Presentation Internalc.pptx

Supervisor
Dr. Mukesh Yadav
Associate Professor
SAGE University, Indore
“PERFORMANCE ANALYSIS OF DIGITAL BEAMFORMING
ALGORITHM FOR WIRELESS COMMUNICATION SYSTEM”
Presentation for Ph.D. Pre Defense Viva Voce
In
Electronics & Communication Engineering
Under
Faculty of Engineering & Technology
Presented By -
Mr. Rahil Khan
Ph. D. Scholar

Outlines
Date :-
Department of Electronics & Communication
2
 Introduction
 Motivation
 Literature Review
 Rationale
 Research Objectives
 Research Methodology
 Outcomes
 Conclusion
 References
 Paper Published

3
Introduction
 Beam forming or spatial filtering is the fundamental
technology in smart antenna systems. It is a signal
processing technique used in antenna/sensor arrays for
directional signal transmission or reception.
 Beam forming can be used at both the transmitting and
receiving ends in order to achieve spatial selectivity. It
has found numerous applications in radar, sonar,
seismology, wireless communications, radio astronomy,
acoustics and biomedicine.
Date :-

4
Introduction
Date :-
Fig 1. Uniform linear array of Ns elements

5
Introduction
 The phase difference between the successive elements of
array is given by following equation
 The array steering vector for a signal arriving from a
direction ϴk is given by
 The signal received by the array can be represented as
Date :-

6
Introduction
 The array response matrix A is represented as
 The received signal is multiplied with the coefficients of
the weight vector W, where the weights are chosen
according to the type of beam former.
 The weighted signal is summed up to give the beam
former output Y.
Y(n) = wH x(n)
Date :-

7
Introduction
 Classes of Beamformers:
Date :-
Fig . 2 Classes of beamformers

8
Introduction
Date :-
Fig. 3 Schematic diagram of non – adaptive
beamformer

9
Introduction
Date :-
Fig. 4 Schematic diagram of Adaptive
beamformer

10
Motivation
 For a radio receiver that must function in an environment
with jammers, it is desirable to suppress disturbances
originating from certain directions or disturbances in
certain frequency bands.
 This can be achieved using an antenna array where the
signal from each individual antenna is filtered and
combined so that the interference is cancelled through
destructive interference.
 The filtering operation for each channel can be a single
complex multiplication, or a more general complex FIR
(Finite Impulse Response) filter.
Date :-

11
Motivation
 The filter coefficients may be computed based on prior
knowledge of the direction and/or frequency band of the
interference and desired signal, or they may be adaptively
computed by an algorithm, based on the received signals.
 The process of finding the appropriate weights is called
beamforming, since the choice of weights controls the
antenna array's sensitivity in both direction and frequency.
Date :-

12
Motivation
Date :-
Fig. 5 A STAP filter with M channels and N taps

13
Motivation
Date :-
 The Minimum Mean Square Error (MMSE) criterion
Consider the general filter y[i] = wHx[i]. This filter can
represent a FIR filter when elements of x are samples of the
same signal at different times. It can also represent a linear
combiner if elements of x are taken from different signals.
Fig. 6 MMSE block diagram

14
Motivation
Date :-
 The CORDIC algorithm (Coordinate Rotation Digital
Computer) is an implementation that can compute a real-
valued Givens rotation.
 Like the Givens rotation, it can be used to perform a QR-
decomposition. We start from the Givens rotation by angle
θ, and rewrite it in terms of tan(θ)

15
Literature Review
Date :-
Sr No Title Details
1 Rahmani, M and M.H. Bastani
(2014) Robust and rapid
converging adaptive
beamforming via a subspace
method for the signal-plus-
interference covariancematrix
estimation, IET Signal
Processing, 8, 507-520.
Projection of the estimated covariance
matrix into the subspace of the ideal
signal plus interference covariance
matrix, results is an effective reduction in
covariance matrix estimation error.
Better SINR and faster convergence are
also obtained but with higher
computational complexity
2 Jiang, M., W. Liu and Y. Li
(2016) Adaptive Beamforming
for Vector-Sensor Arrays
Based on a Reweighted Zero-
Attracting Quaternion-Valued
LMS Algorithm, IEEE
Transactions on Circuits and
Systems II: Express
Briefs,63,274-278.
For reducing system complexity and
energy consumption, a reference
signal based adaptive
beamformer for vector sensor arrays
consisting of crossed dipoles is used
with focus on
reducing the number of sensors
involved in the adaptation. Effective
beamforming is obtained
with reweighted zero-attracting

