In the past decade, remarkable progress has been made in the area of network coding in terms of theory, code design, and applications. However, from a cross-layer perspective, the understanding of resource allocation for random network coding (RNC) and its impact on network throughput has been limited. In this article we address the problem of resource allocation for RNC in a general wireless network, using the recent framework that models the dynamics of RNC using a system of differential equations. Based on this framework, we design a gradient algorithm that can be used to improve a variety of throughput objectives.

With the rapid advance in the technology area of data storage, storage capacities have increased substantially while the price has been dropping fast. Motivated by this trend, it has been proposed in the Cache-and-Forward architecture that storage is incorporated into each intermediate CNF router. Content can be cached at CNF routers when they flow through the network, and therefore, routers can serve the subsequent requests later on, without forwarding the requests to the host server, we refer to this caching paradigm as In-Network Caching. In this paper, the content caching is enhanced by Content Broadcast(CB), by which a CNF router broadcasts the information of cached content to its neighboring nodes. In order to solve the problem that with limited storage, how an intermediate CNF router optimally decides which passing content should be cached, we develop a mathematical model for CB to minimize the average content retrieval latency, and propose the Independent Allocation algorithm. We compare the average content retrieval latencies of the proposed caching scheme with two other commonly used cache replacement policies. We study the impact of cache size and locality parameter. The proposed scheme is shown to provide significant performance improvement under various settings by as large as 65%.

In this paper, we consider multicast with Random Network Coding (RNC) over a wireless network using Orthogonal Frequency Division Multiple Access (OFDMA). Specifically, we propose a cross-layer resource allocation mechanism to minimize the total transmit power in the network to achieve a target throughput. The problem in its original form is a NP-hard mixed integer program. We alleviate this problem with a greedy power and subcarrier allocation algorithm that is combined with a node selection strategy that is enabled by RNC, which we refer to as “min-cut chasing.” We compare it with a reference algorithm that assigns subcarriers independently based on the max-min fairness criterion followed by optimal power allocation. Our results reveal that the proposed greedy algorithm with min-cut chasing, which is of polynomial complexity, yields power savings of 3dB and is within 1dB of a lower bound based on an interference-free assumption.

We develop a framework based on differential equations (DE) and differential inclusions (DI) for analyzing Random Network Coding (RNC) in an arbitrary wireless network. The DEDI framework serves as a powerful numerical and analytical tool to study RNC. For demonstration, we first build a system of DE's with this framework, under the fluid approximation, to model the means of the rank evolution processes. By converting this system to DI's and explicitly solving them, we show that the average multicast throughput is equal to the min-cut bound. We then turn to the precise system of DE's regarding the means and variances of the rank evolution processes. By analyzing this system, we show that the rank evolution processes asymptotically concentrate to the solution of the DI's obtained previously. From this result, it immediately follows that the min-cut bound can be achieved as the number of source packets becomes large. We demonstrate the numerical accuracy and flexibility in performance analysis enabled by the DEDI framework via illustrative examples of networks with multiple multicast sessions, complex topology and correlated reception. We also briefly discuss its application in MAC and PHY adaptation and the extension to Random Coupon Selection.

FCC requires that any white space device be able to sense wireless microphone (WM) signals at -114dBm typically corresponding to a SNR of -20dB, an extremely challenging task. This paper presents a novel WM detector based on the ESPRIT algorithm along with an auxiliary prewhitening filter that meets this requirement. Compared with existing detectors, the proposed WM detector not only successfully combats noise, but also provides a technique to mitigate the effect of adjacent channel interference caused by DTV transmission leakage, an issue much less studied.

We develop a framework called DEDI based on differential equations (DE) and differential inclusions (DI) to describe the rank evolution of random network coding (RNC). The DEDI serves as a powerful numerical and analytical tool to study RNC and we demonstrate this via numerical examples as well as an alternate proof of a well known result on RNC - a multicast at rate R exists if and only if a unicast at rate R exists separately for each destination.

Cooperative forwarding in wireless networks has shown to yield rate and diversity gains, but it incurs energy costs borne by the cooperating nodes. In this paper we consider an incentive mechanism called Bandwidth Exchange (BE) where the nodes flexibly exchange the transmission bandwidth as a means of providing incentive for forwarding data, without increasing either the total bandwidth required or the total transmit power. The advent of cognitive radios and multicarrier systems such as Orthogonal Frequency Division Multiple Access (OFDMA) with the ability to flexibly delegate and employ a number of subcarriers makes this approach particularly appealing compared to other incentive mechanisms that are often based on abstract notions of credit and shared understanding of worth.We consider a N-node wireless network over a fading channel and use a Nash Bargaining Solution (NBS) mechanism to study the benefits of BE in terms of rate and coverage gains.We also propose two heuristic algorithms based on simple probabilistic rules for forwarding and study the tradeoffs in terms of performance among these approaches. Our results reveal that bandwidth exchange based forwarding can provide transmit power savings in OFDMA networks of at least 3dB compared to noncooperation.

Cooperative forwarding in wireless networks has shown to yield benefits of rate and diversity gains, but it needs to be incentivized due to the energy and delay costs incurred by individual nodes in such cooperation. In this paper we consider an incentive mechanism called Bandwidth Exchange (BE) where the cooperating nodes flexibly exchange the transmission bandwidth (spectrum) as a means of providing incentive for forwarding data. The advent of cognitive radios with the ability to flexibly change their carrier frequency as well as their transmission bandwidth makes this form of incentive particularly attractive compared to other incentive mechanisms that are often based on abstract notions of credit and shared understanding of worth. Specifically, we consider a N-node wireless network and use a Nash Bargaining Solution (NBS) mechanism to study the benefits of BE in terms of rate and coverage gains.