蜂窝系统中一种固定信道分配方法的研究

信道分配问题属于组合优化中的 NP完备问题 ,为此在遗传算法的基础上提出了一种新的固定信道分配方法 .目标是得到一个用最少的信道数 ,满足话务需求和电磁兼容限制的信道分配方案 .采用了最小间隔编码方案和选择性变异技术 ,利用固定遗传算子 (交叉和变异 ) ,在整个迭代过程中始终满足话务需求的要求 .通过对几个著名的benchmark问题进行的仿真结果表明 ,它对解决信道分配问题是一个非常有效的方法     

GEO卫星通信系统中MF-TDMA信道分配算法研究

提出了GEO卫星通信系统中MF-TDMA信道分配算法C-BFD。与传统信道资源分配算法相比,C-BFD采用了基于优先组合的方式,利用用户信道请求大小以及信道结构的特点,通过分配过程中对用户信道请求进行组合并优先分配,来减少时隙碎片的产生。仿真结果表明,该算法能够有效降低系统阻塞率以及信道碎片对系统资源利用率的负面影响。     

TD-SCDMA系统中动态信道分配技术研究

作为第三代移动通信系统的标准之一,时分-同步码分多址(TD-SCDMA)系统由于采用了时分双工(TDD)方式而在性能上显示出了不同于其他两个主流标准的优势,它能更充分满足日益兴起的不对称传输服务的需求。动态信道分配(DCA)技术是TD-SCDMA系统中的一种关键技术,它从业务服务的实际情况和趋势出发,更合理地分配信道,缓解频谱资源的紧张状态。本文主要是在现有的动态信道分配技术的基础上提出一种改进算法,通过仿真分析确认该改进算法可以减少数据掉包概率,使得系统资源得到更好的利用,更好的提高无线频谱利用率,防止网络阻塞。     

TDD/CDMA系统动态信道分配技术研究

该文在分析TD-SCDMA系统无线资源管理机制的基础上,对该系统的动态信道分配算法进行研究。首先对现有的TDD-CDMA系统中的各种动态信道分配算法进行分类总结和分析比较,并在此基础了给出针对TD-SCDMA系统的慢速信道分配算法、快速信道分配算法以及考虑空间分布信息的信道分配方案,阐明了各自的特点和适用范围。研究了在TDD CDMA系统中如何进一步提高信道利用率的问题。     

MF-TDMA信道分配研究

MF-TDMA因其灵活的分配策略和信道利用率在通信系统中广泛使用。由于信道资源有限性以及用户需求的多变性,使得如何将有限的信道最大的利用以便为更多的用户服务成为MF-TDMA的关键问题。针对这一问题,文章首先对MF-TDMA信道结构进行处理,将MF-TDMA的信道分配问题转为二维装箱问题。然后,针对该问题的常规算法FFA进行改进,提出了基于最小资源浪费率的FFA贪心算法（IFFA）。最后,对给出的结果进行了简单的验证。     

TD—SCDMA系统中动态信道分配技术研究

作为第三代移动通信系统的标准之一,时分一同步码分多址（TD—SCDMA）系统由于采用了时分双工（TDD）方式而在性能上显示出了不同于其他两个主流标准的优势,它能更充分满足日益兴起的不对称传输服务的需求。动态信道分配（DCA）技术是TD—SCDMA系统中的一种关键技术,它从业务服务的实际情况和趋势出发,更合理地分配信道,缓解频谱资源的紧张状态。本文主要是在现有的动态信道分配技术的基础上提出一种改进算法,通过仿真分析确认该改进算法可以减少数据掉包概率,使得系统资源得到更好的利用,更好的提高无线频谱利用率,防止网络阻塞。     

可变频宽无线网络中信道频谱分配与链路调度

考虑信道频宽对链路传输距离和链路间干扰的影响,对可变频宽无线网络现有的累积干扰模型进行了改进,并基于改进的干扰模型对可变频宽无线网络的信道频谱分配和链路调度问题进行了建模分析。设计了一种两层优化算法对信道频谱分配和链路调度问题进行解耦,提出了一种考虑链路负载需求满足程度的链路优先级指标,启发式地构建并发传输信道分配矩阵的方法。仿真结果表明,两层优化算法能够在合理时间内收敛,启发式方法能够高效地构建并发传输信道分配矩阵。     

OFDMA中继系统中比例公平调度算法研究

研究网络资源调度优化问题,中继技术是发展网络的关键技术。传统的资源调度算法中,部分比例公平调度(PPF)与两跳比例公平调度(THPF)均有不足,PPF算法能获得较高的系统吞吐率,但不能保证用户的公平性,THPF算法则相反。为了解决系统同时获得吞吐率与用户公平性问题,根据THPF算法设计了一种基于最少好信道优先的两跳比例公平调度算法(S-THPF),通过优先给好信道较少的用户分配信道资源,从而保证尽可能多的用户获得最优的信道。仿真表明算法在提高系统吞吐率的同时能满足用户公平性要求。     