16
Literature Review
Date :-
Sr No Title Details
3 F., F. Chen and J. Song (2015)
Robust Adaptive Beamforming
Based on
Steering Vector Estimation and
Covariance Matrix
Reconstruction, IEEE
Communications Letters, 19,
1636-1639.
The array steering vector is treated
as a vector lying within the
intersection of two subspaces
and estimated using a closed-form
formula. The average of the noise
Eigen values replaces the desired
signal Eigen value in the covariance
matrix and only requires knowledge
of the antenna array geometry and
angular sector. Robust performance
is achieved even in the Performance
Analysis of Digital Beamforming
Algorithm for Wireless
Communication System presence of
large look direction error in the array
as long as the input signal-to-noise
ratio
(SNR) is not close to the
interference-to-noise ratio (INR) and

17
Literature Review
Date :-
Sr No Title Details
4 Markovich-Golan, S., S.
Gannot and I. Cohen (2012)
Low-Complexity Addition or
Removal of
Sensors/Constraints in LCMV
Beamformers, IEEE
Transactions
on Signal Processing, 60,
1205-1214.
Linearly constrained minimum
variance (LCMV) beamformer can be
applied to sensor networks.
Suboptimal LCMV beamformers
utilizing only a subset of the available
sensors for scenarios with multiple
desired and interfering sources in
multipath environments result in
signal enhancement. Procedures are
derived for adding or removing either
an active sensor or a constraint from
an existing LCMV beamformer in
closed-form, as well as generalized
sidelobe canceller (GSC)-form
implementations. Computational
burden is reduced by using the
previous coefficients of the
beamformer in the updation processd

18
Literature Review
Date :-
Sr No Title Details
5 Razia, S., T. Hossain and
M.A.Matin (2012) Performance
analysis of adaptive
beamforming algorithm for
smart antenna system,
Proceedings of International
Conference on Informatics,
Electronics & Vision (ICIEV) ,
Dhaka, 946-949.
Active Tap Detection-Normalized
Least Mean Square (ATD-NLMS)
algorithm for robust smart antenna
system used in place of conventional
Least Mean Square (LMS) and
Normalized Least Mean Square
(NLMS) algorithms give a high
convergence rate as it estimates only
the active taps for the beamformer.
Narrow beam width with high gain and
reduced side lobes lead to a better
system efficiency of smart antenna
system

19
Literature Review
Date :-
Sr No Title Details
6 Waheed, O. T., A. Shabra and I.
M. Elfadel (2015) FPGA
methodology for power analysis
of embedded adaptive
beamforming, Proceedings of
International Conference on
Communications, Signal
Processing and their
Applications (ICCSPA-2015)
Sharjah, 1-6..
.
FPGA-based methodology is used for the
analysis, modelling and prediction of
power dissipation in embedded array
signal processing systems containing
adaptive beamforming components. The
adaptive beamforming design space is
explored in terms of power, timing,
overhead, arithmetic precision and
computational resources. Design-space
exploration is enabled in real-time and on
actual received waveforms. A hardware
prototype based on Xilinx's Virtex 7
FPGA is implemented for a four channel
Least-Mean-Squares (LMS) beamformer

