Abstract—Coding strategies that exploit node cooperation are developed for relay networks. Two basic schemes are studied: the relays decode-and-forward the source message to the destination, or they compress-and-forward their channel outputs to the desti-nation. The decode-and-forward scheme is a variant of multihop-ping, but in addition to having the relays successively decode the message, the transmitters cooperate and each receiver uses several or all of its past channel output blocks to decode. For the compress-and-forward scheme, the relays take advantage of the statistical dependence between their channel outputs and the destination’s channel output. The strategies are applied to wireless channels, and it is shown that decode-and-forward achieves the ergodic capacity with phase fading if phase information is available only locally, and if the relays are near the source node. The ergodic capacity coin-cides with the rate of a distributed antenna array with full coop-eration even though the transmitting antennas are not colocated. The capacity results generalize broadly, including to multiantenna transmission with Rayleigh fading, single-bounce fading, certain quasi-static fading problems, cases where partial channel knowl-edge is available at the transmitters, and cases where local user co-operation is permitted. The results further extend to multisource and multidestination networks such as multiaccess and broadcast relay channels. Index Terms—Antenna arrays, capacity, coding, multiuser chan-nels, relay channels. I.

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Wireless meshnet8Ex8 (WMNs)consist of meshrout6L and meshclient8 where meshroutfix have minimal mobilit and formtr backbone of WMNs. They provide netide access for bot mesh andconvent1)fi8 clientt TheintL gratLfl of WMNs wit ot8 net8866 such as t1Int6fiPx1 cellular, IEEE 802.11, IEEE 802.15, IEEE 802.16, sensor netsor1L ets can be accomplishedtccomp tc gatomp and bridging functng1 in t1 meshroutfijx Meshclient can be eit8fi st8fij1)6x or mobile, and can form aclient meshnet16S amongtng1fifiELj and wit meshroutLfifi WMNs are antLfifl1)6fl t resolvets limit18fiflfl andt significantfl improvetp performance of ad hocnetLEP8L wireless local area net1Pxx (WLANs), wireless personal areanet16fij (WPANs), and wirelessmetess1fifljfl areanet1LPS (WMANs). They are undergoing rapid progress and inspiring numerousdeploymentS WMNs will deliver wireless services for a largevariet ofapplicat6fifl in personal, local, campus, andmet8Lfix1)6fi areas. Despit recent advances in wireless mesh netjLfiP1)6 many research challenges remain in allprotjfiS layers. This paperpresent adetEfl81 stEonrecent advances and open research issues in WMNs. Syst1 architL881)6 andapplicat)68 of WMNs are described, followed by discussingts critssi factss influencingprotenc design.Theoret8fiL netore capacit and tdst1LLSjx tt1LL protLLSj for WMNs are exploredwit anobjectE1 t point out a number of open research issues. Finally,tnal beds,indust681 pract68 andcurrent strent actntx1) relatt t WMNs arehighlight8x # 2004 Elsevier B.V. Allrl rl KedI7-8 Wireless meshnet186flfl Ad hocnet8jEES Wireless sensornetor16fl Medium accessconts1fi Routs1 prots1fiS Transport protspor ScalabilitS Securiti Powermanagement andcontfi8fl Timingsynchronizat ion 1389-1286/$ - seefront matt # 2004 Elsevier B.V. Allright reserved. doi:10....

