B. Hassibi | Semantic Scholar

How much training is needed in multiple-antenna wireless links?

B. HassibiB. Hochwald
Computer Science
IEEE Trans. Inf. Theory
1 April 2003

This work compute a lower bound on the capacity of a channel that is learned by training, and maximize the bound as a function of the received signal-to-noise ratio (SNR), fading coherence time, and number of transmitter antennas.

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On the capacity of MIMO broadcast channels with partial side information

M. SharifB. Hassibi
Computer Science
IEEE Transactions on Information Theory
1 February 2005

This work proposes a scheme that constructs M random beams and that transmits information to the users with the highest signal-to-noise-plus-interference ratios (SINRs), which can be made available to the transmitter with very little feedback.

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High-rate codes that are linear in space and time

B. HassibiB. Hochwald
Computer Science
IEEE Trans. Inf. Theory
1 July 2002

This work proposes a high-rate coding scheme that can handle any configuration of transmit and receive antennas and that subsumes both V-BLAST and many proposed space-time block codes as special cases and shows that their performance is generally superior to earlier proposed methods over a wide range of rates and signal-to-noise ratios (SNRs).

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On the sphere-decoding algorithm I. Expected complexity

B. HassibiH. Vikalo
Computer Science
IEEE Transactions on Signal Processing
1 August 2005

For the "sphere decoding" algorithm of Fincke and Pohst, a closed-form expression is found for the expected complexity, both for the infinite and finite lattice, which suggests that maximum-likelihood decoding, which was hitherto thought to be computationally intractable, can be implemented in real time.

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Distributed Space-Time Coding in Wireless Relay Networks

Y. JingB. Hassibi
Computer Science, Business
IEEE Transactions on Wireless Communications
1 December 2006

The idea of space-time coding devised for multiple-antenna systems to the problem of communications over a wireless relay network with Rayleigh fading channels is applied and it is shown that for high SNR, the pairwise error probability (PEP) behaves as (logP/P)min{TH}, with T the coherence interval.

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Second Order Derivatives for Network Pruning: Optimal Brain Surgeon

B. HassibiD. Stork
Computer Science
NIPS
30 November 1992

Of OBS, Optimal Brain Damage, and magnitude-based methods, only OBS deletes the correct weights from a trained XOR network in every case, and thus yields better generalization on test data.

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The Secrecy Capacity of the MIMO Wiretap Channel

F. OggierB. Hassibi
Computer Science
IEEE Transactions on Information Theory
9 October 2007

This paper compute the perfect secrecy capacity of the multiple antenna MIMO broadcast channel, where the number of antennas is arbitrary for both the transmitter and the two receivers.

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An efficient square-root algorithm for BLAST

B. Hassibi
Computer Science, Business
IEEE International Conference on Acoustics…
5 June 2000

An efficient square-root algorithm for the nulling and cancellation step of BLAST is developed, which reduces the computational cost of the scheme by 0.7 M, and makes it attractive for implementation in fixed-point (rather than floating-point) architectures.

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Linear Estimation (Information and System Sciences Series)

T. KailathA. SayedB. Hassibi
Computer Science
2000

Optimal Brain Surgeon and general network pruning

B. HassibiD. StorkG. Wolff
Computer Science
IEEE International Conference on Neural Networks
28 March 1993

The use of information from all second-order derivatives of the error function to perform network pruning to improve generalization, simplify networks, reduce hardware or storage requirements, increase the speed of further training, and, in some cases, enable rule extraction is investigated.

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