Tara Javidi

Optimality of Myopic Sensing in Multichannel Opportunistic Access

This paper considers opportunistic communication over multiple channels where the state (ldquogoodrdquo or ldquobadrdquo) of each channel evolves as independent and identically distributed (i.i.d.) Markov processes. A user, with limited channel sensing capability, chooses one channel to sense and decides whether to use the channel (based on the sensing result) in each time slot. A reward is obtained whenever the user senses and accesses a ldquogoodrdquo channel. The objective is to design a channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast as a partially observed Markov decision process (POMDP) or a restless multiarmed bandit process, to which optimal solutions are often intractable. This paper shows that a myopic policy that maximizes the immediate one-step reward is optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to two or three, while presenting a counterexample for the case of four channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and antijamming.

A high-throughput scheduling algorithm for a buffered crossbar switch fabric

We examine high-throughput scheduling algorithms for buffered crossbar switch fabrics containing one buffer per crosspoint. We propose a scheduling system that uses longest queue first (LQF) scheduling for virtual output queues (VOQs) at the inputs and round-robin (RR) scheduling for the crosspoints. It is shown, through fluid model techniques, that this system achieves 100% throughput for input traffic that satisfies the strong law of large numbers and that produces a load /spl les/1/N for any input/output pair of an N/spl times/N switching fabric. Simulations indicate that 100% throughput may be attained for a much larger class of admissible loads.

Wiretap Channel With Secure Rate-Limited Feedback

This paper studies the problem of secure communication over a wiretap channel p(y,z|x) with a secure feedback link of rate Rf, where X is the channel input, and Y and Z are channel outputs observed by the legitimate receiver and the eavesdropper, respectively. It is shown that the secrecy capacity, the maximum data rate of reliable communication while the intended message is not revealed to the eavesdropper, is upper bounded as Cs(Rf) les maxmin/p(x) {I(X;Y), I(X;Y |Z) + Rf}. The proof of the bound crucially depends on a recursive argument which is used to obtain the single-letter characterization. This upper bound is shown to be tight for the class of physically degraded wiretap channels. A capacity-achieving coding scheme is presented for this case, in which the receiver securely feeds back fresh randomness with rate Rf, generated independent of the received channel output symbols. The transmitter then uses this shared randomness as a secret key on top of Wyners coding scheme for wiretap channels without feedback. Hence, when a feedback link is available, the receiver should allocate all resources to convey a new key rather than sending back the channel output.

Adaptive opportunistic routing for wireless ad hoc networks

A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to opportunistically route the packets even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per-packet reward criterion. The proposed routing scheme jointly addresses the issues of learning and routing in an opportunistic context, where the network structure is characterized by the transmission success probabilities. In particular, this learning framework leads to a stochastic routing scheme that optimally “explores” and “exploits” the opportunities in the network.

Subcarrier allocation in OFDMA systems: beyond water-filling

This paper considers the issue of optimal subcarrier allocation in OFDMA. When including time-varying packet arrivals and channels, we show, via a counter example, that water-filling-based subcarrier allocation policies, contrary to conventional wisdom, fail to provide rate-stability for an otherwise stabilizable OFDMA system. We briefly discuss the long-run throughput optimality in OFDM as demonstrated in (S. Kittipiyakul et al., 2004). In particular under a simple channel model, we consider a nonidling policy which balances the queues and outperforms water-filling policies by achieving the minimum average delay. In this paper, we provide simple computation algorithms for implementing such policies and provide simulations for a numerical comparative study.

