A new joint strategy of radio channel allocation and power control for wireless mesh networks

For wireless mesh networks, it is critical to allocate the limited number of radio channels efficiently while mitigating the co-channel inference to improve network performance. For this reason, after extensively reviewing the related work, we present a new joint radio channel allocation (RCA) and power control (PC) strategy for wireless mesh networks. First, we formulate the RCA problem as a multiple objective optimization problem, with the constraints of transmission power and traffic data rates, while effective channel utilization (ECU) is chosen as the target metric for optimization. Second, we incorporate the channel status into the model of MAC protocols, including as signal-to-noise ratio (SNR) and transmission power. Consequently, the PC is incorporated into the ECU for channel allocation. Third, we propose to directly maximize the ECU to find both optimal radio channel and transmission power. The resulting strategy is a fully distributed RCA/PC algorithm without relying on a coordination mechanism among mesh routers. Our extensive simulation results have demonstrated that the proposed algorithm significantly outperforms the existing RCA strategies and the standard MAC protocols in performance such as throughput, packet dropping, delay and delay jitter.     

Joint power-playout control for media streaming over wireless links

Media streaming applications over wireless links face various challenges, due to both the nature of the wireless channel and the stringent delivery requirements of media traffic. In this paper, we seek to improve the performance of media streaming over an interference-limited wireless link, by using appropriate transmission and playout control. In particular, we choose both the power at the transmitter and the playout scheduling at the receiver, so as to minimize the power consumption and maximize the media playout quality. We formulate the problem using a dynamic programming approach, and study the structural properties of the optimal solution. We further develop a justified, low-complexity heuristic that achieves significant performance gain over benchmark systems. In particular, our joint power-playout heuristic outperforms: 1) the optimal power control policy in the regime where power is most important and 2) the optimal playout control policy in the regime where media (playout) quality is most important; furthermore, this heuristic has only a slight performance loss as compared to the optimal joint power-playout control policy over the entire range of the investigation.     

Stochastic routing in ad-hoc networks

We investigate a network routing problem where a probabilistic local broadcast transmission model is used to determine routing. We discuss this model's key features, and note that the local broadcast transmission model can be viewed as soft handoff for an ad-hoc network. We present results showing that an index policy is optimal for the routing problem. We extend the network model to allow for control of transmission type, and prove that the index nature of the optimal routing policy remains unchanged. We present three distributed algorithms which compute an optimal routing policy, discuss their convergence properties, and demonstrate their performance through simulation.     

Low-Delay Low-Complexity Bandwidth-Constrained Wireless Video Transmission Using SVC Over MIMO Systems

We propose an efficient strategy for the transmission of scalable video over multiple-input multiple-output (MIMO) wireless systems. In this paper, we use the latest scalable H.264 codec (SVC), which provides combined temporal, quality and spatial scalability. At the transmitter, we estimate the decoded video distortion for given channel conditions taking into account the effects of quantization, packet loss and error concealment. The proposed scalable decoder distortion algorithm offers low delay and low complexity. The performance of this method is validated using experimental results. In our proposed system, we use a MIMO system with orthogonal space-time block codes (O-STBC) that provides spatial diversity and guarantees independent transmission of different symbols within the block code. The bandwidth constrained allocation problem considered here is simplified and solved for one O-STBC symbol at a time. Furthermore, we take the advantage of the hierarchical structure of SVC to attain the optimal solution for each group of pictures (GOP) of the video sequence. We incorporate the estimated decoder distortion to optimally select the application layer parameter, i.e., quantization parameter (QP), and physical layer parameters, i.e., channel coding rate and modulation type for wireless video transmission.      

