Searcharxiv⌕ Search

arXiv subjects

Piyush Gupta

Publications and source records attributed to Piyush Gupta.

38 records · Page 3Linked to original sources

On Capacity Scaling in Arbitrary Wireless Networks

In recent work, Ozgur, Leveque, and Tse (2007) obtained a complete scaling characterization of throughput scaling for random extended wireless networks (i.e., $n$ nodes are placed uniformly at random in a square region of area $n$). They showed that for small path-loss exponents $α\in(2,3]$ cooperative communication is order optimal, and for large path-loss exponents $α> 3$ multi-hop communication is order optimal. However, their results (both the communication scheme and the proof technique) are strongly dependent on the regularity induced with high probability by the random node placement. In this paper, we consider the problem of characterizing the throughput scaling in extended wireless networks with arbitrary node placement. As a main result, we propose a more general novel cooperative communication scheme that works for arbitrarily placed nodes. For small path-loss exponents $α\in (2,3]$, we show that our scheme is order optimal for all node placements, and achieves exactly the same throughput scaling as in Ozgur et al. This shows that the regularity of the node placement does not affect the scaling of the achievable rates for $α\in (2,3]$. The situation is, however, markedly different for large path-loss exponents $α>3$. We show that in this regime the scaling of the achievable per-node rates depends crucially on the regularity of the node placement. We then present a family of schemes that smoothly "interpolate" between multi-hop and cooperative communication, depending upon the level of regularity in the node placement. We establish order optimality of these schemes under adversarial node placement for $α> 3$.

cs.IT↗

Information-Theoretic Bounds for Multiround Function Computation in Collocated Networks

We study the limits of communication efficiency for function computation in collocated networks within the framework of multi-terminal block source coding theory. With the goal of computing a desired function of sources at a sink, nodes interact with each other through a sequence of error-free, network-wide broadcasts of finite-rate messages. For any function of independent sources, we derive a computable characterization of the set of all feasible message coding rates - the rate region - in terms of single-letter information measures. We show that when computing symmetric functions of binary sources, the sink will inevitably learn certain additional information which is not demanded in computing the function. This conceptual understanding leads to new improved bounds for the minimum sum-rate. The new bounds are shown to be orderwise better than those based on cut-sets as the network scales. The scaling law of the minimum sum-rate is explored for different classes of symmetric functions and source parameters.

cs.IT↗