Bat algorithm–based beamforming for mmWave massive MIMO systems

Shahjehan, Waleed; Riaz, A.; Khan, Imran; Sadiq, Ali Safaa; Khan, Suleman; Khan, Muhammad Khurram

doi:10.1002/dac.4182

Cited by 10 publications

(11 citation statements)

References 42 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering (13), we conclude that besides combiner processing, the quantization is exploited in the novel problem formulation. However, the message s and its estimate ŝ are not involved.…”

Section: Problem Formulationmentioning

confidence: 95%

“…The hybrid beamforming architecture is implemented at the transmitter side while the multi‐receivers consider analog combiners with feedback overhead. Regarding the issue of magnitude palliation in mmWave networks, authors in [13] have introduced an adjusted bat algorithm that ensures an appropriate analog precoder/combiner conjunction and improves the current methods which use predefined codebooks to obtain the analog precoder/combiner pairs. Furthermore, hybrid beamforming has been addressed for millimeter‐wave NOMA communications [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum and energy efficient kalman‐based hybrid combiner for mmWave massive MIMO systems

2021

View full text Add to dashboard Cite

The hybrid beamforming can significantly improve the spectral and energy efficiencies of the millimeter-wave (mmWave) massive multiple input multiple output (MIMO) systems without obvious performance loss. However, the fundamental limitation of existing hybrid beamforming is that they include high-resolution analog-to-digital converters (ADCs) which consume high power. To overcome this fundamental limitation, in this paper, spectrum and energy efficient mmWave massive MIMO transmission scheme is proposed that jointly optimizes the hybrid combiner and the number of ADC bits. Particularly, the hybrid combiner is presented as a combination of three parts: the analog combiner matrix, the quantization matrix, and the digital baseband matrix which is based on the Kalman filter. Unlike most existing works that rely on sumrate optimization to design the hybrid combiner, in this work, the mean square error (MSE) is chosen as the main performance metric to describe the reliability of the system. Simulations results confirm that the proposed Kalman based hybrid combiner exhibits the best performance in terms of spectral and energy efficiencies compared to minimum mean square error (MMSE) and zero forcing (ZF) receivers.

show abstract

Section: Problem Formulationmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Spectrum and energy efficient kalman‐based hybrid combiner for mmWave massive MIMO systems

2021

View full text Add to dashboard Cite

show abstract

“…Set the system parameters as K = 3, α = 12 dB, β = 1 dB. In this paper, similar to [17], the initial value of the weighting vector at the receiving end is selected as r (0) k = ε max H k H H k , and the algorithm stops when the difference between the minimum transmit power obtained by two consecutive iterations is less than the given threshold. By iteratively updating the weight vectors of the transmitting and receiving ends, and pointing the antenna patterns to each other, it can be ensured that the transmission power is reduced until the algorithm converges without degrading the system performance.…”

Section: Algorithm Complexity and Convergence Analysismentioning

confidence: 99%

“…By selecting the optimal beamforming weight vector at the transmitting and receiving end, the communication quality of the SU can be optimized while suppressing the interference to the primary user, and the normal communication between the CU and the PU can be ensured in the same frequency band [16]. One of the effective methods to solve these problems is cognitive radio (CR) technology [17,18]. CR is a new intelligent wireless communication technology.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Algorithm for CR-MIMO Wireless Networks

Elmitwally¹,

Kanwal²,

Abbas³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU' service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multipath fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-tointerference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations,

show abstract

“…The ubiquitous deployment of interconnected devices is expected to grow in the next decade. This exponential growth is broadly supported by the increasing number of mobile devices (e.g., smartphones and tablets), smart sensors serving different markets (e.g., autonomous transportation, industrial controls and wearables), wireless sensors and actuators networks [2][3][4][5][6][7][8]. Reliable exchange of data by IoT sensors and actuators is one of the biggest challenges faced when deploying an IoT system.…”

Section: Introductionmentioning

confidence: 99%

Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

With the surge of ubiquitous demand for high-complexity and quality mobile Internet-of-things (IoT) services, new cooperative relaying paradigms have emerged. Motivated by the long and unpredictable end-to-end communication in relay-aided IoT networks, there is a need to introduce novel modulation schemes for very low bit error rate (BER) communications. In this paper, a practical modulation mapping scheme has been proposed to reduce decoding errors. Specifically, a hybrid automatic repeat request (HARQ) system has been used with an intermediate relay to transfer a message from a source to a destination. The design of modulation mapping has been optimized by first formulating the objective as the quadratic assignment problem. Later, the solution to the mapping problem is provided using an iterative search method. To validate the proposed solution, extensive simulations have been performed in MATLAB. The results show that the proposed solution outperforms the conventional relay retransmission and the heuristic design approaches.

show abstract

Bat algorithm–based beamforming for mmWave massive MIMO systems

Cited by 10 publications

References 42 publications

Spectrum and energy efficient kalman‐based hybrid combiner for mmWave massive MIMO systems

Spectrum and energy efficient kalman‐based hybrid combiner for mmWave massive MIMO systems

An Optimized Algorithm for CR-MIMO Wireless Networks

Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks

Contact Info

Product

Resources

About