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Author

Ghattas Akkad

Bio: Ghattas Akkad is an academic researcher from University of Balamand. The author has contributed to research in topics: Hardware architecture & Least mean squares filter. The author has an hindex of 4, co-authored 13 publications receiving 29 citations. Previous affiliations of Ghattas Akkad include Centre national de la recherche scientifique.

Papers
More filters
Journal ArticleDOI
TL;DR: Computer simulations demonstrate the outstanding performance of the proposed RC-pLMS in providing accelerated convergence and reduced error floor while preserving a LMS identical $O(N)$ complexity, for an antenna array of $N$ elements.
Abstract: In this paper, we propose a reduced complexity parallel least mean square structure (RC-pLMS) for adaptive beamforming and its pipelined hardware implementation. RC-pLMS is formed by two least mean square (LMS) stages operating in parallel (pLMS), where the overall error signal is derived as a combination of individual stage errors. The pLMS is further simplified to remove the second independent set of weights resulting in a reduced complexity pLMS (RC-pLMS) design. In order to obtain a pipelined hardware architecture of our proposed RC-pLMS algorithm, we applied the delay and sum relaxation technique (DRC-pLMS). Convergence, stability and quantization effect analysis are performed to determine the upper bound of the step size and assess the behavior of the system. Computer simulations demonstrate the outstanding performance of the proposed RC-pLMS in providing accelerated convergence and reduced error floor while preserving a LMS identical $O(N)$ complexity, for an antenna array of $N$ elements. Synthesis and implementation results show that the proposed design achieves a significant increase in the maximum operating frequency over other variants with minimal resource usage. Additionally, the resulting beam radiation pattern show that the finite precision DRC-pLMS implementation presents similar behavior of the infinite precision theoretical results.

14 citations

Proceedings ArticleDOI
01 Sep 2019
TL;DR: An enhanced, low complexity parallel version of the cascade RLMS is presented by eliminating the need for computing the array image vector cascading stage, and a new Kalman based parallel RLMS (RKLMS) method is proposed, where the LMS stage is replaced by a Kalman implementation of the classical LMS, and compared under low Signal to Interference plus Noise ratios (SINR).
Abstract: To ease spectral congestion and enhance frequency reuse, researchers are targeting smart antenna systems using spatial multiplexing and adaptive signal processing techniques. Moreover, the accuracy and efficiency of such systems is highly dependent on the adaptive algorithms they employ. A popular, adaptive beamforming algorithm, widely used in smart antennas, is the Recursive Least Square (RLS) algorithm. While, the classical RLS implementation achieves high convergence, it still suffers from its inability to track the target of interest. Recently, a new adaptive algorithm called Recursive Least Square - Least Mean Square (RLMS) which employs a RLS stage followed by a Least Mean Square (LMS) algorithm stage and separated by an estimate of the array image vector, i.e. steering vector, has been proposed. RLMS outperforms previous RLS and LMS variants, with superior convergence and tracking capabilities, at the cost of a moderate increase in computational complexity. In this paper, an enhanced, low complexity parallel version of the cascade RLMS is presented by eliminating the need for computing the array image vector cascading stage. Hence, For an antenna of N elements our strategy can reduce the complexity of the system by 20N multiplications, 6N additions and 2N divisions. Moreover, a new Kalman based parallel RLMS (RKLMS) method is also proposed, where the LMS stage is replaced by a Kalman implementation of the classical LMS, and compared under low Signal to Interference plus Noise ratios (SINR). Simulation results show identical performance for the parallel RLMS, cascaded RLMS at 10dB and superior performance and robustness for the RKLMS on low SINR cases up to -10dB.

10 citations

Proceedings ArticleDOI
01 Nov 2018
TL;DR: This study focuses on communication systems incorporating filter-based-multicarrier modulations (FBMC), a promising candidate for the 5G technology and implemented and tested various combinations using finite precision, HLS tools and HDL while prompting parallelization, pipelining and hardware reuse architectures.
Abstract: Fast Fourier Transform (FFT) is generally implemented on reconfigurable hardware in several signal processing or digital communication applications. It can be considered the most time and resource consuming operations due to the need of complex operations. The main of this manuscript is to investigate the contribution of High Level Synthesis (HLS) techniques on the implementation of real time FFT algorithms using field programmable gate arrays (FPGAs). In particular, this study focuses on communication systems incorporating filter-based-multicarrier modulations (FBMC), a promising candidate for the 5G technology. In order to evaluate the contribution of HLS, we implemented and tested various combinations such as: 8 and 16 points radix-2 and radix-4 FFT using finite precision, HLS tools and HDL while prompting parallelization, pipelining and hardware reuse architectures.

9 citations

Book ChapterDOI
26 Sep 2018
TL;DR: This study proposes a reconfigurable hardware architecture based on Chebyshev polynomial expansion for computing the cosine and sine trigonometric functions under finite precision arithmetic and shows that this approach presents a flexible 3 decimal digits precision output for variable length FFT operations.
Abstract: Twiddle factor generation is considered a computationally intensive task in generic length, high resolution, FFT operations. In order to accelerate twiddle factor generation, we propose a reconfigurable hardware architecture based on Chebyshev polynomial expansion for computing the cosine and sine trigonometric functions under finite precision arithmetic. We show that our approach presents a flexible 3 decimal digits precision output for variable length FFT operations, since the same design space can be used for any power of 2 FFT length. In particular, this study focuses on communication systems incorporating frequency domain beamforming algorithms for single and multi-beams. The proposed architecture is competitive with classical designs i.e. Coordinate Rotation Digital Computer, CORDIC and Taylor Series by providing low latency, high precision twiddle factors for variable length FFT.

