Beamforming
Beamforming
is the most common spatial processing technique utilized by an antenna
array. A beamformer can be regarded as a spatial filter that separates
the desired signal from interfering signals given that all the signals
share the same frequency band and originate from different spatial
locations. It essentially weighs and sums the signals from the
different antenna elements to optimize the quality of the desired
signal. In addition to interference rejection and multipath fading
mitigation, a beamformer also increases the antenna gain in the
direction of the desired user.
Common beamforming criteria include Minimum Mean
Square Error (MMSE) [2], Maximum Signal to Interference and Noise
Ratio (MSINR) [2], Maximum Signal to Noise Ratio (MSNR) [5], Constant
Modulus (CMA) [6], and Maximum Likelihood (ML) [2].
Beamforming is
typically implemented using adaptive techniques. The adaptive
array algorithms are broadly classified as: trained and blind
algorithms [8].
Trained algorithms use a finite set of training symbols to adapt the
weights of the array and maximize the SINR. The processor in the
adaptive array has a pre-stored training sequence and the array adapts
its weights when the training signal is transmitted by the
transmitter. This technique requires synchronization. These algorithms
work very well, but the cost paid is the excess transmission time
or wastage of bandwidth. The trained algorithms are classified based on
their adaptation criteria including least-mean squares method (LMS),
sample matrix inversion (SMI) or least-squares method (LS), and
recursive least-squares method (RLS). The fundamental assumptions
behind these minimization techniques is that the error vector follows
a Gaussian probability density function.
Blind algorithms do not require training signals to adapt their
weights . Therefore these algorithms save transmission bandwidth.
Blind algorithms can be classified as property restoral algorithms,
channel estimation algorithms, and despread and re-spread algorithms. Property restoral algorithms restore certain
properties of the desired signal and hence enhance the SINR. The
property that is being restored may be the modulus or the spectral
coherence. Blind property restoral algorithms can be classified as
Constant Modulus (CM) algorithms, Spectral self-Coherence Restoral
(SCORE) algorithms, and decision directed (DD) algorithms.
Presentations
[A]
Beamforming at the Transmitter
[B]
Beamforming at the Receiver
[C]
MPRG Smart Antenna Tutorial
References
[1]
Jefferey. H. Reed, “ Software Radio : A modern Approach to Radio
Engineering”, Prentice Hall
[2] J. Litva and T. K. Lo, Digital Beamforming in Wireless Communications.
Boston
,
MA
: Artech House, 1996.
[3] Joseph Liberti, Theodore S. Rappaport, “Smart Antennas for Wireless
Communications: Is-95 and Third Generation Cdma Applications”,
Prentice Hall
[4] R. A. Monzingo and T. W. Miller, “Introduction to Adaptive
Antennas”,
New York
: Wiley, 1980.
[5] F.Alam, D.Shim, and B.D. Woerner,
"Comparison of Low Complexity
Algorithms for MSNR
Beamforming," submitted to VTC Spring , May 2002,
Birmingham
,
Alabama
,
USA
.
[6] T. E. Biedka, A General Framework for the Analysis and Development of
Blind Adaptive Algorithms.
Ph.D. dissertation, Virginia Tech, Oct 2001.
[7] R.M. Buehrer, A.G. Kogiantis, S.-C. Liu,
J.-A. Tsai, and D. Uptegrove, "Intelligent Antennas for Wireless
Communications – Uplink," Bell Labs Technical Journal, vol. 4,
no. 3, pp. 73-103, July-September 1999.
[8] Paul Petrus, Novel Adaptive Array Algorithms
and Their Impact on Cellular System Capacity Ph.D.
dissertation, Virginia Tech, March 1997.
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