Resource allocation for physical-layer security in OFDMAdownlinkwith imperfect CSI

We investigate the problem of resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA) broadband network with an eavesdropper under the condition that both legitimate users and the eavesdropper are with imperfect channel state information (CSI). We consider three kinds of imperfect CSI: (1) noise and channel estimation errors, (2) feedback delay and channel prediction, and (3) limited feedback channel capacity, where quantized CSI is studied using rate-distortion theory because it can be used to establish an informationtheoretic lower bound on the capacity of the feedback channel. The problem is formulated as joint power and subcarrier allocation to optimize the maximum-minimum (max-min) fairness criterion over the users’ secrecy rate. The problem considered is a mixed integer nonlinear programming problem. To reduce the complexity, we propose a two-step suboptimal algorithm that separately performs power and subcarrier allocation. For a given subcarrier assignment, optimal power allocation is achieved by developing an algorithm of polynomial computational complexity. Numerical results show that our proposed algorithm can approximate the optimal solution.     

Minimum Energy Transmission Over a Wireless Channel With Deadline and Power Constraints

We consider optimal rate-control for energy-efficient transmission of data, over a time-varying channel, with packet-deadline constraints. Specifically, the problem scenario consists of a wireless transmitter with $B$ units of data that must be transmitted by deadline $T$ over a fading channel. The transmitter can control the transmission rate over time and the required instantaneous power depends on the chosen rate and the present channel condition, with limits on short-term average power consumption. The objective is to obtain the optimal rate-control policy that minimizes the total energy expenditure while ensuring that the deadline constraint is met. Using a continuous-time stochastic control formulation and a Lagrangian duality approach, we explicitly obtain the optimal policy and show that it possesses a very simple and intuitive form. Finally, we present an illustrative simulation example comparing the energy costs of the optimal policy with the full power policy.      

Topology optimisation for energy management in underwater sensor networks

In general, battery-powered sensors in a sensor network are operable as long as they can communicate sensed data to a processing node. In this context, a sensor network has two competing objectives: (1) maximisation of the network performance with respect to the probability of successful search for a specified upper bound on the probability of false alarms, and (2) maximisation of the network’s operable life. As both sensing and communication of data consume battery energy at the sensing nodes of the sensor network, judicious use of sensing power and communication power is needed to improve the lifetime of the sensor network. This paper presents an adaptive energy management policy that will optimally allocate the available energy between sensing and communication at each sensing node to maximise the network performance subject to specified constraints. Under the assumptions of fixed total energy allocation for a sensor network operating for a specified time period, the problem is reduced to synthesis of an optimal network topology that maximises the probability of successful search (of a target) over a surveillance region. In a two-stage optimisation, a genetic algorithm-based meta-heuristic search is first used to efficiently explore the global design space, and then a local pattern search algorithm is used for convergence to an optimal solution. The results of performance optimisation are generated on a simulation test bed to validate the proposed concept. Adaptation to energy variations across the network is shown to be manifested as a change in the optimal network topology by using sensing and communication models for underwater environment. The approximate Pareto-optimal surface is obtained as a trade-off between network lifetime and probability of successful search over the surveillance region.     

On the end-to-end flow allocation and channel assignment in multi-channel multi-radio wireless mesh networks with partially overlapped channels

The performance of wireless mesh networks (WMNs) can be improved significantly with the increase in number of channels and radios. Despite the availability of multiple channels in several of the current wireless standards, only a small fraction of them are non-overlapping and many channels are partially overlapped. In this paper, we formulate the joint channel assignment and flow allocation problem for multi-channel multi-radio WMNs as a Mixed Integer Linear Program (MILP). Unlike most of the previous studies, we consider the case when both non-overlapped and partially overlapped channels are being used. We consider an objective of maximizing aggregate end-to-end throughput and minimizing queueing delay in the network, instead of the sum of link capacities, since the traffic characteristics of a multi-hop WMN are quite different from a single hop wireless network. Our static channel assignment algorithm incorporates network traffic information, i.e., it is load aware. Our formulation takes into consideration several important network parameters such as the transmission power of each node, path loss information, the signal to interference plus noise ratio at a node, and the frequency response of the filters used in the transmitter and receiver. We show by simulations that our MILP formulation makes efficient use of the spectrum, by providing superior channel assignments and flow allocations with the addition of partially overlapped channels, without the use of any additional spectrum. We also justify the need to consider alternative objective functions such as, minimizing average queueing in the network. We also propose a polynomially bounded heuristic algorithm to scale the proposed algorithm to bigger network topologies.     

