Venkat Anantharam

Achieving 100% throughput in an input-queued switch

It is well known that head-of-line blocking limits the throughput of an input-queued switch with first-in-first-out (FIFO) queues. Under certain conditions, the throughput can be shown to be limited to approximately 58.6%. It is also known that if non-FIFO queueing policies are used, the throughput can be increased. However, it has not been previously shown that if a suitable queueing policy and scheduling algorithm are used, then it is possible to achieve 100% throughput for all independent arrival processes. In this paper we prove this to be the case using a simple linear programming argument and quadratic Lyapunov function. In particular, we assume that each input maintains a separate FIFO queue for each output and that the switch is scheduled using a maximum weight bipartite matching algorithm. We introduce two maximum weight matching algorithms: longest queue first (LQF) and oldest cell first (OCF). Both algorithms achieve 100% throughput for all independent arrival processes. LQF favors queues with larger occupancy, ensuring that larger queues will eventually be served. However, we find that LQF can lead to the permanent starvation of short queues. OCF overcomes this limitation by favoring cells with large waiting times.

Optimal sequences, power control, and user capacity of synchronous CDMA systems with linear MMSE multiuser receivers

There has been intense effort in the past decade to develop multiuser receiver structures which mitigate interference between users in spread-spectrum systems. While much of this research is performed at the physical layer, the appropriate power control and choice of signature sequences in conjunction with multiuser receivers and the resulting network user capacity is not well understood. In this paper we will focus on a single cell and consider both the uplink and downlink scenarios and assume a synchronous CDMA (S-CDMA) system. We characterize the user capacity of a single cell with the optimal linear receiver (MMSE receiver). The user capacity of the system is the maximum number of users per unit processing gain admissible in the system such that each user has its quality-of-service (QoS) requirement (expressed in terms of its desired signal-to-interference ratio) met. This characterization allows one to describe the user capacity through a simple effective bandwidth characterization: users are allowed in the system if and only if the sum of their effective bandwidths is less than the processing gain of the system. The effective bandwidth of each user is a simple monotonic function of its QoS requirement. We identify the optimal signature sequences and power control strategies so that the users meet their QoS requirement. The optimality is in the sense of minimizing the sum of allocated powers. It turns out that with this optimal allocation of signature sequences and powers, the linear MMSE receiver is just the corresponding matched filter for each user. We also characterize the effect of transmit power constraints on the user capacity.

Analysis and comparison of TCP Reno and Vegas

We propose some improvements of TCP Vegas and compare its performance characteristics with TCP Reno. We argue through analysis that TCP Vegas, with its better bandwidth estimation scheme, uses the network resources more efficiently and fairly than TCP Reno. Simulation results are given that support the results of the analysis.

Optimal sequences and sum capacity of synchronous CDMA systems

The sum capacity of a multiuser synchronous CDMA system is completely characterized in the general case of asymmetric user power constraints-this solves the open problem posed by Rupf and Massey (see ibid., vol.40, p.1261-6, 1994) which had solved the equal power constraint case. We identify the signature sequences with real components that achieve sum capacity and indicate a simple recursive algorithm to construct them.

Utility-based rate control in the Internet for elastic traffic

In a communication network, a good rate allocation algorithm should reflect the utilities of the users while being fair. We investigate this fundamental problem of achieving the system optimal rates in the sense of maximizing aggregate utility, in a distributed manner, using only the information available at the end hosts of the network. This is done by decomposing the overall system problem into subproblems for the network and for the individual users by introducing a pricing scheme. The users are to solve the problem of maximizing individual net utility, which is the utility less the amount they pay. We provide algorithms for the network to adjust its prices and the users to adjust their window sizes such that at an equilibrium the system optimum is achieved. Further, the equilibrium prices are such that the system optimum achieves weighted proportional fairness. It is notable that the update algorithms of the users do not require any explicit feedback from the network, rendering them easily deployable over the Internet. Our scheme is incentive compatible in that there is no benefit to the users to lie about their utilities.

