Sundaram Vanka

Superposition Coding Strategies: Design and Experimental Evaluation

We design and implement a software-radio system for Superposition Coding (SC), a multiuser transmission scheme that deliberately introduces interference among user signals at the transmitter, using a library of off-the-shelf point-to-point channel codes. We experimentally determine the set of rate-pairs achieved by this transmission scheme under a packet-error constraint. Our results suggest that SC can provide substantial gains in spectral efficiencies over those achieved by orthogonal schemes such as Time Division Multiplexing. Our findings also question the practical utility of the Gaussian approximation for the inter-user interference in Superposition-Coded systems.

Implementation and Experimental Results of Superposition Coding on Software Radio

Superposition coding is a well-known capacity-achieving coding scheme for stochastically degraded broadcast channels. Although well-studied in theory, it is important to understand issues that arise when implementing this scheme in a practical setting. In this paper, we present a software-radio based design of a superposition coding system on the GNU Radio platform with the Universal Software Radio Peripheral acting as the transceiver frontend. We also study the packet error performance and discuss some issues that arise in its implementation.

Convergence Speed of the Consensus Algorithm With Interference and Sparse Long-Range Connectivity

We analyze the effect of interference on the convergence rate of average consensus algorithms, which iteratively compute the measurement average by message passing among nodes. It is usually assumed that these algorithms converge faster with a greater exchange of information (i.e., by increased network connectivity) in every iteration. However, when interference is taken into account, it is no longer clear if the rate of convergence increases with network connectivity. We study this problem for randomly placed consensus-seeking nodes connected through an interference-limited network. We investigate the following questions: 1) How does the rate of convergence vary with increasing communication range of every node? 2) How does this result change when every node is allowed to communicate with a few selected far-off nodes? When nodes schedule their transmissions to avoid interference, we show that the convergence speed scales with r2-d , where r is the communication range and d is the number of dimensions. This scaling is the result of two competing effects when increasing r: increased schedule length for interference-free transmission versus the speed gain due to improved connectivity. Hence, although one-dimensional networks can converge faster with a greater communication range despite increased interference, the two effects exactly offset one another in two-dimensions. In higher dimensions, increasing the communication range can actually degrade the rate of convergence. Our results thus underline the importance of factoring in the effect of interference in the design of distributed estimation algorithms.

Scheduling using Superposition Coding: Design and Software Radio implementation

Cellular base stations typically orthogonalize downlink transmissions, although this approach is not always throughput-optimal. Indeed, it can be shown that removing the orthogonality constraint (as in Superposition Coding) can provide significant benefits in some scenarios. Based on this principle, we propose a scheduling algorithm for a two-user downlink that leverages the disparity in their respective channel qualities. By a judicious reallocation of the transmit power and bandwidth, this algorithm improves the throughput to both users vis-à-vis an orthogonal scheme. We design a software-defined radio platform to implement the scheduler and experimentally verify the gains promised by theory.

Analysis of the benefits of Superposition Coding in random wireless networks

Network-wide adoption of a multipacket transmission scheme such as Superposition Coding (SC) for local “one-to-many” communication results in mutually interfering “broadcast” clusters. We analyze the benefits of SC and traditional Frequency Division (FD) with this interference via a utility function that measures the rate of information transfer per unit area. In particular, we study transmitters forming a Poisson point process and using ALOHA for medium access. For a fixed bandwidth allocation, FD allows spatial reuse to be independently optimized for each frequency band. On the other hand, with SC for a fixed power allocation, the optimal spatial reuse depends on the relative contribution of each link to the utility function. Since optimal spatial reuse is a function of the network geometry, the gains provided by SC depend on the geometry of the receiver node placement.

Coordinated Packet Transmission in Random Wireless Networks

This paper studies the value of allowing multiple transmitters to share all of the available bandwidth to concurrently transmit to a single receiver with multi-packet decoding capability. While such coordination can be bandwidth-efficient, it increases the density of interferers when many such multiple-access clusters exist in the network. On the other hand, orthogonal schemes such as FDMA may not be as bandwidth-efficient but operate at lower interferer densities due to orthogonalization. We take the first step towards understanding this trade-off. In particular, we analyze equidistant transmitters sending data using a coordination scheme based on the optimum strategy for a Gaussian multiple access channel. In terms of the throughputs seen in a typical cluster in a Poisson network, this form of coordination has little or no benefit when compared to FDMA. We also find that the increased interference due to multiple coordinated transmissions reduces the efficacy of successive decoding.

A practical approach to strengthen vulnerable downlinks using superposition coding

We propose and experimentally demonstrate a novel approach to improve the packet delivery efficiency on a vulnerable downlink (e.g., from a transmitter to a far-away receiver) using superposition coding, a multiuser transmission scheme that forgoes orthogonal transmission and deliberately introduces interference among signals at the transmitter. On a software radio platform that uses off-the-shelf point-to-point channel codes, we show that a transmitter serving multiple links can use simple two-user superposition codes to dramatically improve (compared to time division multiplexing) the packet delivery efficiency on its most vulnerable links. Interestingly, our results suggest that superposing signals of far-away users on to those of high-traffic users yields the maximum benefits - implying that the degrees-of-freedom gain in doing so can more than compensate for the increased interference from signal superposition.

Distributed Averaging in Dense Wireless Networks

We consider the effect of network throughput on the convergence of a specific class of distributed averaging algorithms, called consensus algorithms. These algorithms rely on iterative computation of the desired average by message passing among the nodes. It is thus assumed that the rate of convergence should benefit from greater network connectivity. However, one must also account for the additional network resources that establishing such a connectivity would entail. In this paper, we study this problem in the context of randomly-placed consensus-seeking nodes that are connected through a dense wireless network, i.e., whose capacity is interference-limited. By analyzing the outage of each communication link along with results from mixing times of Markov chains, we obtain scaling laws for the mixing times of fastest-converging consensus topologies over such networks.

On consensus over stochastically switching directed topologies

We consider average consensus algorithms executed over stochastically varying communication topologies that may be unbalanced. It is known that the state values will reach consensus, under fairly weak conditions. However, the consensus value is a random variable. We provide concentration bounds for the distance of the state vector from the consensus subspace and for the asymptotic distribution of the value to which the various nodes converge as they reach consensus. The results allow the analysis of average consensus over wireless communication networks with more realistic assumptions than before.

Power-delay analysis of consensus algorithms on wireless networks with interference

We study the convergence of the average consensus algorithm in wireless networks in the presence of interference. For regular lattices with periodic boundary conditions, we characterize the convergence properties of optimal MAC protocol that maximizes the speed of convergence on these networks. We provide analytical upper and lower bounds for the convergence rate. Our results show that the fastest converging interconnection topology for the consensus algorithm crucially depends on the geometry of node placement in an interference-limited scenario.