无线网络中具有信道感知的期望能耗最小化策略研究

随着无线网络技术的快速发展,节省能耗已成为构建绿色无线网络的一个非常重要的课题。由于信道的时变特性,在无线通信中利用好的信道状态能够获得更高的能量利用率。从整个无线网络的数据传输能耗出发,提出一种基于最优停止理论的数据传输期望能耗最小化策略(E²CMS)。E²CMS策略延迟数据的传输直到找到最好的期望信道状态,同时考虑了最大传输延迟和给定的接收端功率。首先,构建具有QoS约束的能耗最小化问题;接着,通过最优停止理论证明E²CMS策略是一种纯粹的阈值策略;然后,通过逆向归纳法求解定点方程,以求出功率阈值;最后,在典型的小尺度衰落信道模型中进行仿真实验,将E²CMS策略与多种不同的传输调度策略进行对比。结果表明,E²CMS策略具有更小的单位数据平均能耗,显著提高了网络性能。     

Joint routing, scheduling, and power control for multichannel wireless sensor networks with physical interference

Reliability and real-time requirements bring new challenges to the energy-constrained wireless sensor networks, especially to the industrial wireless sensor networks. Meanwhile, the capacity of wireless sensor networks can be substantially increased by operating on multiple nonoverlapping channels. In this context, new routing, scheduling, and power control algorithms are required to achieve reliable and real-time communications and to fully utilize the increased bandwidth in multichannel wireless sensor networks. In this paper, we develop a distributed and online algorithm that jointly solves multipath routing, link scheduling, and power control problem, which can adapt automatically to the changes in the network topology and offered load. We particularly focus on finding the resource allocation that realizes trade-off among energy consumption, end-to-end delay, and network throughput for multichannel networks with physical interference model. Our algorithm jointly considers 1) delay and energy-aware power control for optimal transmission radius and rate with physical interference model, 2) throughput efficient multipath routing based on the given optimal transmission rate between the given source-destination pairs, and 3) reliable-aware and throughput efficient multichannel maximal link scheduling for time slots and channels based on the designated paths, and the new physical interference model that is updated by the optimal transmission radius. By proving and simulation, we show that our algorithm is provably efficient compared with the optimal centralized and offline algorithm and other comparable algorithms.     

一种利用信道侦听的IEEE 802.11自适应优化算法

提出一种适用于DCF(distributed coordination function)机制的自适应优化算法.该算法基于网络节点侦听信道得到的网络状态信息进行相关参数的自适应调整以获得最优的网络性能,称为CSB(channel sensing backoff)算法.算法采用了对节点的信道接入请求以概率参数P_T进行过滤的方法控制节点竞争接入信道的激烈程度.不同于已有的DCF机制优化方法,CSB算法的特点在于,在优化调整过程中不需要进行计算复杂的网络节点数量估计,并且可以在不同网络状态下始终围绕确定的优化目标进行参数优化调整.仿真实验结果表明,算法能够针对网络节点数量和分组大小改变等网络状态变化作出自适应的网络优化调整,并获得了系统吞吐量、碰撞概率、延迟、延迟抖动、公平性等多方面的性能改善.     

基于中断概率的星地网络鲁棒功率控制算法

相较于传统的地面认知网络,星地认知网络链路传输时延较长,因此基于实时信道感知的认知用户中断概率较高。以离散时间马尔科夫链描述授权用户的动态及衰落信道,同时考虑信道转移概率的不确定性,建立基于似然不确定性模型的信道状态马尔科夫链,利用鲁棒的向后递归方法得到离线存储的功率分配矩阵,并对网络中多个用户采用博弈论的方法,进而提出基于中断概率的功率控制算法。仿真结果表明,与RRAP和TS-RS-PA算法相比,该算法在网络延迟存在的情况下能够更有效地降低中断概率并节约能耗。     

Performance investigation of a token passing access protocol for a multichannel optical fibre LAN