VLSI architectures for iterative decoders in magnetic recording channels

VLST implementation complexities of soft-input soft-output (SISO) decoders are discussed. These decoders are used in iterative algorithms based on Turbo codes or Low Density Parity Check (LDPC) codes, and promise significant bit error performance advantage over conventionally used partial-response maximum likelihood (PRML) systems, at the expense of increased complexity. This paper analyzes the requirements for computational hardware and memory, and provides suggestions for reduced-complexity decoding and reduced control logic. Serial concatenation of interleaved codes, using an outer block code with a partial response channel acting as an inner encoder, is of special interest for magnetic storage applications.

High throughput low-density parity-check decoder architectures

Two decoding schedules and the corresponding serialized architectures for low-density parity-check (LDPC) decoders are presented. They are applied to codes with parity-check matrices generated either randomly or using geometric properties of elements in Galois fields. Both decoding schedules have low computational requirements. The original concurrent decoding schedule has a large storage requirement that is dependent on the total number of edges in the underlying bipartite graph, while a new, staggered decoding schedule which uses an approximation of the belief propagation, has a reduced memory requirement that is dependent only on the number of bits in the block. The performance of these decoding schedules is evaluated through simulations on a magnetic recording channel.

Charge-sensitive TCP and rate control in the Internet

We investigate the fundamental problem of achieving the system optimal rates in a distributed environment, which maximize the total user utility, using only the information available at the end hosts. This is done by decomposing the system problem into two subproblems-network and user problems-and introducing an incentive-compatible pricing scheme, while maintaining proportional fairness. We demonstrate that when users update their parameters by solving their own optimization problem, at an equilibrium the system optimum is achieved. Furthermore, this algorithm does not require any explicit feedback from the network and can be deployed over the Internet with modifications only on the end hosts. In the second part of the paper we model as a noncooperative game the case where the choice of each users action has nonnegligible effect on the price per unit flow at the resources and investigate the Nash equilibria of the game. We show, in the simple case of a single bottleneck, that there exists a unique Nash equilibrium of the game. Further, as the number of users increases, the unique Nash equilibrium approaches the system optimum.

The stability region of the finite-user slotted ALOHA protocol

A version of the discrete-time slotted ALOHA protocol operating with finitely many buffered terminals is considered. The stability region is defined to be the set of vectors of arrival rates lambda =( lambda /sub 1/,. . ., lambda /sub M/) for which there exists a vector of transmission probabilities such that the system is stable. It is assumed that arrivals are independent from slot to slot, and the following model for the arrival distribution in a slot is assumed: the total number of arrivals in any slots is geometrically distributed, with the probability that such an arrival is at node i being lambda /sub i/ times ( Sigma /sub k/ lambda /sub k/)/sup -1/, independent of the others. With this arrival model, it is proven that the closure of the stability region of the protocol is the same as the closure of the Shannon capacity region of the collision channel without feedback, as determined by J.L. Massey and P. Mathys (1985). At present it is not clear if this result depends on the choice of arrival distribution. The basic probabilistic observation is that the stationary distribution and certain conditional distributions derived from it have positive correlations for bounded increasing functions. >

Optimal routing control: repeated game approach

Communication networks shared by selfish users are considered and modeled as noncooperative repeated games. Each user is interested only in optimizing its own performance by controlling the routing of its load. We investigate the existence of a Nash equilibrium point (NEP) that achieves the system-wide optimum cost. The existence of a subgame-perfect NEP that not only achieves the system-wide optimum cost but also yields a cost for each user no greater than its stage game NEP cost is shown for two-node multiple link networks. It is shown that more general networks where all users have the same source-destination pair have a subgame-perfect NEP that achieves the minimum total system cost, under a mild technical condition. It is shown that general networks with users having multiple source-destination pairs do not necessarily have such an NEP.