基于信道聚合的OFDM自适应资源分配算法

针对OFDM系统中多用户自适应资源分配问题,提出了一种新的基于信道聚合和遗传算法的自适应资源分配算法。该算法首先将所有子载波均匀分组,将子载波分配问题转化成子载波组分配问题。然后,兼顾用户之间的公平性,引入遗传算法,实现用户间的资源分配。仿真结果表明,与现有的算法相比,新算法在保证用户公平性的同时,有效地降低了计算复杂度和获得系统最优解的计算时间,实现系统总的容量最大化。     

改进人工蜂群算法在信道分配上的应用

移动通信网络中频率资源是有限的,为了提高无线资源的利用率,将改进的人工蜂群算法用于解决无线信道分配问题。提出的算法用逐步减小邻域搜索范围的动态步长来均衡局部与全局搜索能力;对单个体引入选择性变异技术,增加了种群的多样性,加快了算法的收敛速度。仿真结果表明,改进后的算法能较好地解决无线信道分配问题,提高了算法的收敛率和收敛速度。     

A novel global harmony search algorithm for task assignment problem

The objective of task assignment problem (TAP) is to minimize the sum of interprocessor communication and task processing costs for a distributed system which subjects to several resource constraints. We use a novel global harmony search algorithm (NGHS) to solve this problem, and the NGHS algorithm has demonstrated higher efficiency than the improved harmony search algorithm (IHS) on finding the near optimal task assignment. We also devise a new method called normalized penalty function method to tradeo^® the costs and the constraints. A large number of experiments show that our algorithm performs well on finding the near optimal task assignment, and it is a viable approach for the task assignment problem.     

A Lagrangian Approach for Multiple Personalized Campaigns

The multicampaign assignment problem is a campaign model to overcome the multiple-recommendation problem that occurs when conducting several personalized campaigns simultaneously. In this paper, we propose a Lagrangian method for the problem. The original problem space is transformed to another simpler one by introducing Lagrange multipliers, which relax the constraints of the multicampaign assignment problem. When the Lagrangian vector is supplied, we can compute the optimal solution under this new environment in O(NK2) time, where N and K are the numbers of customers and campaigns, respectively. This is a linear-time method when the number of campaigns is constant. However, it is not easy to find a Lagrangian vector in exact accord with the given problem constraints. We thus combine the Lagrangian method with a genetic algorithm to find good near-feasible solutions. We verify the effectiveness of our evolutionary Lagrangian approach in both theoretical and experimental viewpoints. The suggested Lagrangian approach is practically attractive for large-scale real-world problems.     

Continuous spatial assignment of moving users

Consider a set of servers and a set of users, where each server has a coverage region (i.e., an area of service) and a capacity (i.e., a maximum number of users it can serve). Our task is to assign every user to one server subject to the coverage and capacity constraints. To offer the highest quality of service, we wish to minimize the average distance between users and their assigned server. This is an instance of a well-studied problem in operations research, termed optimal assignment. Even though there exist several solutions for the static case (where user locations are fixed), there is currently no method for dynamic settings. In this paper, we consider the continuous assignment problem (CAP), where an optimal assignment must be constantly maintained between mobile users and a set of servers. The fact that the users are mobile necessitates real-time reassignment so that the quality of service remains high (i.e., their distance from their assigned servers is minimized). The large scale and the time-critical nature of targeted applications require fast CAP solutions. We propose an algorithm that utilizes the geometric characteristics of the problem and significantly accelerates the initial assignment computation and its subsequent maintenance. Our method applies to different cost functions (e.g., average squared distance) and to any Minkowski distance metric (e.g., Euclidean, L ₁ norm, etc.).     

Optimization models and methods for planning wireless mesh networks

In this paper novel optimization models are proposed for planning Wireless Mesh Networks (WMNs), where the objective is to minimize the network installation cost while providing full coverage to wireless mesh clients. Our mixed integer linear programming models allow to select the number and positions of mesh routers and access points, while accurately taking into account traffic routing, interference, rate adaptation, and channel assignment. We provide the optimal solutions of three problem formulations for a set of realistic-size instances (with up to 60 mesh devices) and discuss the effect of different parameters on the characteristics of the planned networks. Moreover, we propose and evaluate a relaxation-based heuristic for large-sized network instances which jointly solves the topology/coverage planning and channel assignment problems. Finally, the quality of the planned networks is evaluated under different traffic conditions through detailed system level simulations.     

A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem

This paper presents a hybrid Hopfield network-genetic algorithm (GA) approach to tackle the terminal assignment (TA) problem. TA involves determining minimum cost links to form a communications network, by connecting a given set of terminals to a given collection of concentrators. Some previous approaches provide very good results if the cost associated with assigning a single terminal to a given concentrator is known. However, there are situations in which the cost of a single assignment is not known in advance, and only the cost associated with feasible solutions can be calculated. In these situations, previous algorithms for TA based on greedy heuristics are no longer valid, or fail to get feasible solutions. Our approach involves a Hopfield neural network (HNN) which manages the problem's constraints, whereas a GA searches for high quality solutions with the minimum possible cost. We show that our algorithm is able to achieve feasible solutions to the TA in instances where the cost of a single assignment in not known in advance, improving the results obtained by previous approaches. We also show the applicability of our approach to other problems related to the TA.     