An alternative approach to multichannel local area network access is described. The new multichannel access scheme or protocol is based on a token passing arbitration policy. It is specifically intended for use with a starconfigured optical fibre LAN employing wavelength division multiplexing technology in order to provide a multichannel transmission medium. The multichannel network architecture comprises a single reservation channel and several equivalent speed data channels. Delay versus throughput performance characteristics for the network obtained by both analysis and discrete event simulation are shown. The multichannel token passing protocol is found to significantly improve these characteristics in comparison with a conventional single channel token passing approach using the same overall network transmission rate (i.e. the sum of the individual channel bit rates in the multichannel case). Furthermore, when the end-to-end propagation delay for an individual channel on the network becomes large, the multichannel token passing protocol is shown to exhibit an improved delay versus throughput performance over a multichannel protocol employing carrier sense multiple access with collision detection.     

Structural Properties of Optimal Transmission Policies Over a Randomly Varying Channel

We consider the problem of transmitting packets over a randomly varying point to point channel with the objective of minimizing the expected power consumption subject to a constraint on the average packet delay. By casting it as a constrained Markov decision process in discrete time with time-averaged costs, we prove structural results about the dependence of the optimal policy on buffer occupancy, number of packet arrivals in the previous slot and the channel fading state for both i.i.d. and Markov arrivals and channel fading. The techniques we use to establish such results: convexity, stochastic dominance, decreasing-differences, are among the standard ones for the purpose. Our main contribution, however, is the passage to the average cost case, a notoriously difficult problem for which rather limited results are available. The novel proof techniques used here are likely to have utility in other stochastic control problems well beyond their immediate application considered here.      

Rate-distortion optimized streaming of packetized media

This paper addresses the problem of streaming packetized media over a lossy packet network in a rate-distortion optimized way. We show that although the data units in a media presentation generally depend on each other according to a directed acyclic graph, the problem of rate-distortion optimized streaming of an entire presentation can be reduced to the problem of error-cost optimized transmission of an isolated data unit. We show how to solve the latter problem in a variety of scenarios, including the important common scenario of sender-driven streaming with feedback over a best-effort network, which we couch in the framework of Markov decision processes. We derive a fast practical algorithm for nearly optimal streaming in this scenario, and we derive a general purpose iterative descent algorithm for locally optimal streaming in arbitrary scenarios. Experimental results show that systems based on our algorithms have steady-state gains of 2-6 dB or more over systems that are not rate-distortion optimized. Furthermore, our systems essentially achieve the best possible performance: the operational distortion-rate function of the source at the capacity of the packet erasure channel.     

Video Packet Selection and Scheduling for Multipath Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under network bandwidth and playback delay constraints. The streaming policy consists in a joint selection of the network path and of the video packets to be transmitted, along with their sending time. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies between packets. A careful timing analysis allows to compute the quality perceived by the receiver for a constrained playback delay, as a function of the streaming policy. We derive an optimization problem based on a video abstraction model, under the assumption that the server knows, or can predict accurately the state of the network. A detailed analysis of constrained multipath streaming systems provides helpful insights to design an efficient branch and bound algorithm that finds the optimal streaming strategy. This solution allows to bound the performance of any scheduling strategy, but the complexity of the algorithm becomes rapidly intractable. We therefore propose a fast heuristic-based algorithm, built on load-balancing principles. It allows to reach close to optimal performance with a polynomial time complexity. The algorithm is then adapted to live streaming scenarios, where the server has only a partial knowledge of the packet stream, and the channel bandwidth. Extensive simulations show that the proposed algorithm only induces a negligible distortion penalty compared to the optimal strategy, even when the optimization horizon is limited, or the rate estimation is not perfect. Simulation results also demonstrate that the proposed scheduling solution performs better than common scheduling algorithms, and therefore represents a very efficient low-complexity multipath streaming algorithm, for both stored and live video services     

Optimal power allocation for a time-varying wireless channel under heavy-traffic approximation