20
Literature Review
Date :-
Sr No Title Details
7 Srar, J.A., K.S. Chung
and A. Mansour (2010)
Adaptive Array Beamforming
Using a Combined LMS-LMS
Algorithm, IEEE Transactions
on Antennas and Propagation,
58, 3545-3557..
.
The least mean square - least mean square
(LLMS) algorithm, which employs an
array image factor , sandwiched in
between two least mean suare (LMS)
algorithm sections is applied for array
beamforming. The convergence of LLMS
algorithm is analyzed for two different
operation modes; namely with external
reference or self- referencing. Its overall
error signal is derived by feeding back the
error signal from the second LMS
algorithm stage to combine with that of
the first LMS algorithm section. Superior
convergence performance, reduced
sensitivity to variations in input SNR,
higher stability in AWGN environments,
and robust performance in the presence of
Rayleigh fading are obtained. The fidelity
of the beamformer output signal is

21
Literature Review
Date :-
Sr No Title Details
8 Di Martino,G., and A. Iodice
(2017), Passive beamforming
with coprime arrays, in
IETRadar, Sonar & Navigation,
11- 6, 964-971.
.
Coprime arrays are observed to be as
effective as sparse configurations
suitable for radar beamforming and
angle-of-arrival estimation
applications. An approach for passive
beamforming using coprime arrays is
discussed with a new detection
strategy being proposed, which offers
improved performance in terms of
peak sidelobe ratio and integrated
sidelobe ratio. The advantage of the
proposed detector is demonstrated
through the simulation of appropriate
array scanned responses and
receiver operating characteristic
curves (Di

22
Literature Review
Date :-
Sr No Title Details
9 Yu, L., W. Liu and R. Langley
(2010) SINR Analysis of the
Subtraction- Based SMI
Beamformer, IEEE
Transactions on Signal
Processing, 58, 5926-5932..
The subtraction-based SMI (S-SMI)
beamformer imparts robustness by
blocking the desired signal from the
received data before calculating the
beamformer weight vector. The effect of
both finite sample size and arrival angle
mismatch are considered for forming
closed-form approximations of the
expected value of the signal-to-
interference-plus-noise ratio (SINR). True
values of the SINR are obtained when the
sample size is small and the arrival
direction mismatch exists

23
Literature Review
Date :-
Sr No Title Details
9 Yu, L., W. Liu and R. Langley
(2010) SINR Analysis of the
Subtraction- Based SMI
Beamformer, IEEE
Processing, 58, 5926-5932..
The subtraction-based SMI (S-SMI)
beamformer imparts robustness by
blocking the desired signal from the
received data before calculating the
beamformer weight vector. The effect of
both finite sample size and arrival angle
mismatch are considered for forming
closed-form approximations of the
expected value of the signal-to-
interference-plus-noise ratio (SINR). True
values of the SINR are obtained when the
sample size is small and the arrival
direction mismatch exists

24
Literature Review
Date :-
Sr No Title Details
10 Khedekar,S., and M.
Mukhopadhyay (2016), Digital
beamforming to reduce
antenna side lobes and
minimize DOA error,
Proceedings of 6 International
Conference on Signal
Processing, Communication,
Power and Embedded System
(SCOPES), Paralakhemundi,
Odisha, India, 1578-1583
..
Appropriate signal model derivation
for transmitter and receiver to achieve
side lobe reduction in phased array
antenna and for determining direction
of arrival (DOA) is discussed. Dolph-
Chebyshev array structure is used for
placing nulls in the antenna radiation
pattern. This structure ensures nulls
have been placed at proper positions
to jam the interferers. For this
purpose, interferers can be tracked by
using direction of arrival estimation.
This work investigates DOA for smart
antenna array and thereby computes
the error in DOA. This error is then
removed by adding appropriate phase
offset into input phase value to the
array based on look-up table (LUT)

25
Literature Review
Date :-
Sr No Title Details
11 Serra J. and M. Najar (2014)
Asymptotically Optimal Linear
Shrinkage of Sample LMMSE
and MVDR Filters, IEEE
Processing, 62, 3552-3564.
Corrections of the sample methods in
Sample Matrix Inversion resulting in
reduced computational cost are defined
that counteract their performance
degradation in the small sample size
regime and keep their optimality in large
sample size situations. The twofold
approach considers shrinkage estimators
which shrink the sample LMMSE or
sample MVDR filters towards a variously
called matched filter or conventional
beamformer in array processing for small
filters and random matrix theory for
obtaining the optimal shrinkage factors
for large filters

