B. Hassibi

Publications592
h-index73
Citations27,989
Highly Influential Citations2,226
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How much training is needed in multiple-antenna wireless links?
TLDR
We 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, fading coherence time, and number of transmitter antennas. Expand
  • 2,138
  • 234
  • PDF
On the capacity of MIMO broadcast channels with partial side information
TLDR
We propose 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. Expand
  • 1,262
  • 209
  • PDF
High-rate codes that are linear in space and time
TLDR
We propose 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. Expand
  • 1,456
  • 178
  • PDF
On the sphere-decoding algorithm I. Expected complexity
  • B. Hassibi, H. Vikalo
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • 1 August 2005
TLDR
The problem of finding the least-squares solution to a system of linear equations where the unknown vector is comprised of integers, but the matrix coefficient and given vector are comprised of real numbers, arises in many applications. Expand
  • 1,156
  • 108
  • PDF
Distributed Space-Time Coding in Wireless Relay Networks
  • Y. Jing, B. Hassibi
  • Mathematics, Computer Science
  • IEEE Transactions on Wireless Communications
  • 1 December 2006
TLDR
We apply 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. Expand
  • 976
  • 96
  • PDF
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
TLDR
We investigate the use of information from all second order derivatives of the error function to perform network pruning (i.e., removing unimportant weights from a trained network) in order to improve generalization, simplify networks, reduce hardware or storage requirements, increase the speed of further training, and enable rule extraction. Expand
  • 1,341
  • 66
  • PDF
On the capacity of MIMO broadcast channel with partial side information
  • M. Sharif, B. Hassibi
  • Engineering
  • The Thrity-Seventh Asilomar Conference on Signals…
  • 9 November 2003
TLDR
In this paper, we propose 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. Expand
  • 314
  • 66
Linear Estimation (Information and System Sciences Series)
  • 634
  • 63
An efficient square-root algorithm for BLAST
  • B. Hassibi
  • Computer Science, Mathematics
  • IEEE International Conference on Acoustics…
  • 5 June 2000
TLDR
We develop an efficient square-root algorithm for the nulling and cancellation step of Bell Labs Layered Space-Time, which can reduce the computational cost of BLAST by 0.7 MFlops/s. Expand
  • 498
  • 41
Indefinite-Quadratic Estimation And Control
  • 270
  • 39
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