This paper studies the problem of minimizing the queueing delay for a time-varying channel with a single queue, subject to constraints on the average and peak power. First, by separating the time-scales of the arrival process, the channel process and the queueing dynamics it derives a heavy-traffic limit for the queue length in the form of a reflected diffusion process. Given a monotone function of the queue-length process that serves as a penalty, and constraints on the average and peak available power, it shows that the optimal power allocation policy is a channel-state based threshold policy. For each channel state j there corresponds a threshold value of the queue length, and it is optimal to transmit at peak power if the queue length exceeds this threshold, and not transmit otherwise. Numerical results compare the optimal policy for the original Markovian dynamics to the threshold policy which is optimal for the heavy-traffic approximation, to conclude that that latter performs very well even outside the heavy-traffic operating regime.     

Optimal Relay Assignment and Power Allocation for Cooperative Communications

Cooperative communication for wireless networks has gained a lot of recent interest due to its ability to mitigate fading with exploration of spatial diversity. In this paper, we study a joint optimization problem of jointly considering transmission mode selection, relay assignment and power allocation to maximize the capacity of the network through cooperative wireless communications. This problem is much more challenging than relay assignment considered in literature work which simply targets to maximize the transmission capacity for a single transmission pair. We formulate the problem as a variation of the maximum weight matching problem where the weight is a function over power values which must meet power constraints (VMWMC). Although VMWMC is a non-convex problem whose complexity increases exponentially with the number of relay nodes, we show that the duality gap of VMWMC is virtual zero. Based on this result, we propose a solution using Lagrange dual decomposition to reduce the computation complexity. We do simulations to evaluate the performance of the proposed solution. The results show that our solution can achieve maximum network capacity with much less computation time compared with exhaustive search, and our solution outperforms existing sub-optimal solutions that can only achieve much lower network capacity.     

广域稳控系统通信网络性能仿真及分析

广域稳控系统对覆盖面积广阔的电力系统运行状态进行监测和分析，对实时性与可靠性要求很高。分析了广域稳控系统的通信网络需求，基于现网架构设计了相应的通信方案；理论上分析了广域稳控系统分段时延产生机理，建立了广域稳控系统通信时延的计算模型；基于Exata网络仿真平台建立了电力设备模型及SDH（Synchronous Digital Hierarchy，同步数字体系）传输网网络模型，并通过电力通信联合仿真接口模型接入电力实物设备，验证了仿真模型的有效性；最后通过对比不同业务传输通道下的广域稳控系统通信网络时延，结果表明在广域规模达3 000 km以上时，专有通道传输时延仍能满足50 ms以内的需求，而基于SPDnet的三级IP网络通道受厂站负载的影响，无法保证所有厂站业务均满足时延需求，且交换时延的不稳定带来了极大的时延抖动。     

Optimal transmission schemes for parallel and fading Gaussian broadcast channels with an energy harvesting rechargeable transmitter

We consider an energy harvesting transmitter sending messages to two users over parallel and fading Gaussian broadcast channels. Energy required for communication arrives (is harvested) at the transmitter and a finite-capacity battery stores it before being consumed for transmission. Under off-line knowledge of energy arrival and channel fading variations, we obtain the trade-off between the performances of the users by characterizing the maximum departure region in a given interval. We first analyze the transmission with an energy harvesting transmitter over parallel broadcast channels. We show that the optimal total transmit power policy that achieves the boundary of the maximum departure region is the same as the optimal policy for the non-fading broadcast channel, which does not depend on the priorities of the users, and therefore is the same as the optimal policy for the non-fading scalar single-user channel. The optimal total transmit power can be found by a directional water-filling algorithm. The optimal splitting of the power among the parallel channels is performed in each epoch separately. Next, we consider fading broadcast channels and obtain the transmission policies that achieve the boundary of the maximum departure region. The optimal total transmit power allocation policy is found using a specific directional water-filling algorithm for fading broadcast channels. The optimal power allocation depends on the priorities of the users unlike in the case of parallel broadcast channels. Finally, we provide numerical illustrations of the optimal policies and maximum departure regions for both parallel and fading broadcast channels.