386 research outputs found
Compound relay channel with informed relay and destination
No full textA two-state compound relay channel is considered where the relay and the destination are informed about the channel state while the source is not. Achievable rates and upper bounds are derived for discrete memoryless and Gaussian models, and specialized to a scenario with orthogonal components. It is shown that, apart from some special cases, optimality conditions valid for decode-and-forward (DF)-based solutions on a standard relay channel do not carry over to a compound setting, and more fl exible transmission strategies are generally advantageous. For instance, partial decode-and-forward (PDF) that superimposes transmission of three layers and uses joint decoding at the destination performs better than the standard two-layer PDF with successive decoding, even when the latter is optimal for the regular relay channel. Moreover, the capacity is derived in the special case in which the relay is not active in one state. Extension to the broadcast coding approach, as an alternative to the compound model, is also discussed. ©2009 IEEE
Improved Bounds on the Number of Support Points of the Capacity-Achieving Input for Amplitude Constrained Poisson Channels
No full textIEEE Transactions on Information Theory: Vol. 59, No. 2, February 2013
No full text1. MMSE of "Bad" Codes / R. Bustin, S. Shamai
2. Converse Coding Theorems for Identification via Channels / Y. Oohama
3. Computable Bounds for Rate Distortion with Feed Forward for Stationary and Ergodic Sources / I. Naiss, H. H. Permuter
4. Classification of Homogeneous Data with Large Alphabets / B. G. Kelly, A. B. Wagner, T. Tularak, P. Viswanath
5. Guesswork, Large Deviations and Shannon Entropy / M. M. Christiansen, K. R. Duffy
6. Entropic Inequalities and Marginal Problems / T. Fritz, R. Chaves
7. On MMSE Crossing Properties and Implications in Parallel Vector Gaaussian Channels / R. Bustin, M. Payaro, D. P. Palomor
8. The Approximate Capacity of the Gaussian N-Relay Diamond Network / U. Niesen, S. N. Diggavi
9. Half-Duplex Relaying Over Slow Pading Channels Based on Quantize-and-Forward / S. Yao, T. T. Kim, M. Skoglund, H. V. Poor
10. Effective Capacity of Two-Hop Wireless Communication Systems / D. Qiao, M. C. Gursoy, S. Velipasalar
11. Capacity Bounds and Exact Results for the Cognitive Z-Interference Channel / N. Liu, I. Maric, A. J. Goldsmith, S. Shamai
12. Futher Results on the Asymptotic Mutual Information of Rician Fading MIMO Channels / G. Tricco
13. Multicarrier Beamforming With Limited Feedback: A Rate Distortion Approach / M. Xu, D. Guo, M. L. Honig
14. Capacity of DNA Data Embedding Under Substitution Mutations / F. Balado
15. Capacity of a Diffusion-Based Molecular Communication System With Channel Memory and Molecular Noise / M. Pierobon, I. F. Akyildiz
Etc
Latency Limits for Content Delivery in a Fog-RAN with D2D Communication
No full textR. Karasik, O. Simeone and S. Shamai (Shitz), “Latency Limits for Content Delivery in a Fog-RAN with D2D Communication”, The 2019 IEEE International Symposium on Information Theory (ISIT2019), July 7-12, 2019, Paris, France
The capacity of the frequency/time-selective fading channel
No full textWe find the capacity of discrete-time channels subject to both frequency-selective and time-selective fading, where the channel output is observed in additive Gaussian noise. A coherent model is assumed where the fading coefficients are known at the receiver. Capacity depends on the first-order distributions of the fading processes in frequency and in time, which are assumed to be independent of each other, and a simple formula is given when one of the processes is iid and the other one is sufficiently mixing. When the frequency-selective fading coefficients are known also to the transmitter, we show that the optimum normalized power spectral density is the waterfilling power allocation for a reduced signal-to-noise ratio, where the gap to the actual signal-to-noise ratio depends on the fading distributions
Broadcast approach for the sparse-input random-sampled MIMO Gaussian channel
No full textWe consider a MIMO (linear Gaussian) channel where the inputs are turned on and off at random, and the outputs are sampled at random with probability p. In particular, for a given probability of 'on' input q (input sparsity), we consider a scenario where the transmitter wishes to send information to a family of possible receivers characterized by different random sampling rates p ��� [0,1]. For this setting, we focus on the broadcast approach, i.e., a coding technique where the transmitter sends information encoded into superposition layers, such that the number of decoded layers depends on the receiver sampling rate p. We obtain a method for calculating the power allocation across the layers for given statistics of the MIMO channel matrix in order to maximize the system weighted sum rate for arbitrary non-negative weighting function w(p). In particular, we provide analytical solutions both for iid and Haar distributed MIMO channel matrices. The latter case accounts also for DFT matrices (see [1]), with application to sparse spectrum signals with random sub-Nyquist sampling. �� 2014 IEEE
Information Bottleneck for an Oblivious Relay with Channel State Information: The Scalar Case
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