26
Literature Review
Date :-
Sr No Title Details
12 Khanna,R., R. Mehra and
Chandni (2017), FPGA based
implementation of pulsed radar
with time delay in digital
beamforming using partially
serial architecture, Proceedings
of 3rd International Conference
on Computational Intelligence
& Communication Technology
(CICT), Ghaziabad, India, 1-6
Digital and analog beamformers find
application in radar. The implementation
of fractional delay filter (FD) using
partially serial architecture with FPGA is
discussed. Simulation is performed with
ISE using devices, SPARTAN-3ADSP
and VIRTEX 5. The SPARTAN-3ADSP
based XC3SD1800ACS484-4 device is
compared with VIRTEX 5 based
XC5VLX50TFF1136-3. The fractional
delay filter on VIRTEX 5 is observed to
be faster than SPARTAN-3ADSP

27
Literature Review
Date :-
Sr No Title Details
13 Shubair, Raed, M and Ali
Hakam 2013, 'Versatile
beamforming utilizing variable
advance size LMS calculation
with novel ULA cluster design',
Communication Technology
(ICCT), fifteenth IEEE
International Conference on
IEEE.pp.17-19
introduced the useful plan of a keen
recieving wire framework dependent on
the DOA estimation and versatile
beamforming. The structure of a brilliant
radio wire includes an equipment part
plan which gives 15 estimations of got
signals parameters got by the sensor
exhibit. Versatile beamforming is
accomplished by utilizing LMS
calculation for coordinating the pillar
towards the ideal client signal and
creating nulls in the ways of undesired
client signa

28
Literature Review
Date :-
Sr No Title Details
14 Chen, Y., F. Wang, J. Wan and
K. Xu (2016) Robust adaptive
beamforming based on
matched spectrum
processing with little prior
information, Proceedings of
2016 IEEE 13th International
Conference on Signal
Processing (ICSP), China, 404-
408..
For reducing system complexity and
energy consumption, a reference
signal based adaptive beamformer for
vector sensor arrays consisting of
crossed dipoles is used with focus on
reducing the number of sensors
involved in the adaptation. Effective
beamforming is obtained with
reweighted zero-attracting quaternion-
valued least- mean-square algorithm
(Jiang et al., 2016). The interference-
plus-noise covariance matrix and the
desired signal covariance matrix are
reconstructed by matched spectrum
processing and the weight vector is
directly obtained using the general-
rank minimum variance distortionless
response method resulting in a robust
beamforming algorithm. This method

29
Rationale
Date :-
 We investigate the stability of QR decomposition based
algorithms for adaptive filtering in comparison with the
conventional recursive least squares (RLS) algorithm.
 To investigate this problem, the following algorithms are
applied;
 RLS
 QRD-RLS
 inverse QRD-RLS.

30
Research Objectives
 The main objectives of the work presented are:
 To develop a computationally efficient algorithm for
beamforming.
 To validate the efficiency of the algorithm on various
beamformers using MATLAB simulations.
 To implement the beamformers on FPGA using Xilinx
System Generator modelling.
Date :-

31
Research Methodology
 Recursive algorithm for adaptive filtering is applied to
least squares method. The method of least squares may
be realized via block estimation or recursive
estimation.
 The approach based on block estimation updates the
input signal on a block by block basis while recursive
estimation updates the input signal on a sample by
sample basis.
 Recursive estimation approach includes recursive least
squares and QR decomposition techniques.
Date :-

32
 Adaptive beamforming using SR algorithm involves two main
tasks, first is to identify the direction of arrival (DOA) of the
desired signal and second is calculate the optimal weight
vector.
 DOA of the desired signal would be determined using any
spectral based and parametric algorithms. SR algorithms
performance depends on antenna array structure, number of
antenna elements in the array, their spacing.
 Optimal weight vector will be estimated by the phase shift
which is owing to the delay in the received signal arrival at
the array elements from particular desired direction.
 The performance of SR algorithms depends on the accuracy of
the array factor, difference between the desired and un desired
signals in angles and on the number of array elements.
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33
Date :-
Fig. 5 Block diagram of Smart Antenna based
receiver system

