[7] Frank Gross, âSmart Antenna For Wireless Communicationâ Mcgraw- hill, September 14, 2005. [8] M.Kamaraju, K.Ramakrishna, K.Ramanjaneyulu, âA Novel ...
IEEE International Conference on Computer, Communication and Control, MGI Indore, INDIA. September 10 -12, 2015. > PAPER IDENTIFICATION NUMBER (IC4_5214)
PAPER IDENTIFICATION NUMBER (IC4_5214) < algorithm plays a vital role in meeting the desired goals of the communication network. III. ADAPTIVE BEAM-FORMING ALGORITHMS II. BEAM-FORMING Beam forming is an alternative name given for spatial filtering where, with appropriate analog or digital signal processing, an array of antennas can be steered in a way to block the reception of radio signals coming from specified direction. Beam forming technique is capable of controlling the 'directionality of' or 'sensitivity to' a particular radiation pattern [2]. Beam forming is a major concern for mobile communication and has been used for many years in different radio applications such as communications, surveillance, radar and with different array sensors in sonar and audio fields .This method creates the radiation pattern of the antenna array by adding the phases of the signals in the desired direction and by nulling the pattern in the unwanted direction. The phases (the inter-element phase) and usually amplitudes are adjusted to optimize the received signal [4]
d(n A 1 A 2 A 3
y(n)
+
+
e(n) AN
Adaptive algorithms are formulated on prescribed performance criteria which are being implemented by a set of iterative equations to meet those criteria. The digital signal processor interprets the incoming data information, determines the complex weights (amplification and phase information) and multiplies the weights to each element output to optimize the array pattern. The optimization is based on a particular criterion, which minimizes the contribution from noise and interference while producing maximum beam gain at the desired direction. There are several Adaptive beam-forming [6] algorithms varying in complexity based on different criteria for updating and computing the optimum weights. The two major types of adaptive algorithms are blind and non-blind. Non-blind algorithms require a reference signal to detect the desired signal and update the complex weights. Algorithms in this category are LMS, RLS, SMI and CGM. In contrast to non-blind algorithms, blind algorithms do not need a reference signal to find the complex weights. Blind algorithms are CMA, In this paper we have analyzed least mean square (LMS) algorithm, decision feedback equalizer based least mean square (DFE-LMS) algorithms that requires a reference signal. The weight vector W is calculated using the statistics of signal x(n) arriving from the antenna array. An adaptive processor will minimize the error e(n) between a desired signal d(t) and the array output y(n). (a) Least Mean Square Algorithm
Adaptive Algorithm W1
W2
W3
WN
Fig.1.Block diagram for Adaptive Beam forming network
Beamformer of N antenna elements is able to steer the array on a desired direction and rejects at maximum N-1 interfering signals coming from N-1 different directions. This leads to consider the array as a FIR filter in the spatial domain, then the weights can be computed similarly to the coefficients of a standard FIR filter. Taking FFT of the amplitude coefficients Wn, the radiation pattern S(α)of the array obtained with the beamformer, is given by the expression:
Where
rad/m, λ(m) is the wavelength of the incident
wave. d is the spacing between adjacent sensors of the array.
The Least Mean Square (LMS) algorithm introduced by Widrow and Hoff in 1959 is the simplest adaptive filtering algorithm based on gradient approach [2]. It comprises recent observations and minimizes the mean square error [3]. The direction of gradient vector is opposite to that of steepest descent (SD). It computes the weight vector recursively using the equations. For each k
Where; d(n) = reference signal x(n) = input data vector w(n) = weight vector e(n) = error signal µ = step size Stability and convergence rate of LMS algorithm is controlled by the scalar constant µ. The step size should be set in a range in which convergence is insured [3]
IEEE International Conference on Computer, Communication and Control, MGI Indore, INDIA. September 10 -12, 2015. > PAPER IDENTIFICATION NUMBER (IC4_5214)
PAPER IDENTIFICATION NUMBER (IC4_5214) < in the system. It is through adaptive beam forming approach that base station can form narrower beams towards the desired user and nulls towards interfering users, considerably improving the signal-to-interference-plus-noise ratio. For adaptive beam forming the concern is to select the best adaptive algorithm. There are several benefits of smart antenna; the most important is higher network capacity. It increase network capacity by precise control of signal nulls quality and mitigation of interference combine to frequency reuse reduce distance (or cluster size), improving capacity. Smart antenna demands high processing bandwidth. The popularity of smart antenna is gaining strength day by day because smart antenna in wireless communication is trying to play the role that fibre optic cable play in wired communication system.
Fig. 4: Comparison of LMS and DFE-LMS on the basis of power
REFERENCES [1] Chhaya Singh, B G Hogade, “Implementation of an Adaptive Beam Forming Antenna for Radio Technology” International Journal of Innovative Science and Modern Engineering, Vol. 2, No. 9,pp.17-20, August 2014. [2] Balasem. S.S, S.K.Tiong, S.P.Koh, “Beamforming Algorithms Technique by Using MVDR and LCMV” Special section for proceeding of International E-Conference on Information Technology and Applications, Vol. 2, No. 5, pp.315-324, May 2012. [3] Susmita Das; “A Smart Antenna Design for Wireless Communication using Adaptive Beam-forming Approach’’, IEEE xplore, 2007 International symposium on 23 April. 2009. [4] Abdul Aziz, M.Ali Qureshi, M.Junaid Iqbal “Performance and Quality Analysis of Adaptive Beamforming Algorithms (LMS, CMA, RLS, CGM) for Smart Antennas”, International Conference on Computer and Electrical Engineering, Vol. 6, pp. 302-306, 2010. [5] Amara Prakasa Rao, N.V.S.N.Sarma, “Adaptive Beamforming Algorithms for Smart Antenna System” WSEAS Transactions on Communications, Vol. 13, 2014. [6] D.M. Motiur Rahaman, Md.Moswer Hossain, Md.Masud Rana ,“ Least Mean Square(LMS)for Smart Antenna”,Universal Journal of Communications and Network,pp.16-21, 2013.
[7] Frank Gross, “Smart Antenna For Wireless Communication” Mcgrawhill, September 14, 2005 [8] M.Kamaraju, K.Ramakrishna, K.Ramanjaneyulu, “A Novel Adaptive Beamforming RLMS Algorithm for Smart Antenna System”, International Journal of Computer Applications, Vol. 86, No.5, pp.27-32 January 2014. [9] Kapil Dungriyal, S.Anand, Sriram Kumar, “Performance of MIR-LMS Algorithm For Adaptive Beamforming in Smart Antenna”,Proceedings of IRF International Conference,pp.27-31, April 2014.