6 citations

Proceedings ArticleDOI
24 Jan 2021
TL;DR: In this paper, a multi-stage parallel least mean square (LMS) adaptive algorithm with an error feedback is proposed to accelerate the LMS convergence while maintaining a minimal steady state error and a computational complexity of order O(N), where N represents the number of antenna elements.
Abstract: Generally, the least mean square (LMS) adaptive algorithm is widely used in antenna array beamforming given its target tracking capability and its low computational requirements. However, the classical LMS implementation still suffers from a trade-off between convergence speed and residual error floor. Numerous variants to the classical LMS have been suggested as a solution for the previous problem at the cost of a considerable increase in the computational complexity and degraded performance in low signal to noise ratio (SNR). Thus, in this paper, we propose a multi-stage parallel LMS structure with an error feedback for accelerating the LMS convergence while maintaining a minimal steady state error and a computational complexity of order O(N), where N represents the number of antenna elements. In parallel LMS (pLMS), the second LMS stage (LMS 2 ) error is delayed by one sample and fed-back to combine with that of the first LMS stage (LMS 1 ) to form the total pLMS error. A transfer function approximation to the pLMS is derived in order to numerically assess the pLMS stability and to determine the approximate maximum parametric value of the step size for which the pLMS remains stable. Simulation result highlight the superior performance of the pLMS in demonstrating accelerated convergence and low steady state error compared to previous variants and for different SNR environment.

3 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Computer simulations demonstrate the outstanding performance of the proposed RC-pLMS in providing accelerated convergence and reduced error floor while preserving a LMS identical $O(N)$ complexity, for an antenna array of $N$ elements.
Abstract: In this paper, we propose a reduced complexity parallel least mean square structure (RC-pLMS) for adaptive beamforming and its pipelined hardware implementation. RC-pLMS is formed by two least mean square (LMS) stages operating in parallel (pLMS), where the overall error signal is derived as a combination of individual stage errors. The pLMS is further simplified to remove the second independent set of weights resulting in a reduced complexity pLMS (RC-pLMS) design. In order to obtain a pipelined hardware architecture of our proposed RC-pLMS algorithm, we applied the delay and sum relaxation technique (DRC-pLMS). Convergence, stability and quantization effect analysis are performed to determine the upper bound of the step size and assess the behavior of the system. Computer simulations demonstrate the outstanding performance of the proposed RC-pLMS in providing accelerated convergence and reduced error floor while preserving a LMS identical $O(N)$ complexity, for an antenna array of $N$ elements. Synthesis and implementation results show that the proposed design achieves a significant increase in the maximum operating frequency over other variants with minimal resource usage. Additionally, the resulting beam radiation pattern show that the finite precision DRC-pLMS implementation presents similar behavior of the infinite precision theoretical results.

14 citations

Proceedings ArticleDOI
01 Sep 2019
TL;DR: An enhanced, low complexity parallel version of the cascade RLMS is presented by eliminating the need for computing the array image vector cascading stage, and a new Kalman based parallel RLMS (RKLMS) method is proposed, where the LMS stage is replaced by a Kalman implementation of the classical LMS, and compared under low Signal to Interference plus Noise ratios (SINR).
Abstract: To ease spectral congestion and enhance frequency reuse, researchers are targeting smart antenna systems using spatial multiplexing and adaptive signal processing techniques. Moreover, the accuracy and efficiency of such systems is highly dependent on the adaptive algorithms they employ. A popular, adaptive beamforming algorithm, widely used in smart antennas, is the Recursive Least Square (RLS) algorithm. While, the classical RLS implementation achieves high convergence, it still suffers from its inability to track the target of interest. Recently, a new adaptive algorithm called Recursive Least Square - Least Mean Square (RLMS) which employs a RLS stage followed by a Least Mean Square (LMS) algorithm stage and separated by an estimate of the array image vector, i.e. steering vector, has been proposed. RLMS outperforms previous RLS and LMS variants, with superior convergence and tracking capabilities, at the cost of a moderate increase in computational complexity. In this paper, an enhanced, low complexity parallel version of the cascade RLMS is presented by eliminating the need for computing the array image vector cascading stage. Hence, For an antenna of N elements our strategy can reduce the complexity of the system by 20N multiplications, 6N additions and 2N divisions. Moreover, a new Kalman based parallel RLMS (RKLMS) method is also proposed, where the LMS stage is replaced by a Kalman implementation of the classical LMS, and compared under low Signal to Interference plus Noise ratios (SINR). Simulation results show identical performance for the parallel RLMS, cascaded RLMS at 10dB and superior performance and robustness for the RKLMS on low SINR cases up to -10dB.

10 citations

Journal ArticleDOI
TL;DR: A comparative study between HLS and HDL for FPGA, using a Sobel filter as a case study in the image processing field shows that the HDL implementation is slightly better than the HLS version considering resource usage and response time.
Abstract: The increasing complexity in today's systems and the limited market times demand new development tools for FPGA. Currently, in addition to traditional hardware description languages (HDLs), there are high-level synthesis (HLS) tools that increase the abstraction level in system development. Despite the greater simplicity of design and testing, HLS has some drawbacks in describing harware. This paper presents a comparative study between HLS and HDL for FPGA, using a Sobel filter as a case study in the image processing field. The results show that the HDL implementation is slightly better than the HLS version considering resource usage and response time. However, the programming effort required in the HDL solution is significantly larger than in the HLS counterpart.

6 citations