34
Date :-
Fig. 6 FPGA based system
design flow

35
Results & Findings
 A standard beamformer, with quiescent beamforming
weights wn, can be called a deterministic beamformer.
 The system is considered quiescent in the sense that
the calculation of the beamforming weights need only
depend on the intended steering direction of the array,
with all other system properties static or not included
as part of the calculation process.
 For narrowband signals, the complex spatial response
vector sn is formed
Date :-
𝑆𝑛 =𝑒
𝑗2𝜋 𝑛−1
𝑑
𝜆
sin 𝜃0
0≤𝑛 ≤𝑁−1

36
Results/Findings
Date :-
Fig. 7 Normalized Radiation Pattern of ULA, θ =
0, N= 16

37
Results & Findings
Date :-
Fig. 8 Normalized Radiation Pattern of ULA with different element
spacing N=32

38
Results & Findings
 The basic function of Adaptive Beamforming is to
calculate beamforming weights which-when applied in
the same beamforming architecture provides gain to
signals incident from a desired direction, while
dynamically nulling signals from other spatial
locations.
Date :-

39
Results & Findings
Date :-
Fig. 9 Output Spectrum from the MVDR Beamformer
for a ULA

40
Results & Findings
Date :-
Fig. 10 Radiation Plot of the MVDR
Beamforming Weights

41
Results & Findings
Date :-
Fig. 11 MVDR Output Spectrum with Multiple Interference
Sources, N = 8

42
Results & Findings
 Observation of MSE & SINR by varying different
parameters
 Table 1. for N= 8, 16 & 32, λ = 0.5 & ϴ = 0
Date :-
RLS QRDRLS RLS QRDRLS RLS QRDRLS
MSE 67.03 53.63 94.7 75.76 233 187
SINR 14.67 17.26 21.38 22.51 23.83 28.04
Paramter
N=8 N=16 N=32
l = 0.5 ,q = 0 l = 0.5 ,q = 0 l = 0.5 ,q = 0

43
Results & Findings
Date :-
Fig. 12 Graphical representation of MSE & SINR for l =0.5 ,q=0

44
Results & Findings
Date :-
Fig. 13 Output Spectrum for N=8, l = 0.5 & q = 0

45
Results & Findings
Date :-
Fig. 14 Output Spectrum for N=16, l =0.5 & q = 0

46
Results & Findings
Date :-
Fig. 15 Output Spectrum for N=32, l =0.5 & q = 0

47
Results & Findings
Date :-
Table 2. Observation of MSE & SINR for N= 8, 16 & 32, λ = 0.5 & ϴ = 5
Parameter
N=8 N=16 N=32
l =0.5 ,q =5 l =0.5 ,q =5 l =0.5 ,q =5
MSE 59.22 47.38 95.09 76.07 156.89 125.51
SNIR 14.86 17.48 19.47 22.91 24.24 28.52

48
Results & Findings
Date :-
Fig. 16 Graphical representation of MSE & SINR for l
=0.5 ,q=5

49
Results & Findings
Date :-
Fig. 17 Output Spectrum for N=8, l =0.5, q =5

50
Results & Findings
Date :-
Fig. 18 Output Spectrum for N=16, l =0.5 ,q =5

51
Results & Findings
Date :-
Fig. 19 Output Spectrum for N=32, l =0.5, q =5

52
Results & Findings
Date :-
Table 3. Observation of MSE & SINR for N= 8, 16 & 32, λ = 1 & ϴ = 0
MSE 65.74 52.58 121.8 97.43 235.97 188.54
SNIR 14.86 17.49 19.47 22.9 24.31 28.61
Parameter
N=8 N=16 N=32
l =1 ,q =0 l =1 ,q =0 l =1 ,q =0