Opportunistic Routing with Congestion Diversity in Wireless Multi-hop Networks

This paper considers the problem of routing packets across a multi-hop network consisting of multiple sources of traffic and wireless links with stochastic reliability while ensuring bounded expected delay. Each packet transmission can be overheard by a random subset of receiver nodes among which the next relay is selected opportunistically. The main challenge in the design of minimum-delay routing policies is balancing the trade-off between routing the packets along the shortest paths to the destination and distributing traffic across the network. Opportunistic variants of shortest path routing may, under heavy traffic scenarios, result in severe congestion and unbounded delay. While the opportunistic variants of backpressure, which ensure a bounded expected delay, are known to exhibit poor delay performance at low to medium traffic conditions. Combining important aspects of shortest path routing with those of backpressure routing, this paper provides an opportunistic routing policy with congestion diversity (ORCD). ORCD uses a measure of draining time to opportunistically identify and route packets along the paths with an expected low overall congestion. Previously, ORCD was proved to ensure a bounded expected delay for all networks and under any admissible traffic (without any knowledge of traffic statistics). This paper proposes practical implementations and discusses criticality of various aspects of the algorithm. Furthermore, the expected delay encountered by the packets in the network under ORCD is compared against known existing routing policies via simulations where substantial improvements are observed.

Towards Throughput and Delay Optimal Routing for Wireless Ad-Hoc Networks

The design of routing protocols for wireless ad-hoc networks is guided by the dual requirements of throughput optimality and minimum delay. Lately, there has been a movement from the traditional routing approach, which identifies a best path to the destination before transmission and routes all the packets through it, to opportunistic approaches which make routing decisions adaptively based on actual transmission outcomes. We compare the stable rate region of both the approaches and find, interestingly, that opportunistic routing schemes do not always support a larger stable-rate region than traditional routing protocols. Backpressure based schemes are known to be throughput optimal but compromise on delay performance instead. We study the behavior of various schemes and propose a routing policy that considers both the goals of throughput optimality and minimizing expected delay in its design.

A fresh look at optimal subcarrier allocation in OFDMA systems

This paper considers the issue of optimal subcarrier allocation in OFDMA. We show, via a counter example, that water-filling based subcarrier allocation policies, contrary to conventional wisdom, fail to provide rate-stability for an otherwise stabilizable OFDMA system. Water-filling is too myopic when considering long-time average performance, e.g. delay, queue lengths, and even long-run throughput. This is because such policies ignore variable state (queue length) information, while, in fact, such an information is necessary to guarantee rate stability and/or to minimize average delay. In this paper, we identify an optimal non-idling policy which balances the queue lengths, when the channel follows an ON/OFF model. In such case, we show that such a policy achieves the minimum average holding cost (mean response time) at any time.

Resource Allocation in OFDMA with Time-Varying Channel and Bursty Arrivals

This paper considers the issue of delay optimal subcarrier allocation in OFDMA wireless networks when the arrivals and channels are stochastic. Our objective is to minimize the long-term average packet delay over multiple time epochs. In previous studies, we have shown that the optimal policy is complicated and unknown. However, based on the insights learned from a simple on-off channel model, we provide heuristic policies that use different degrees of channel and queue state information. More importantly, these examples show how the significance of queue vs. channel state information varies with the traffic load. This is of extreme practical interest when one considers the overhead associated with channel estimation and feedback.

A delay-minimizing routing strategy for wireless multi-hop networks

We consider a network where each route comprises a backlogged source, a number of relays and a destination at a finite distance. The locations of the sources and the relays are realizations of independent Poisson point processes. Given that the nodes observe a TDMA/ALOHA MAC protocol, our objective is to determine the number of relays and their placement such that the mean end-to-end delay in a typical route of the network is minimized.We first study an idealistic network model where all routes have the same number of hops, the same distance per hop and their own dedicated relays. Combining tools from queueing theory and stochastic geometry, we provide a precise characterization of the mean end-to-end delay. We find that the delay is minimized if the first hop is much longer than the remaining hops and that the optimal number of hops scales sublinearly with the source-destination distance. Simulating the original network scenario reveals that the analytical results are accurate, provided that the density of the relay process is sufficiently large. We conclude that, given the considered MAC protocol, our analysis provides a delay-minimizing routing strategy for random, multihop networks involving a small number of hops.