Error Analysis of Grouped Multilevel Space-Time Trellis Coding with the Combined Application of Massive MIMO and Cognitive Radio

Chopra, Shakti Raj; Gupta, Akhil

doi:10.1007/s11277-020-07878-y

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…When all of the deployment scenarios in the ITU-R M.2135 are compared, the urban macro comes out ahead. The graphs show that in the highspeed deployment scenario, i.e., urban macro, the total power per subcarrier receives less FER/BER than the other two sectors [23][24][25][26][27]. In this paper, the model for multi-UAV network deployment is both energy-efficient and costeffective.…”

Section: Resultsmentioning

confidence: 87%

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Chopra¹,

Singh²,

Alhussen³

et al. 2023

Computers, Materials &Amp; Continua

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Many extensive UAV communication networks have used UAV cooperative control. Wireless networking services can be offered using unmanned aerial vehicles (UAVs) as aerial base stations. Not only is coverage maximization, but also better connectivity, a fundamental design challenge that must be solved. The number of applications for unmanned aerial vehicles (UAVs) operating in unlicensed bands is fast expanding as the Internet of Things (IoT) develops. Those bands, however, have become overcrowded as the number of systems that use them grows. Cognitive Radio (CR) and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks, and hence as technological solutions for future generations, from this perspective. As a result, combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment. The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication. Coverage maximization, power control, and enhanced connection quality are the three steps of the proposed model. To satisfy the desired signal-to-noise ratio, the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural, suburban, and urban macro deployment scenarios of the ITU-R M.2135 standard (SNR).

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Section: Resultsmentioning

confidence: 87%

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Chopra¹,

Singh²,

Alhussen³

et al. 2023

Computers, Materials &Amp; Continua

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“…A fully connected hybrid beamformer utilizes all Angles of Departure (AoD) to accomplish the mapping via a system of phases. In massive MMO grouping and selection of antennae according to the channel behavior and calculation of SNR with best optimal solutions has been discussed [21][22][23].…”

Section: Hybrid Beamforming In Massive Mimomentioning

confidence: 99%

Power Optimization of Unmanned Aerial Vehicle-Assisted Future Wireless Communication Using Hybrid Beamforming Technique in Disaster Management

Sharma,

Chopra,

Gupta

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Several established procedures are required to be followed when responding to disasters. Although most of them have so far been successful, they pose their own set of difficulties. The primary factor is response time, which is crucial for disaster management. The use of unmanned aerial vehicles (UAVs) for wireless communication not only increases response time but also is energy efficient. However, UAVs have limited power capacities, which makes their use in wireless communication challenging. In wireless communication, hybrid beamforming is a technique for optimizing the battery consumption of unmanned aerial vehicles (UAVs). Massive MIMO technology facilitates hybrid beamforming by deploying antenna arrays, efficiently directing transmitted energy to intended receivers, and minimizing power waste. Beamforming relies heavily on accurate Channel State Information (CSI). Advanced channel estimation and tracking techniques optimize power allocation based on real-time channel conditions, thereby minimizing energy consumption and ensuring connection reliability. Hybrid beamforming combines the advantages of digital and analog beamforming to reduce power consumption while maintaining communication performance. This paper proposes a power optimization technique for UAV-based wireless communication using hybrid beamforming to maximize spectral efficiency and increase the battery life of UAVs.

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“…In cognitive radio networks with trellis codes as compo-nent codes, authors in [22] examine the potential gains by adopting the multilevel coding scheme with huge multipleinput-multiple-output. The use of channel state information to implement beam-forming and antenna grouping in spacetime coding based on multi-level QAM signaling and beamforming is for use in cognitive radio to mitigate the impact of primary users and allows secondary users to operate.…”

Section: B Sony Eltresmentioning

confidence: 99%

“…[19] Use of Cell Design Methodology (CDM)for optimization of ECC Lack of emphasis on the impact of using CDM logic style on other performance parameters such as area, complexity, and error correction capability. [22] Proposes the use of multilevel coding scheme with trellis codes as component codes and massive MIMO in Cognitive Radio Networks to improve spectral efficiency andlow decoding complexity.…”

Section: Lorawan Network and The Ccarr Technique For Qos Enhancementmentioning

confidence: 99%

A Reviewing Approach to Analyze the Advancements of Error Detection and Correction Codes in Channel Coding With Emphasis on LPWAN and IoT Systems

Ali,

Hashim,

Chaudhary

et al. 2023

IEEE Access

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Error control coding improves reliability and efficiency of wireless communication systems. This research delves into the latest advancements in error control coding schemes for wireless communication systems, with a specific focus on their application within the domain of Low Power Wide Area Networks (LPWANs) such as Narrowband Internet of Things (NB-IoT) systems, LoRa energy-efficient hardware design, random access control systems, and wireless sensor networks. In the context of LPWANs, particularly NB-IoT systems, we investigate the adaptive coding and modulation (ACM) in NB-IoT systems, which adapts coding rates and modulation techniques to changing channel circumstances. Comprehensive analysis of the literature review shows that the proposed approaches are more energy efficient and less errorprone than fixed coding and modulation schemes. Using Cell Design Methodology (CDM), Single Error Correction (SEC) codes are optimized for energy efficiency. Power and energy efficiency have improved in standard CMOS and CDM logic systems. This impacts IoT devices, memory storage, and security systems. Next, Forward Error Correction (FEC) is examined for ALOHA-based random-access control systems wherein packet loss rate and spectral efficiency metrics are mathematically calculated. FEC is beneficial, especially when time and frequency synchronization diverge. Monte Carlo simulations studied in the literature shows that FEC improves satellite communication systems. This review establishes benefits of FEC, notably in satellite communications systems. In addition, Wireless Sensor Networks (WSNs) error control techniques and network reliability and performance are examined along with Convolutional, Turbo, Low density parity check codes(LDPC), and Polar error correction coding (ECC) methods. We learn about their benefits, drawbacks, and implementation challenges by assessing their efficacy, efficiency, and applicability in wireless sensor networks (WSN) applications. This study further highlights advancements of energy-efficient error control coding when, integrated into network designs alongwith the trade-offs between efficacy and intricacy to help improve wireless communication networks. Finally, this study summarizes current error control coding approaches for wireless communication systems. We provide insights into improving reliability, energy conservation, and efficacy by examining ACM, CDM, FEC, and error correction coding (ECC) across varied domains including LPWAN and IoT technologies with earnest hope to facilitate researchers, engineers, and professionals working in the field of error control coding in wireless communication.

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Error Analysis of Grouped Multilevel Space-Time Trellis Coding with the Combined Application of Massive MIMO and Cognitive Radio

Cited by 7 publications

References 30 publications

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Power Optimization of Unmanned Aerial Vehicle-Assisted Future Wireless Communication Using Hybrid Beamforming Technique in Disaster Management

A Reviewing Approach to Analyze the Advancements of Error Detection and Correction Codes in Channel Coding With Emphasis on LPWAN and IoT Systems

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