53
Results & Findings
Date :-
Fig. 20. Graphical representation of SINR & MSE for l
=1 ,q =0

54
Results & Findings
Date :-
Fig. 21. Output Spectrum for N=8, l =1 ,q =0

55
Results & Findings
Date :-

56
Results/Findings
Date :-

57
Results/Findings
Date :-
Fig. 24. Orignal & Reconstructed Signals for RLS and
QRD RLS (N=8)

58
Results & Findings
Date :-
Fig. 24 Block schematic of QRD RLS
algorithm

59
Results & Findings
Date :-
Fig. 25 Spectrum Analysis output

60
Results & Findings
Date :-
Fig. 26 Block schematic of QRD RLS
algorithm

61
Results & Findings
Date :-
Fig. 27 Block schematic of Input Signal

62
Results & Findings
Date :-
Fig. 28 Block schematic Noise Signal
Block

63
Results & Findings
Date :-
Fig. 29 Block schematic of Inference
Signal

64
Results & Findings
Date :-
Fig. 30 IQRD SYSTOLIC ARRAY

65
Results & Findings
Date :-
Fig. 31 Weighted Sum Average output

66
Results & Findings
Date :-
Fig. 32 Block schematic of Digital
Beamforming

67
Conclusion
Date :-
 In this research work, we have covered the
background knowledge of adaptive beam forming
algorithms like RLS and QRDRLS array processing
in the context of SINR. Here comparison between
RLS and QRDRLS is shown on the basis of SINR
and MSE. Detail analysis of how SINR and MSE are
affected, because of change in parameters like
wavelength, angle of arrival and number of users has
been indicated.

68 Date :-

69 Date :-
 From the above tables, we observe and conclude
that QRDRLS algorithm performs better as compared
to RLS algorithm in terms of MSE and SINR with
increase in number of users(N). For analysis, we
have changed l and q values and it is concluded
that, the best results are obtained with l = 0.5, q = 0.

70
Conclusion
 In this research work, the background knowledge of
RF array processing in the context of current and
next generation, MIMO systems is utilized.
 Here, Adaptive Beamforming processes for
embedded FPGA devices are explored. After creating
a baseline implementation for performance and
resource comparisons, a novel Deep Learning model
is used to solve Adaptive Beamforming weights in a
more efficient process then the current closed-form,
statistical solution.
 The complexity of an adaptive algorithm for real time
operation is determined by two principal factors
namely, the number of operations per iteration and
precision required to perform arithmetic operations.
Date :-

71
Conclusion
 These parameters are the deciding factors when
designing any hardware-efficient implementation.
 Pipelined adaptive filter realizations (LMS & RLS) are
implemented using the relaxed look-ahead
technique.
 The RLS filters exhibit faster convergence rates with
higher order of pipelining at the cost of higher
residual error and increase in computational
complexity. This is a performance trade-off issue.
 The relax look-ahead technique results in substantial
hardware savings as compared to either parallel
processing or look ahead techniques.
 Pipelining realizations with latches introduced in the
error feedback loop and the weight update block
were investigated and the convergence
characteristics were observed.
Date :-

72
Conclusion
 With this bound reached, pipelining can no longer
increase the speed and we need to combine the
parallel processing with pipelining to further increase
the speed of the system.
 Hence we need to adopt a parallel processing
approach, however with an increase in hardware.
This is a performance/hardware tradeoff.
 Owing to the hardware overhead with the look ahead
techniques, we proposed a systolic array
implementation based on the QRD-RLS algorithm.
 Pipelining with multiprocessing at each stage of a
pipeline yields the best performance.
Date :-

73
Conclusion
 The systolic array implementation is scalable with
very high throughputs. The QR decomposition (QRD)
RLS algorithm using the triangularization process is
ideal for implementation since it has good numerical
properties and can be mapped to a coarse grain
pipelining systolic array making it very suitable for
VLSI implementation.
 The hardware utilization efficiency is vastly increased
with the array structure. The suggested model
compensates for the errors to still produce the better
SINR, which leads to a significant contribution to the
field of adaptive beamforming.
Date :-

