Results 1  10
of
12
SpaceTime Autocoding
 IEEE Trans. Inform. Theory
, 1999
"... Prior treatments of spacetime communications in Rayleigh flat fading generally assume that channel coding covers either one fading intervalin which case there is a nonzero "outage capacity"or multiple fading intervalsin which case there is a nonzero Shannon capacity. However, we ..."
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Cited by 33 (5 self)
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Prior treatments of spacetime communications in Rayleigh flat fading generally assume that channel coding covers either one fading intervalin which case there is a nonzero "outage capacity"or multiple fading intervalsin which case there is a nonzero Shannon capacity. However, we establish conditions under which channel codes span only one fading interval and yet are arbitrarily reliable. In short, spacetime signals are their own channel codes. We call this phenomenon spacetime autocoding, and the accompanying capacity the spacetime autocapacity. Let an Mtransmitterantenna, Nreceiverantenna Rayleigh flat fading channel be characterized by an M \Theta N matrix of independent propagation coefficients, distributed as zeromean, unitvariance complex Gaussian random variables. This propagation matrix is unknown to the transmitter, it remains constant during a T symbol coherence interval, and there is a fixed total transmit power. Let the coherence interval and number of ...
Cutoff Rate and Signal Design for the Quasistatic Rayleigh Fading SpaceTime Channel
, 2001
"... We consider the computational cutoff rate and its implications on signal design for the complex quasistatic Rayleigh at fading spatiotemporal channel under a peak power constraint where neither transmitter nor receiver know the channel matrix. The cutoff rate has an integral representation which ..."
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Cited by 14 (1 self)
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We consider the computational cutoff rate and its implications on signal design for the complex quasistatic Rayleigh at fading spatiotemporal channel under a peak power constraint where neither transmitter nor receiver know the channel matrix. The cutoff rate has an integral representation which is an increasing function of the distance between pairs of complex signal matrices. When the analysis is restricted to finite dimensional sets of signals interesting characterizations of the optimal rateachieving signal constellation can be obtained. For arbitrary finite dimension, the rateoptimal constellation must admit an equalizer distribution, i.e., a positive set of signal probabilities which equalizes the average distance between signal matrices in the constellation. When the number N of receive antennas is large the distanceoptimal constellation is nearly rateoptimal. When the number of matrices in the constellation is less than the ratio of the number of time samples to the number of transmit antennas, the rateoptimal cutoff rate attaining constellation is a set of equiprobable mutuallyorthogonal unitary matrices. When the SNR is below a specified threshold the matrices in the constellation are rank one and the cutoff rate is achieved by applying all transmit power to a single antenna and using orthogonal signaling. Finally, we derive recursive necessary conditions and sucient conditions for a constellation to lie in the feasible set.
Cutoff rate and signal design for the Rayleigh fading space–time channel
 IEEE TRANS. INFORM. THEORY
, 2001
"... We consider the singleuser computational cutoff rate for the complex Rayleigh flat fading spatiotemporal channel under a peak power constraint. Determination of the cutoff rate requires maximization of an average error exponent over all possible spacetime codeword probability distributions. This ..."
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Cited by 4 (0 self)
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We consider the singleuser computational cutoff rate for the complex Rayleigh flat fading spatiotemporal channel under a peak power constraint. Determination of the cutoff rate requires maximization of an average error exponent over all possible spacetime codeword probability distributions. This error exponent is monotone decreasing in a measure of dissimilarity between pairs of codeword matrices. For low SNR the dissimilarity function reduces to a trace norm of differences between outerproducts of pairs of codewords. We characterize the cutoffrate and the rate achieving constellation under different operating regimes depending on the number of transmit and receive antennas, the number of codewords in the constellation, and the received SNR.
MultipleAntenna Capacity in a Deterministic . . .
 SUBMITTED TO IEEE TRANSACTIONS ON INFORMATION THEORY, SEPT. 2001
, 2001
"... We calculate the capacity of a multipleantenna wireless link in a Rician fading channel... ..."
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Cited by 2 (0 self)
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We calculate the capacity of a multipleantenna wireless link in a Rician fading channel...
Cuto Rate and Signal Design for the Quasistatic Rayleigh Fading SpaceTime Channel 1
, 2001
"... We consider the computational cuto rate and its implications on signal design for the complex quasistatic Rayleigh
at fading spatiotemporal channel under a peak power constraint where neither transmitter nor receiver know the channel matrix. The cuto rate has an integral representation which ..."
