Spectral Efficiency Improvement Research Articles

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

Reducing Successive Interference Cancellation Iterations in Hybrid Beamforming Multiuser Massive Multiple Input Multiple Output Systems Through Grouping Users with Symmetry Channels

This paper presents a comprehensive exploration of advanced beamforming techniques tailored for millimeter-wave (mm-Wave) communication systems. In response to the burgeoning demand for higher data rates, coupled with the constraints of power consumption and hardware complexity, this study focuses on developing a hybrid beamforming framework optimized for downlink scenarios, specifically targeting groups of users based on the approximate symmetry of their channels. The primary innovation of this research lies in leveraging the symmetry of channels among near users to develop a group-based successive interference cancellation (SIC) algorithm. Unlike traditional approaches that address interference on a per-user basis, this algorithm utilizes channel symmetry within clusters of users to reduce computational complexity and improve the efficiency of SIC. By grouping users with symmetrical channel characteristics, the algorithm simplifies the interference management process while maintaining system performance. The proposed system demonstrates notable advantages over existing non-linear algorithms through extensive simulations and performance evaluations, particularly in terms of spectral efficiency and computational complexity. In this study, we further emphasize the importance of balancing spectral efficiency improvements with reduced computational demands, offering a nuanced trade-off that accommodates various operational requirements. The flexible optimization framework provided showcases the system’s adaptability to diverse deployment scenarios and network configurations.

Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.

Deep Learning based enhanced hybrid beamforming using RSSI signals in MIMO systems

Hybrid beamforming has gained popularity in recent years due to its ability to enhance the performance of massive multiple-input multiple-output (massive MIMO) systems and improve the efficiency of wireless communication. However, designing a hybrid precoder is a challenging task as it requires accurate channel state information (CSI) and needs to solve an optimization problem. This paper introduces an unsupervised hybrid deep learning technique, namely CNN-BiLSTM, for developing hybrid beamforming in massive MIMO systems based on received signal strength indicator (RSSI) without having prior knowledge of CSI. Additionally, incremental principal component analysis (Incremental PCA) is used to reduce the dataset's dimensionality. The results of our proposed model demonstrate a significant improvement in spectral efficiency. This technique reduces the training overhead significantly and is able to quickly serve all the users. The simulation results indicate that proposed model produces low side lobes when providing coverage to areas. As a result, the performance of our model has a noticeable advantage compared to other models. In addition, proposed method achieves 99.21% accuracy and 5.30 bps/Hz sum-rate that is close to optimal performance and outperforms the state-of-the-art method.

IMPROVING 5G NETWORK PERFORMANCE WITH MIMO TECHNOLOGY USING BEAMFORMING ALGORITHM

The advent of 5G technology aims to revolutionize wireless communication by providing significantly higher data rates, ultra- reliable low latency, and enhanced connectivity. However, optimizing 5G network performance remains a challenging task, particularly in terms of Bit Error Rate (BER) and spectral efficiency. Multiple-Input Multiple-Output (MIMO) technology, coupled with beamforming algorithms, offers a promising solution to these challenges. This study investigates the application of the Fractional Least Mean Square (FLMS) algorithm for beamforming in MIMO systems to improve BER and spectral efficiency in 5G networks. The primary problem addressed is the need for effective interference mitigation and signal enhancement in densely populated environments, which traditional methods struggle to handle. The proposed method utilizes the FLMS algorithm to dynamically adjust the beamforming weights, thereby optimizing the signal reception quality. The algorithm's fractional nature allows for finer adjustments and better adaptation to varying channel conditions compared to conventional LMS algorithms. Simulation results show the efficacy of the proposed method. Specifically, the implementation of the FLMS algorithm in a 4x4 MIMO system shows a reduction in BER by 30% and an improvement in spectral efficiency by 25% compared to traditional beamforming techniques. These numerical values highlight the potential of FLMS in enhancing 5G network performance, making it a viable approach for future wireless communication systems.

