Capacity enhancement using adaptive arrays in an AMPS system

Petrus, P.; Ertel, R.B.; Reed, Jeffrey H.

doi:10.1109/25.704827

Cited by 41 publications

(31 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike the MMSE solution (8), there exists no closed-form MBER solution. In theory, there is no guarantee that the above conjugate gradient algorithm can always find a global minimum point of the BER surface .…”

Section: Mber Beamforming Solutionmentioning

confidence: 99%

“…Classically, the beamformer's weight vector is determined by minimizing the MSE term of , which leads to the following MMSE solution: (8) with being the first column of . Although the system matrix is generally unknown, the MMSE solution can be readily realized using the block-data based adaptive SMI algorithm [11], [12].…”

Section: System Modelmentioning

confidence: 99%

“…The BER curves in Fig. 1 were computed using the theoretical BER expression (15) with the MMSE solution given by (8) and the MBER solution obtained by numerical optimization based on the simplified conjugate gradient algorithm portrayed in Section III. For this example, the superior performance of the MBER beamforming technique over the MMSE benchmarker scheme becomes evident.…”

Section: Simulation Studymentioning

confidence: 99%

See 2 more Smart Citations

Adaptive minimum bit-error rate beamforming

Chen

Ahmad

Hanzo

2005

IEEE Trans. Wireless Commun.

122

View full text Add to dashboard Cite

Abstract-An adaptive beamforming technique is proposed based on directly minimizing the bit-error rate (BER). It is demonstrated that this minimum BER (MBER) approach utilizes the antenna array elements more intelligently than the standard minimum mean square error (MMSE) approach. Consequently, MBER beamforming is capable of providing significant performance gains in terms of a reduced BER over MMSE beamforming. A block-data adaptive implementation of the MBER beamforming solution is developed based on the Parzen window estimate of probability density function. Furthermore, a sample-by-sample adaptive implementation is considered, and a stochastic gradient algorithm, referred to as the least bit error rate, is derived. The proposed adaptive MBER beamforming technique provides an extension to the existing work for adaptive MBER equalization and multiuser detection.Index Terms-Adaptive beamforming, bit-error rate (BER), mean square error, probability density function (pdf), smart antenna.

show abstract

Section: Mber Beamforming Solutionmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

See 1 more Smart Citation

Adaptive minimum bit-error rate beamforming

Chen

Ahmad

Hanzo

2005

IEEE Trans. Wireless Commun.

122

View full text Add to dashboard Cite

show abstract

“…The ever-increasing demand for mobile communication capacity has motivated the development of adaptive antenna array-assisted spatial processing techniques [1][2][3][4][5][6][7][8][9][10][11][12] in order to further improve the achievable spectral efficiency. A technique that has shown real promise in achieving substantial capacity enhancements is the use of adaptive beamforming with antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

Adaptive beamforming for binary phase shift keying communication systems

2007

View full text Add to dashboard Cite

The paper revisits adaptive beamforming assisted receiver for multiple antenna aided multiuser systems that employ binary phase shift keying (BPSK) modulation. The standard minimum mean square error (MMSE) design is based on the criterion of minimising the mean square error (MSE) between the beamformer's desired output and complex-valued beamformer's output. Since the desired output for BPSK systems is real-valued, minimising the MSE between the beamformer's desired output and real-part of the beamformer's output can significantly improve the bit error rate (BER) performance, and we refer to this alternative MMSE design as the real-valued MMSE (RV-MMSE) to contrast to the standard complex-valued MMSE (CV-MMSE) design. The minimum BER (MBER) design however still outperforms the RV-MMSE solution, particularly for overloaded systems where degree of freedom of the antenna array is smaller than the number of BPSK users. Adaptive implementation of this RV-MMSE beamforming design is realised using a least mean square (LMS) type adaptive algorithm, which we refer to as the RV-LMS, in comparison to the standard CV-LMS algorithm. The RV-LMS adaptive beamformer is shown to have a similar computational complexity as the adaptive MBER beamforming implementation known as the least bit error rate (LBER), imposing only half of the computational requirements of the CV-LMS algorithm. r

show abstract

“…However, their practical application is restricted by their algorithmic complexity and computational cost, which results in the development of simpler models. A simplified approach is the geometrical-based modeling that allows an approximate but adequate evaluation of system performance (Petrus et al, 1998;Au et al 2001;Zhang et al, , 2006Baltzis & Sahalos, 2005, 2009bPanagopoulos et al, 2007;Baltzis, 2008). Another important issue in the study of a wireless system is the prediction of signal attenuation (Parsons, 2000;Fryziel et al 2002;Baltzis, 2009).…”

mentioning

confidence: 99%

Hexagonal vs Circular Cell Shape: A Comparative Analysis and Evaluation of the Two Popular Modeling Approximations

Baltzis¹

2011

Cellular Networks - Positioning, Performance Analysis, Reliability

View full text Add to dashboard Cite

Capacity enhancement using adaptive arrays in an AMPS system

Cited by 41 publications

References 12 publications

Adaptive minimum bit-error rate beamforming

Adaptive minimum bit-error rate beamforming

Adaptive beamforming for binary phase shift keying communication systems

Hexagonal vs Circular Cell Shape: A Comparative Analysis and Evaluation of the Two Popular Modeling Approximations

Contact Info

Product

Resources

About