of joint utility maximization. At the user bandwidth allocation stage, the service bandwidth in each network is optimally allocated among users in the network according to bankruptcy game theory. Numerical results demonstrate the efficiency of the proposed algorithm.     

Efficient ID-Based Multi-Decrypter Encryption with Short Ciphertexts

Multi-decrypter encryption is a typical application in multi-user cryptographic branches. In multi-decrypter encryption, a message is encrypted under multiple decrypters＇ public keys in the way that only when all the decrypters cooperate, can the message be read. However, trivial implementation of multi-decrypter encryption using standard approaches leads to heavy computation costs and long ciphertext which grows as the receiver group expands. This consumes much precious bandwidth in wireless environment, such as mobile ad hoc network. In this paper, we propose an efficient identity based multi-decrypter encryption scheme, which needs only one or zero （if precomputed） pairing computation and the ciphertext contains only three group elements no matter how many the receivers are. Moreover, we give a formal security definition for the scheme, and prove the scheme to be chosen ciphertext secure in the random oracle model, and discuss how to modify the scheme to resist chosen ciphertext attack.     

A suboptimal joint bandwidth and power allocation for cooperative relay networks＂ a cooperative game theoretic approach

In commercial networks, user nodes operating on batteries are assumed to be selfish to consume their resources (i.e., bandwidth and power) solely maximizing their own benefits (e.g., the received signal-to-noise ratios (SNRs) and datarates). In this paper, a cooperative game theoretical framework is proposed to jointly perform the bandwidth and power allocation for selfish cooperative relay networks. To ensure a fair and efficient resource sharing between two selfish user nodes, we assume that either node can act as a source as well as a potential relay for each other and either node is willing to seek cooperative relaying only if the datarate achieved through cooperation is not lower than that achieved through noncooperation (i.e., direct transmission) by consuming the same amount of bandwidth and power resource. Define the cooperative strategy of a node as the number of bandwidth and power that it is willing to contribute for relaying purpose. The two node joint bandwidth and power allocation (JBPA) problem can then be formulated as a cooperative game. Since the Nash bargaining solution (NBS) to the JBPA game (JBPAG) is computationally difficult to obtain, we divide it into two subgames, i.e., the bandwidth allocation game (BAG) and the power allocation game (PAG). We prove that both the subgames have unique NBS. And then the suboptimal NBS to the JBPAG can be achieved by solving the BAG and PAG sequentially. Simulation results show that the proposed cooperative game scheme is efficient in that the performance loss of the NBS result to that of the maximal overall data-rate scheme is small while the maximal-rate scheme is unfair. The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.     

Efficient resource allocation scheme for multi-service based on interference mitigation in LTE-Advanced networks

The exponential growth of various services demands the increased capacity of the next-generation broadband wireless access networks, which is toward the deployment of femtocell in macrocell network based on orthogonal frequency division multiple access. However, serious time-varying interference obstructs this macro/femto overlaid network to realize its true potential. In this article, we present a macro services guaranteed resource allocation scheme, which can mitigate various dominant interferences and provide multiple services in macro/femto overlaid Third-Generation Partnership Project Long Term Evolution-Advanced networks. We model our multiple services resource allocation scheme into a multiobjective optimization problem, which is a non-deterministic polynomial-time （NP）-hard problem. Then, we give a low-complexity algorithm consisting of two layers based on chordal graph. Simulation results verify that the proposed scheme can achieve better efficiency than the previous works and raise the satisfaction ratio of Guaranteed Bit Rate （GBR） services while improving the average performance of non-GBR services.     

Access point selection in heterogeneous wireless networks using belief propagation

The next generation wireless network will be composed by various heterogenous wireless access networks,such as cellular network,worldwide interoperability for microwave access(WiMAX),wireless local area network(WLAN),etc.Different access networks cooperatively provide high-bandwidth connectivity with bandwidth guarantees.This paper proposes a utility-based access point selection scheme,which selects an accessible point for each user,such that the bandwidth requirement of each user is satisfied,and also the defined utility function is maximized.Due to the NP-complete nature of the problem,the existing proposals apply the greedy method to find a solution.We find that belief propagation is an efficient tool to solve this problem,and thus,we derive the same optimization objective in a new way,and then draw a factor graph representation which describes our combinatorial optimization problem.Afterwards,we develop the belief propagation algorithm,and show that our algorithm converges.Finally,we conduct numerical experiments to evaluate the convergency and accuracy of the belief propagation in load balancing problem.     

