Communication complexity

About: Communication complexity is a research topic. Over the lifetime, 3870 publications have been published within this topic receiving 105832 citations.

Channel Estimation Algorithms for OFDM-IDMA: Complexity and Performance

Abstract: In this paper, a number of channel estimation algorithms for iterative receivers are compared for the case of an up-link orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) system. Both pilot based algorithms, used to obtain an initial estimate, as well as semi-blind decision-directed algorithms working as a component of the iterative receiver are considered. Algorithms performing either joint minimum mean square error (MMSE) channel estimation, or iterative estimation using space-alternating expectation maximization (SAGE), are evaluated. The considered algorithms differ in terms of complexity, as well as performance. The main contribution of this paper is to give an overview of different channel estimation approaches for OFDM-IDMA, where the complexity versus performance tradeoff is at the focal point. There is no single channel estimator providing the best tradeoff and our analysis shows how the system load (number of users) and the SNR influence the estimator choice.

Efficient communication primitives on hypercubes

Abstract: We give practical algorithms, complexity analysis and implementation for one-to-all broadcasting, all-to-all personalized communication and matrix transpose (with two-dimensional partitioning of the matrix) on hypercubes. We assume the following communication characteristics: circuit-switched, e-cube routing and one-port communication model. For one-to-all broadcasting, we give an algorithm that combines the well-known recursive doubling algorithm[1] and the algorithm based on edgedisjoint spanning trees[2]. The measured times of the combined algorithm are always superior to those of the edge-disjoint spanning tree algorithm and outperform the recursive doubling algorithm. For all-to-all personalized communication we propose a hybrid algorithm that combines the well-known recursive doubling algorithm[3,4] and the recently proposed direct-route algorithm[5,6] Our hybrid algorithm balances between data transfer time and start-up time of these two algorithms, and its communication complexity is estimated to be better than the two previous algorithms for a range of machine parameters. For matrix transpose with two-dimensional partitioning of the matrix, we relate a two-phase algorithm to the previous result in Reference 7. The algorithm is predicted to be better than the recursive transpose algorithm[8] by n nearest-neighbor communications[4]. It takes advantage of circuit-switched routing and is congestion-free within each phase. We also suggest a way of storing the matrix such that the transpose operation can be realized in one phase without congestion.

On quantum and approximate privacy

Abstract: This paper studies privacy and secure function evaluation in communication complexity. The focus is on quantum versions of the model and on protocols with only approximate privacy against honest players. We show that the privacy loss (the minimum divulged information) in computing a function can be decreased exponentially by using quantum protocols, while the class of privately computable functions (i.e., those with privacy loss 0) is not increased by quantum protocols. Quantum communication combined with small information leakage on the other hand makes certain functions computable (almost) privately which are not computable using quantum communication without leakage or using classical communication with leakage. We also give an example of an exponential reduction of the communication complexity of a function by allowing a privacy loss of o(1) instead of privacy loss 0.

Toward high communication performance through compiled communications on a circuit switched interconnection network

Abstract: This paper discusses a new principle of interconnection network for massively parallel architectures in the field of numerical computation. The principle is motivated by an analysis of the application features and the need to design new kind of communication networks combining very high bandwidth, very low latency, performance independence to communication pattern or network load and a performance improvement proportional to the hardware performance improvement. Our approach is to associate compiled communications and a circuit switched interconnection network. This paper presents the motivations for this principle, the hardware and software issues and the design of a first prototype. The expected performance are a sustained aggregate bandwidth of more than 500 GBytes/s and an overall latency less than 270 ns, for a large implementation (4K inputs) with the current available technology. >

A gradual neural-network approach for frequency assignment in satellite communication systems

Abstract: A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of N/spl times/M binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.