Different Types Of Adaptive Beamforming

DIFFERENT TYPES OF ADAPTIVE BEAMFORMING

1. Adaptive beamforming for uplink

Reasons studies for uplink:  traditionally used for radar, remote sensing and sonar reception system  spatial channel information available on the uplink

Adaptive criteria  optimum weights using different criteria are all given by the Wiener solution because it provides the upper limit on the theoretical adaptive beamforming steady-state performance

Adaptive Algorithms  LMS algorithm: simple to implement , but limited in dynamic range  over which it operates. Required power control or alternatively use normalized LMS algorithm  SMI technique:  fast convergence rate but  increase computational complexity & numerical instability  RLS algorithm:  reduce computational complexity while maintaining similar performance, convergence rate faster than LMS provided that SNR is high, but  has forgotten factor that is very dependent on fading rate of the channel  Conjugate gradient method  Eigenanalysis algorithm  Rotational invariance based method  Linear least squares error algorithm (LSSE)  Hopfield neural network

Reference Signals  If explicit reference signal available in communication it should be used as much as possible for less complexity, high accuracy, fast convergence

a) spatial reference:  referred to as angle of arrival(AOA) information of desired signal and its multipath components  AOA estimation techniques:  wavenumber estimation: based on decomposition of a covariance matrix whose terms consist of estimates of the correlation between the signals at the elements of an array antenna. Example: Multiple signal classification (MUSIC), modified forward-backward linear prediction (FBLP), Principal Eigenvector Gram-Schmidt (PEGS), Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT)  parametric estimation: variety of maximum likelihood estimation (MLE) : particular likelihood function is formulated for the given radio signals, high computational complexity    requirement for array calibration, extra processing load required for estimating AOA

b) temporal reference: may be a pilot signal that is correlated with the wanted signal, or known PN code in CDMA

Blind Adaptive Beamforming  when explicit reference signal is not available

a) Constant Modulus Algorithm (CMA)  For both compensating fading and canceling cochannel interference  Applied to advanced mobile phone services ( AMPS), IS-54 signals, GMSK signals, 16-QAM signals

b) Decision-Directed Algorithm   Low cost since not computationally intensive, and no array calibration required   fast convergence, typically within 50 symbols  locks on desired signal with probability of 99.9% at SIR levels as low as 1dB  cochannel rejection is typically more than 20 dB  implementation based on incoherent differential binary phase-shift keying (DBPSK) demodulation and LMS algorithm  converge faster than CMA and SCORE

c) Cyclostationary Algorithm  Developed and applied to AMPS  AMPS exhibit cyclostationary properties due to presence of supervisory audio tone  Show considerable improvement in MSE compared to the case of omnidirectional antennas  Cylic beamforming can be applied to GMSK signals, only require that cochannel users have slightly different frequencies. Used in GSM and DECT. Shown capacity improvement 2. Adaptive Beamforming for Downlink

 Objective: to maximize the received signal strength at the desired mobile and to minimize the interference to other mobiles and adjacent base stations, thereby maximizing the downlink SINR  If transfer function of the channel at the downlink is known, the downlink SINR can be maximized by multiplying the desired signal with a set of downlink weights.  The weights are a scale version of the uplink weights, provided that the frequency of both links is same and the channel is relatively static during reception and transmission. Weight reuse can be applied to TDD systems ( CT2/CT2+, DECT, PHP, DCS1800).  In FDD system (IS-54, IS-95, GSM), weight reuse cannot be used because far frequency separation  Essence of the problem: to estimate the downlink transfer function  Feedback technique was proposed. Using probing signal transmitted by BS. The mobile measure it own response to the probe signal and report them back to BS. The transfer function is estimated using the report. Simple but require complete redesign of protocols and signaling & applicable only for slowly change environments  Other way: mobile directly transmit a narrowband testing signal at downlink frequency so that the BS can directly estimate the downlink channel transfer function from that. Not interrupt the normal uplink transmission but still require complete protocol redesign & require additional hardware in MS.  Approached using AOA info. Downlink weights are derived by maximizing SIRN based on the same AOA.  Use fixed multiple beams for both reception and transmission at the BS. On uplink, BS determine the direction of the path on which the strongest component of the desired signal arrived. On downlink, the BS points the beam in the corresponding direction. Not optimal, but SINR improved since narrowband signal is pointed, and can use high power beam to boost the SINR.

Ref: John Litva, Titus Kwok-Yeung Lo, “ Digital Beamforming in Wireless Communications”, pg 157-184, Artech House Publisher, 1996