74
Future Scope
 With technology of beamforming pioneering into
more and more applications in practical and
advancement in low cost DSP hardware will be
explored and expected that beamforming will attract
a growing research areas.
 In the future, the work can be extended by combining
with various Direction of Arrival (DOA) estimation
algorithms and have tremendous scope for future
work.
Date :-

75
Future Scope
 Finally, a neural network back propagation algorithm
is proposed to overcome tracking problem and other
limitations in using the conventional methods in light
of the results obtained previously by using (LMS,
RLS, and CMA) algorithms, The use of ANN in
beamforming has proved better performance; the
fastest convergence to the desired signal and
flexibility, Moreover, this method has ability to reject
greater number of interferer compared with other
algorithms, which leads to making the SAS
applicable to different applications.
 In future work, the multiplier and adder units can be
implemented via LUT, for further improvement in
latency and throughput
Date :-

76
References
 Zhen-Hai Xu, Ming-Zui Chen and Bin Rao (2013) The optimal
LCMV beamformer under multiple desired signals case, Proceedings
of IET International Radar
 Zaharov, V.V., A. Gonzalez, J. Acosta and M. Teixeira (2007)
Implementing a Vector RLS Smart Antenna Beamformer Using
Xilinx System Generator, Proceedings of 2nd International
Symposium on Wireless Pervasive Computing, San Juan, 654-657.
 Yu, L., W. Liu and R. Langley (2010) SINR Analysis of the
Subtraction- Based SMI Beamformer, IEEE Transactions on Signal
Processing, 58, 5926-5932..
 Yasin M. and P. Akhtar (2012) Performance analysis of Bessel
beamformer with LMS algorithm for smart antenna array,
Proceedings of International Conference on Open Source Systems
and Technologies, Lahore,1-5..
Date :-

77
References
 Widrow, B. and Streams, S. (1985) Adaptive Signal Processing,
Prentice Hall, Englewood Cliffs, NJ.
 Xin, D., L. Guisheng, L. Hongqing and T. Haihong (2006) Robust
Constrained LMS Adaptive Beamformer, Proceedings of CIE
International Conference on Radar, Shanghai, 1-4
 Waheed, O. T., A. Shabra and I. M. Elfadel (2015) FPGA
methodology for power analysis of embedded adaptive beamforming,
Proceedings of International Conference on Communications, Signal
Processing and their Applications (ICCSPA-2015) Sharjah, 1-6..
 Qin, B., Y. Cai, B. Champagne, M. Zhao and S. Yousefi (2013) Low-
complexity variable forgetting factor constant modulus RLS-based
algorithm for blind adaptive beamforming, Proceedings of Asilomar
Conference on Signals, Systems and Computers, Pacific Grove, CA,
171-175.
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78
References
 Widrow, B. and Streams, S. (1985) Adaptive Signal Processing,
Prentice Hall, Englewood Cliffs, NJ.
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85
Paper Published
 Rahil Khan and Dr. Mukesh Yadav, “Review Of Digital Beamforming
Algorithms Using XSG (Xlinx System Generator),” International
Journal for Research in Engineering Application & Management
(IJREAM)ISSN : 2454-9150 Vol-06, Issue- 01, pp 346-349 Apr 2020,
 Rahil Khan and Dr. Mukesh Yadav, “IMPLEMENTATION OF QRD
RLS ADAPTIVE FILTER USING XILINX,” Wesleyan Journal of
Research , Vol.13 No4(XI) pp. 159–166, 2020.
 Rahil Khan and Dr. Mukesh Yadav, “Performance Analysis of Digital
Beam forming Algorithm for Wireless Communication System”
Zeichen Journa Volume 7, Issue 11, 2021ISSN No: 0932-4747Page No
:133
 Rahil Khan and Dr. Mukesh Yadav, “Simulation of Systolic Arrays for
QR Decomposition,”IJFANS Journal , Vol.11,S Iss 1, 2022, pp. 1485–
1491.
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86
Paper Published
 Rahil Khan and Dr. Mukesh Yadav, “Evaluation of Performance
Analysis of Digital Beamforming Algorithm” Industrial Engineering
Journal,
Date :-

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