Communication network designing:Transmission capacity,cost and scalability

Despite the growing number of works centering around the traffic dynamics on complex networks,these researches still have some common shortcomings,e.g.,too simple traffic flow model and lack of considerations for the designing cost and scalability issues.This paper builds on a more realistic traffic flow model,and offers a holistic view on the network designing problem.In addition to the extensively studied transmission capacity,this paper takes designing cost and scalability as two other designing objectives,and presents a quantitative study of how different designing choices independently and collectively influence these objectives by the introduction of a cartesian coordinate system.It is shown that different kinds of network topologies display different shapes of achievable solution spaces and exhibit different abilities to achieve cost-effective and scalable designing.In particular,we find the philosophy underlying empirical network designing and engineering today fails to meet the cost-effective and scalable designing requirements,and propose a cost-effective and scalable designing scheme for BA-like networks,i.e.,the efficient routing combined with effective betweenness based link bandwidth allocation.In addition,when designing a thoroughly new network from the beginning,we find that ER network is a good candidate to achieve cost-effective and scalable designing in most settings.     

Resource allocation algorithm with limited feedback for multicast single frequency networks

The single frequency network (SFN) can provide a multimedia broadcast multicast service over a large coverage area. However, the application of SFN is still restricted by a large amount of feedback. Therefore, we propose a multicast resource allocation scheme based on limited feedback to maximize the total rate while guaranteeing the quality of service (QoS) requirement of real-time services. In this scheme, we design a user feedback control algorithm to effectively reduce feedback load. The algorithm determines to which base stations the users should report channel state information. We then formulate a joint subcarrier and power allocation issue and find that it has high complexity. Hence, we first distribute subcarriers under the assumption of equal power and develop a proportional allocation strategy to achieve a tradeoff between fairness and QoS. Next, an iterative water-filling power allocation is proposed to fully utilize the limited power. To further decrease complexity, a power iterative scheme is introduced. Simulation results show that the proposed scheme significantly improves system performance while reducing 68% of the feedback overhead. In addition, the power iterative strategy is suitable in practice due to low complexity.     

Joint subcarrier,code, and power allocation for parallel multi-radio access in heterogeneous wireless networks

In the heterogeneous wireless networks, it has been proved that the joint spectrum and power allocation can achieve network diversity gains for parallel multi-radio access in theory. This article aims to develop an effective and practical algorithm of joint subearrier, code, and power allocation for parallel multi- radio access of the downlink in heterogeneous wireless networks （e.g., CDMA and OFDMA）. Firstly, we propose a unified framework to formulate the subcarrier, code, and power allocation as an optimization problem. Secondly, we propose a resource element （subcarrier and code） scheme based on the threshold type. Simulation results show that the proposed scheme outperforms the existing algorithm for considered wireless scenarios.     

Convergence, stability and robustness analysis of the OFEX controller for high-speed networks

The OFEX (Optimal and Fully EXplicit) rate controller is able to provide not only the optimal bandwidth allocation but also the fully explicit congestion signal to sources. It exercises link-wise proportional fairness and network-wise max-min fairness, which enables the controller to feed the congestion signal back from the most congested link, instead of using the networkwise proportional fairness which feeds back the congestion signal summed along a flow path. As a result, the OFEX controller overcomes the drawbacks of the relatively explicit controllers that 1) bias the multi-bottlenecked users in terms of their source sending rate and convergence speed, 2) are not adaptable to varying link bandwidth and 3) can potentially incur large queue size upon congestion. In this paper, we first prove that the OFEX controller can converge to its equilibrium at least as fast as a geometric series in a link. Then we analyze the system stability with time delay in a single bottleneck network and show that the OFEX controller can achieve local stability under certain conditions. Besides, the robustness analysis of the OFEX controller shows how the dynamics of link bandwidth may affect the revenue of a link. Finally, with the OPNET modeler, we evaluate the performances of the OFEX controller and verify its effectiveness.     

异构无线网络负载均衡研究*

针对异构网络中的带宽分配及网络的负载均衡问题,提出了一种异构无线网络负载均衡的联合优化模型,该模型在一定条件下利用用户的带宽进行负载成本和网络效用的建立,并通过求解网络与用户的关联矩阵来得到带宽在网络中的优化分配方案,使异构无线网络达到负载均衡。仿真结果表明,联合优化模型的结果在最小化负载成本以及优化网络总效用性能之间,它让用户通过在不同网络间的切换使网络带宽优化分配、网络负载达到均衡。     

WSN中层次型拓扑控制与网络资源配置联合设计方法

综合考虑异构无线传感器网络中节点速率分配、簇的划分规则和链路层网络频带资源占用情况, 提出一种基于拓扑控制与资源优化分配的层次型路由算法. 在网络层, 该算法根据成员节点和簇首节点的速率分配机制建立节点流量平衡模型. 在链路层, 分析无线传感器网络频谱共享行为, 研究邻近用户间访问冲突的规避抑制模型, 重构网络频带资源. 通过引入带宽比例因子将可用频带划分成若干子带, 提高网络频带资源的利用效率. 本文基于跨层联合设计思路, 建立一个混合整数非线性规划问题,对异构无线传感器网络中拓扑控制和网络资源分配问题联合设计, 得到最优的分簇结果和资源分配方案. 最后, 在设定网络拓扑中评估性能, 仿真结果证实该算法在网络频带资源充分利用的同时, 可实现最优的簇首匹配和路由建立结果.     