A cross-layer optimization framework for joint channel assignment and multicast routing in multi-channel multi-radio wireless mesh networks

Existing literature on multicast routing protocols in wireless mesh networks (WMNs) from the view point of the links involved in routing are divided into two categories: schemes are aimed at multicast construction with minimal interference which is known as NP hard problem. In contrast, other methods develop network-coding-based solutions with the main objective of throughput maximization, which can effectively reduce the complexity of finding the optimal routing solution from exponential to polynomial time. The proposed framework in this paper is placed in the second category. In multi-channel multi-radio WMNs (MCMR WMNs), each node is equipped with multiple radios, each tuned on a different channel. In this paper, for the first time, we propose a cross-layer convex optimization framework for joint channel assignment and multicast throughput maximization in MCMR WMNs. The proposed method is composed of two phases: in the first phase, using cellular learning automata, channels are assigned to the links established between the radios of the nodes in a distributed fashion such that the minimal interference coefficient for each link is provided. Then, the resultant channel assignment scheme is utilized in the second phase for throughput maximization within an iterative optimization framework based on Lagrange relaxation and primal problem decomposition. We have conducted many experiments to contrast the performance of our solution against many representative approaches.     

Fixed channel assignment using new dynamic programming approach in cellular radio networks

In this paper, we propose a new approach to the fixed channel assignment problem by modifying the dynamic programming technique. This new strategy extends the already known dynamic programming so that the channel assignment solutions can be obtained. There is no need to have an initial random solution for convergence. One of the questions in the fixed channel assignment is the minimum bandwidth, which is usually unknown; the new strategy can obtain this lower bound. Parallel processing can be implemented over the proposed algorithm. The existing fixed channel assignment methods do not have all these in one place. The performance of modified dynamic programming (MDP) is evaluated by computer simulation, applied to seven well-known benchmark problems on channel assignment. The channel assignment strategies results shows that required bandwidths of modified dynamic programming are closely match or sometimes better than the algorithms that we have investigated.     

Distributed channel assignment algorithms for 802.11n WLANs with heterogeneous clients

As the latest IEEE 802.11 standard, 802.11n applies several new technologies, such as multiple input multiple output (MIMO), channel bonding, and frame aggregation to greatly improve the rate, range and reliability of wireless local area networks (WLANs). In 802.11n WLANs, access points (APs) are often densely deployed to provide satisfactory coverage. Thus nearby APs should operate at non-overlapping channels to avoid mutual interference. It is challenging to assign channels in legacy 802.11a/b/g WLANs due to the limited number of channels. Channel assignment becomes more complex in 802.11n WLANs, as the channel bonding in 802.11n allows WLAN stations (APs and clients) to combine two adjacent, non-overlapping 20MHz channels together for transmission. On the other hand, IEEE 802.11n is backward compatible, such that 802.11n clients will coexist with legacy clients in 802.11n WLANs. Legacy clients may affect the performance of nearby 802.11n clients, and reduce the effectiveness of channel bonding. Based on these observations, in this paper, we study channel assignment in 802.11n WLANs with heterogeneous clients. We first present the network model, interference model, and throughput estimation model to estimate the throughput of each client. We then formulate the channel assignment problem into an optimization problem, with the objective of maximizing overall network throughput. Since the problem is NP-hard, we give a distributed channel assignment algorithm based on the throughput estimation model. We then present another channel assignment algorithm with lower complexity, and aim at minimizing interference experienced by high-rate, 802.11n clients. We have carried out extensive simulations to evaluate the proposed algorithms. Simulation results show that our algorithms can significantly improve the network throughput of 802.11n WLANs, compared with other channel assignment algorithms.     

A two-phase approach to interactivity enhancement for large-scale distributed virtual environments

Distributed virtual environments (DVEs) are distributed systems that allow multiple geographically distributed clients (users) to interact simultaneously in a computer-generated, shared virtual world. Applications of DVEs can be seen in many areas nowadays, such as online games, military simulations, collaborative designs, etc. To support large-scale DVEs with real-time interactions among thousands or even more distributed clients, a geographically distributed server architecture (GDSA) is generally needed, and the virtual world can be partitioned into many distinct zones to distribute the load among the servers. Due to the geographic distributions of clients and servers in such architectures, it is essential to efficiently assign the participating clients to servers to enhance users’ experience in interacting within the DVE. This problem is termed the client assignment problem (CAP) in this paper. We propose a two-phase approach, consisting of an initial assignment phase and a refined assignment phase to address the CAP. Both phases are shown to be NP-hard. Several heuristic assignment algorithms are then devised and evaluated via extensive simulations with realistic settings. We find that, even under heterogeneous environments like the Internet where accurate input data for the assignment algorithms are usually impractical to obtain, the proposed algorithms are still beneficial to the performances of DVE.