Atilla Eryilmaz

Joint congestion control, routing, and MAC for stability and fairness in wireless networks

In this paper, we describe and analyze a joint scheduling, routing and congestion control mechanism for wireless networks, that asymptotically guarantees stability of the buffers and fair allocation of the network resources. The queue-lengths serve as common information to different layers of the network protocol stack. Our main contribution is to prove the asymptotic optimality of a primal-dual congestion controller, which is known to model different versions of transmission control protocol well

Fair resource allocation in wireless networks using queue-length-based scheduling and congestion control

We consider the problem of allocating resources (time slots, frequency, power, etc.) at a base station to many competing flows, where each flow is intended for a different receiver. The channel conditions may be time-varying and different for different receivers. It is well-known that appropriately chosen queue-length based policies are throughput-optimal while other policies based on the estimation of channel statistics can be used to allocate resources fairly (such as proportional fairness) among competing users. In this paper, we show that a combination of queue-length-based scheduling at the base station and congestion control implemented either at the base station or at the end users can lead to fair resource allocation and queue-length stability.

On Delay Performance Gains From Network Coding

This paper analyzes the gains in delay performance resulting from network coding. We consider a model of file transmission to multiple receivers from a single base station. Using this model, we show that gains in delay performance from network coding with or without channel side information can be substantial compared to conventional scheduling methods for downlink transmission.

On the Delay and Throughput Gains of Coding in Unreliable Networks

In an unreliable packet network setting, we study the performance gains of optimal transmission strategies in the presence and absence of coding capability at the transmitter, where performance is measured in delay and throughput. Although our results apply to a large class of coding strategies including maximum-distance separable (MDS) and Digital Fountain codes, we use random network codes in our discussions because these codes have a greater applicability for complex network topologies. To that end, after introducing a key setting in which performance analysis and comparison can be carried out, we provide closed-form as well as asymptotic expressions for the delay performance with and without network coding. We show that the network coding capability can lead to arbitrarily better delay performance as the system parameters scale when compared to traditional transmission strategies without coding. We further develop a joint scheduling and random-access scheme to extend our results to general wireless network topologies.

Polynomial Complexity Algorithms for Full Utilization of Multi-Hop Wireless Networks

In this paper, we provide and study a general framework that allows the development of distributed mechanisms to achieve full utilization of multi-hop wireless networks. In particular, we describe a generic randomized routing, scheduling and flow control scheme that is applicable to a large class of interference models, and that allows for the development of distributed algorithms which maximize network throughput and utilization. In particular, we focus on a specific interference model, namely the secondary interference model, and develop distributed algorithms with polynomial communication and computation complexity in the network size. This is an important result given that earlier throughput-optimal algorithms developed for such a model relies on the solution to an NP-hard problem. This results in a polynomial complexity cross-layer algorithm that achieves throughput optimality and fair allocation of network resources amongst the users. We further show that our algorithmic approach enables us to efficiently approximate the capacity region of a multi-hop wireless network.

Joint Asynchronous Congestion Control and Distributed Scheduling for Multi-Hop Wireless Networks

We consider a multi-hop wireless network shared by many users. For an interference model that only constrains a node to either transmit or receive at a time, but not both, we propose an architecture for fair resource allocation that consists of a distributed scheduling algorithm operating in conjunction with an asynchronous congestion control algorithm. We show that the proposed joint congestion control and scheduling algorithm supports at least one-third of the throughput supportable by any other algorithm, including centralized algorithms.

A Large Deviations Analysis of Scheduling in Wireless Networks

We consider a cellular network consisting of a base station and N receivers. The channel to each receiver is assumed to be in one of two states (ON or OFF) and the channel states of the receivers are assumed to be independent of each other. The goal is to compare the throughput of two different scheduling policies given an upper bound on the queue overflow probability or the delay violation probability. The two scheduling policies that we consider are: (i) a greedy scheduling policy which chooses to serve any of the channels in the ON state, and (ii) a queue-length-based policy which serves the longest queue connected to an ON channel. We show that the total network throughput of the queue-length-based policy is no less than that of the greedy policy for all N and is strictly larger than the throughput of the greedy policy for large N. Further, given an upper bound on the delay violation probability, we show that the throughput of the queue-length-based policy is an increasing function of N while the throughput of the greedy policy eventually decreases with increasing N and goes to zero. Given an upper bound on the queue overflow probability, we show that the throughput of the queue-length-based policy is a strictly increasing function of N while the throughput of the greedy policy eventually goes to a constant.

Asymptotically tight steady-state queue length bounds implied by drift conditions

The Foster–Lyapunov theorem and its variants serve as the primary tools for studying the stability of queueing systems. In addition, it is well known that setting the drift of the Lyapunov function equal to zero in steady state provides bounds on the expected queue lengths. However, such bounds are often very loose due to the fact that they fail to capture resource pooling effects. The main contribution of this paper is to show that the approach of “setting the drift of a Lyapunov function equal to zero” can be used to obtain bounds on the steady-state queue lengths which are tight in the heavy-traffic limit. The key is to establish an appropriate notion of state-space collapse in terms of steady-state moments of weighted queue length differences and use this state-space collapse result when setting the Lyapunov drift equal to zero. As an application of the methodology, we prove the steady-state equivalent of the heavy-traffic optimality result of Stolyar for wireless networks operating under the MaxWeight scheduling policy.

Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation for wireless networks, through which the predictability of user behavior is exploited to balance the wireless traffic over time, and significantly reduces the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which smart wireless devices are assumed to predict the arrival of new requests and submit them to the network time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases with the prediction duration . Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed light on multicasting with predictable demands and show that proactive multicast networks can achieve a significantly higher diversity gain that scales superlinearly with . Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive wireless resource framework.

Distributed cross-layer algorithms for the optimal control of multihop wireless networks

In this paper, we provide and study a general framework that facilitates the development of distributed mechanisms to achieve full utilization of multihop wireless networks. In particular, we describe a generic randomized routing, scheduling, and flow control scheme that allows for a set of imperfections in the operation of the randomized scheduler to account for potential errors in its operation. These imperfections enable the design of a large class of low-complexity and distributed implementations for different interference models. We study the effect of such imperfections on the stability and fairness characteristics of the system and explicitly characterize the degree of fairness achieved as a function of the level of imperfections. Our results reveal the relative importance of different types of errors on the overall system performance and provide valuable insight to the design of distributed controllers with favorable fairness characteristics. In the second part of the paper, we focus on a specific interference model, namely the secondary interference model, and develop distributed algorithms with polynomial communication and computation complexity in the network size. This is an important result given that earlier centralized throughput-optimal algorithms developed for such a model relies on the solution to an NP-hard problem at every decision. This results in a polynomial complexity cross-layer algorithm that achieves throughput optimality and fair allocation of network resources among the users. We further show that our algorithmic approach enables us to efficiently approximate the capacity region of a multihop wireless network.