宽带卫星网络中基于跨层设计的带宽分配研究

传统的带宽分配策略按照分层结构进行设计,集中在链路层解决问题,没有考虑动态变化的信道状态和所承载的数据流的QoS性能。基于跨层设计的思想,针对支持话音业务和Internet数据业务的卫星网络提出了一种基于跨层设计的带宽分配算法。该策略将应用层的业务特性和数据链路层的带宽分配策略以及物理层的信道状态进行跨层优化。主控站通过建立代价函数的方法将所有相关参数综合考虑,利用动态规划算法得到了最佳的带宽分配方案。数值结果表明：跨层设计方式可以适应变化的信道状态,并且同传统带宽分配算法比较,提高了Internet数据的有效吞吐量并且保持了话音业务的QoS要求。     

Fair allocation of utilities in multirate multicast networks: aframework for unifying diverse fairness objectives

We study fairness in a multicast network. We assume that different receivers of the same session can receive information at different rates. We study a fair allocation of utilities, where the utility of a bandwidth is an arbitrary function of the bandwidth. The utility function is not strictly increasing, nor continuous in general. We discuss fairness issues in this general context. Fair allocation of utilities can be modeled as a nonlinear optimization problem. However, nonlinear optimization techniques do not terminate in a finite number of iterations in general. We present an algorithm for computing a fair utility allocation. Using specific fairness properties, we show that this algorithm attains global convergence and yields a fair allocation in polynomial number of iterations     

Subcarrier allocation in multi-hop orthogonal frequency division multiple access wireless networks

Orthogonal frequency division multiplexing (OFDM) has been widely considered as a key technique for next generation mobile communication systems. Meanwhile, relaying technologies can improve users’ quality of service, increase network capacity and enlarge cellular coverage at a low cost. In this paper, we focus on subcarrier allocation and utilization in multi-hop OFDM access (OFDMA) wireless networks, and propose two efficient subcarrier allocation schemes aiming to increase network throughput and subcarrier utilization. The first scheme selects suitable links for data transmission from base stations to terminals at the beginning. Then, interference-free links are included into the same group for network resource reuse. For the purpose of global optimization, we propose a Tabu-based searching algorithm as the second subcarrier allocation scheme. Simulation results demonstrate that our proposed algorithms outperform other schemes in both network throughput and subcarrier utilization.     

大规模通信网络带宽优化分配问题研究

提出一种大规模通信网络带宽分配的新方法．作者将大系统理论中的分解—协调方法运用于解决大规模通信网络带宽分配的优化问题，大型网络的优化问题被分解成一些互相关联的小型子网的优化问题．整个带宽优化分配问题的解决分为三个阶段：分解、协调优化及合并优化．计算结果表明，与现有算法相比，分解—协调大规模带宽管理算法(DCLPBM)既保证了很高的计算精度，又降低了时间与空间复杂度．由于算法中用到的协调机制较简单，DCLPBM易于推广到分布式计算环境，从以网络来治理网络的角度看，它具有较广的前景．     

Achieving multipoint-to-multipoint fairness with RCNWA

In current Internet, applications employ lots of sessions with multiple connections between multiple senders and receivers. Sessions or users with more connections gain higher throughput. To obtain more network resource, applications tend to create more connections. It causes unfair bandwidth allocation by per-connection TCP rate allocation and the network suffers lots of TCP overheads. In this paper, we explore the issue on fair share allocation of bandwidth among sessions or users. Various fairness definitions are discussed in this study. Then, we propose a novel distributed scheme to achieve various fairness definitions. Simulation results show that our distributed scheme could achieve fair allocation according to each fairness definition.     

Generalized regression neural networks with multiple-bandwidth sharing and hybrid optimization.

This paper proposes a novel algorithm for function approximation that extends the standard generalized regression neural network. Instead of a single bandwidth for all the kernels, we employ a multiple-bandwidth configuration. However, unlike previous works that use clustering of the training data for the reduction of the number of bandwidths, we propose a distinct scheme that manages a dramatic bandwidth reduction while preserving the required model complexity. In this scheme, the algorithm partitions the training patterns to groups, where all patterns within each group share the same bandwidth. Grouping relies on the analysis of the local nearest neighbor distance information around the patterns and the principal component analysis with fuzzy clustering. Furthermore, we use a hybrid optimization procedure combining a very efficient variant of the particle swarm optimizer and a quasi-Newton method for global optimization and locally optimal fine-tuning of the network bandwidths. Training is based on the minimization of a flexible adaptation of the leave-one-out validation error that enhances the network generalization. We test the proposed algorithm with real and synthetic datasets, and results show that it exhibits competitive regression performance compared to other techniques.