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Cited by 1 (1 self)
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We consider the computational cuto rate and its implications on signal design for the complex quasistatic Rayleigh
at fading spatiotemporal channel under a peak power constraint where neither transmitter nor receiver know the channel matrix. The cuto rate has an integral representation which is an increasing function of the distance between pairs of complex signal matrices. When the analysis is restricted to nite dimensional sets of signals interesting characterizations of the optimal rateachieving signal constellation can be obtained. For arbitrary nite dimension, the rateoptimal constellation must admit an equalizer distribution, i.e., a positive set of signal probabilities which equalizes the average distance between signal matrices in the constellation. When the number N of receive antennas is large the distanceoptimal constellation is nearly rateoptimal. When the number of matrices in the constellation is less than the ratio of the number of time samples to the number of transmit antennas, the rateoptimal cuto rate attaining constellation is a set of equiprobable mutuallyorthogonal unitary matrices. When the SNR is below a specied threshold the matrices in the constellation are rank one and the cuto rate is achieved by applying all transmit power to a single antenna and using orthogonal signaling. Finally, we derive recursive necessary conditions and suÆcient conditions for a constellation to lie in the feasible set.
On Computational Cutoff Rate for SpaceTime Coding
"... We consider the computational cuto rate for the complex Rayleigh at fading spatiotemporal channel under a peak power constraint, where the propagation matrix is unknown at both transmitter and receiver. Determination of the cuto rate requires maximization of an average error exponent over all pos ..."
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Cited by 1 (0 self)
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We consider the computational cuto rate for the complex Rayleigh at fading spatiotemporal channel under a peak power constraint, where the propagation matrix is unknown at both transmitter and receiver. Determination of the cuto rate requires maximization of an average error exponent over all possible probability distributions on the signal matrices. This error exponent is monotone decreasing in a measure of dissimilarity between pairs of signal matrices. For low SNR the dissimilarity function reduces to a trace norm of the dierence between the outerproduct of each signal matrix. Under the practical constraint of nite dimensional signal constellations, dierent characterizations of the optimal constellation are obtained. For arbitrary nite dimension, the optimal constellation must admit an equalizer distribution, i.e., a positive set of signal probabilities which equalizes the conditional decoding error probabilities. The cuto rate optimization reduces to maximization of a q...
SIGNAL DESIGN FOR MULTIPLEANTENNA SYSTEMS AND WIRELESS NETWORKS
, 2007
"... This dissertation was presented by ..."
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Multiple Antennas in Wireless Communications: Array Signal Processing and Channel Capacity
, 2001
"... We investigate two aspects of multipleantenna wireless communication systems in this thesis: 1) deployment of an adaptive beamformer array at the receiver; and 2) spacetime coding for arrays at the transmitter and the receiver. In the first part of the thesis, we establish sufficient conditions fo ..."
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We investigate two aspects of multipleantenna wireless communication systems in this thesis: 1) deployment of an adaptive beamformer array at the receiver; and 2) spacetime coding for arrays at the transmitter and the receiver. In the first part of the thesis, we establish sufficient conditions for the convergence of a popular least mean squares (LMS) algorithm known as the sequential Partial Update LMS Algorithm for adaptive beamforming. Partial update LMS (PULMS) algorithms are reduced complexity versions of the full update LMS that update a subset of filter coefficients at each iteration. We introduce a new improved algorithm, called Stochastic PULMS, which selects the subsets at random at each iteration. We show that the new algorithm converges for a wider class of signals than the existing PULMS algorithms.
Antenna Arrays in Wireless Communications
, 2001
"... this paper we assume that d k itself obeys an FIR model given by d k = W y opt X k + n k where W opt are the coefficients of an FIR model given by W opt = [w 1;opt : : : wN;opt ] T . Here fn k g is assumed to be a zero mean i.i.d sequence that is independent of the input sequence X k . For desc ..."
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this paper we assume that d k itself obeys an FIR model given by d k = W y opt X k + n k where W opt are the coefficients of an FIR model given by W opt = [w 1;opt : : : wN;opt ] T . Here fn k g is assumed to be a zero mean i.i.d sequence that is independent of the input sequence X k . For description purposes we will assume that the filter coefficients can be divided into P mutually exclusive subsets of equal size, i.e. the filter length N is a multiple of P . For convenience, define the index set S = f1; 2; : : : ; Ng. Partition S into P mutually exclusive subsets of equal size, S 1 ; S 2 ; : : : ; S P . Define I i by zeroing out 15 the j th row of the identity matrix I if j = 2 S i . In that case, I i X k will have precisely N P nonzero entries. Let the sentence "choosing S i at iteration k" stand to mean "choosing the weights with their indices in S i for update at iteration k"