Early simulation experience with wireless ad hoc networks suggests that their capacity can be surprisingly low, due to the requirement that nodes forward each others’ packets. The achievable capacity depends on network size, traffic patterns, and detailed local radio interactions. This paper examines these factors alone and in combination, using simulation and analysis from first principles. Our results include both specific constants and general scaling relationships helpful in understanding the limitations of wireless ad hoc networks. We examine interactions of the 802.11 MAC and ad hoc forwarding and the effect on capacity for several simple configurations and traffic patterns. While 802.11 discovers reasonably good schedules, we nonetheless observe capacities markedly less than optimal for very simple chain and lattice networks with very regular traffic patterns. We validate some simulation results with experiments. We also show that the traffic pattern determines whether an ad hoc network’s per node capacity will scale to large networks. In particular, we show that for total capacity to scale up with network size the average distance between source and destination nodes must remain small as the network grows. Non-local traffic patterns in which this average distance grows with the network size result in a rapid decrease of per node capacity. Thus the question “Are large ad hoc networks feasible?” reduces to a question about the likely locality of communication in such networks.

Abstract not found

Mobile Ad Hoc Networks (MANETs) provide rapidly deployable and self-configuring network capacity required in many critical applications, e.g., battlefields, disaster relief and wide area sensing. In this paper we study the problem of efficient data delivery in sparse MANETs where network partitions can last for a significant period. Previous approaches rely on the use of either long range communication which leads to rapid draining of nodes ’ limited batteries, or existing node mobility which results in low data delivery rates and large delays. In this paper, we describe a Message Ferrying (MF) approach to address the problem. MF is a mobility-assisted approach which utilizes a set of special mobile nodes called message ferries (or ferries for short) to provide communication service for nodes in the deployment area. The main idea behind the MF approach is to introduce non-randomness in the movement of nodes and exploit such non-randomness to help deliver data. We study two variations of MF, depending on whether ferries or nodes initiate proactive movement. The MF design exploits mobility to improve data delivery performance and reduce energy consumption in nodes. We evaluate the performance of MF via extensive ns simulations which confirm the MF approach is efficient in both data delivery and energy consumption under a variety of network conditions.

Intermittently connected mobile networks are sparse wireless networks where most of the time there does not exist a complete path from the source to the destination. These networks

Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off the data to wired access points when in proximity. This leads to substantial power savings at the sensors as they only have to transmit over a short range. Detailed performance analysis is presented based on a simple model of the system incorporating key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points) and the required buffer capacities on the sensors and the MULEs. The modeling along with simulation results can be used for further analysis and provide certain guidelines for deployment of such systems. I.

The random waypoint model is a commonly used mobility model in the simulation of ad hoc networks. It is known that the spatial distribution of network nodes moving according to this model is, in general, nonuniform. However, a closed-form expression of this distribution and an in-depth investigation is still missing. This fact impairs the accuracy of the current simulation methodology of ad hoc networks and makes it impossible to relate simulation-based performance results to corresponding analytical results. To overcome these problems, we present a detailed analytical study of the spatial node distribution generated by random waypoint mobility. More specifically, we consider a generalization of the model in which the pause time of the mobile nodes is chosen arbitrarily in each waypoint and a fraction of nodes may remain static for the entire simulation time. We show that the structure of the resulting distribution is the weighted sum of three independent components: the static, pause, and mobility component. This division enables us to understand how the model’s parameters influence the distribution. We derive an exact equation of the asymptotically stationary distribution for movement on a line segment and an accurate approximation for a square area. The good quality of this approximation is validated through simulations using various settings of the mobility parameters. In summary, this article gives a fundamental understanding of the behavior of the random waypoint model.

We consider dynamic routing and power allocation for a wireless network with time varying channels. The network consists of power constrained nodes which transmit over wireless links with adaptive transmission rates. Packets randomly enter the system at each node and wait in output queues to be transmitted through the network to their destinations. We establish the capacity region of all rate matrices (# ij ) that the system can stably support---where (# ij ) represents the rate of traffic originating at node i and destined for node j. A joint routing and power allocation policy is developed which stabilizes the system and provides bounded average delay guarantees whenever the input rates are within this capacity region. Such performance holds for general arrival and channel state processes, even if these processes are unknown to the network controller. We then apply this control algorithm to an ad-hoc wireless network where channel variations are due to user mobility, and compare its performance with the Grossglauser-Tse relay model developed in [13].