Spectral efficiency improvement in cooperative RIS aided OFDM-IM system with hybrid indexing

Increasing demand for wireless communication leads to significant challenges in achieving high spectral efficiency (SE) and energy efficiency (EE). Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) is one of the considerations, although it has the inherent disadvantage of a trade-off between SE and EE. In this work, frequency-dependent control on reconfigurable intelligent surfaces (RIS) and indexing to activate or deactivate the elements of RIS have been proposed to improve the SE of OFDM-IM systems. The Upper bound pairwise error probability has been derived and confirmed by simulation. The proposed work enhances the SE and EE compared to the conventional OFDM-IM systems. For instance, SE loss of 0.5bpcu in an OFDM-IM system with quadrature phase shift keying (QPSK) compared to an OFDM system has been compensated by the proposed work by maintaining EE.

A hybrid PSO-GWO-based phase shift design for a hybrid-RIS-aided heterogeneous network system

The effectiveness of developing reconfigurable intelligent surfaces (RIS) for heterogeneous network (HetNet) systems has resulted in significant spectral efficiency (SE) gains. The majority of current research has not addressed effectively whether a hybrid metaheuristic technique may be used to create the hybrid RIS phase changes in HetNet. In this paper, we study a heterogeneous network (HetNet) assisted by a hybrid reconfigurable intelligent surface (H-RIS). Compared with the passive RIS, a hybrid RIS that has only 8% active elements is proposed to enhance by reflecting and amplifying incident signals. Hybrid RIS phase shift and SBS transmit beamforming are optimised. At the SBS, transmit beamforming based on zero-forcing is employed, whereas for hybrid RIS phase shift optimisation, the hybrid PSO-GWO (HPSOGWO) method is employed. The exploitation power of particle swarm optimisation (PSO) and the exploration power of the grey wolf optimizer (GWO) are combined in this hybrid approach. Simulation findings show that the suggested method, which uses only modest active RIS elements, can achieve a significant spectral efficiency improvement over both RIS-aided HetNet with totally passive RIS elements but with the SDR method to optimise the RIS phase changes and RIS-aided HetNet with entirely active RIS elements but with the Lagrange multiplier (LM) scheme to design the phase shift at the active RIS.

Generalized index and mode modulated OFDM with improved spectral and energy efficiency

AbstractRecently developed trends in wireless communication encounter extremely high surge in data traffic, which makes it inevitable to employ energy efficient techniques to reduce detrimental consequence of carbon emission over society. This concern has motivated us to upgrade our previous work on index and mode modulated orthogonal frequency division multiplexing (IMM‐OFDM) for achieving dual benefits in terms of spectral and energy efficiency. In this paper, we have proposed a generalized index and mode modulated OFDM scheme with variable number of subcarrier activation. Novelty of this generalized scheme is its capability to act as a unified model for classic OFDM and three other benchmark index modulated OFDM schemes along with our previously proposed IMM‐OFDM scheme. This new scheme outperforms those five descendent schemes in terms of energy efficiency and error performance as indicated by simulation results. Spectral efficiency improvement in this scheme is achieved through optimum sets of active subcarrier number, which are determined to gain most optimized trade‐off between spectral and energy efficiency with least detector complexity. This generalized parent scheme can replace individual models of its five descendent schemes and consequently can be considered to be one of the most promising candidates for next generation mobile communication system.

Channel estimation for pilot contamination in massive MIMO-NOMA system

Channel estimation is crucial for massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. The nature of non-orthogonal cause pilot contamination is experienced only while estimating multi-cell MIMO scheme with the training and it is misplaced while narrowing concentration to multi-cell or one-cell setting, where information of the channel is assumed to be obtainable at no cost. Non-orthogonal multiple access (NOMA) serves numerous users concurrently utilizing channel gain differences. The advancement in massive MIMO-NOMA technology has offered diverse techniques recently for reducing pilot contamination in massive MIMO-NOMA based on pilot allocation. Here, a new approach called War Strategy Chimp Optimization+Deep Neuro-Fuzzy Network (WSChO+DNFN) is designed for the estimation of channels to reduce pilot contamination in a massive MIMO-NOMA system. It takes place in two phases, the transmitter and the receiver phase. The channel estimation is conducted by DNFN that is tuned by devised WSChO. Furthermore, WSChO is an amalgamation of War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). Additionally, the WSChO+DNFN attained minimal values of BER and normalized MSE of 0.000103 and 0.000074, respectively. The proposed method has achieved a performance gain of 44.39%, 19.26%, 9.17%, 5.22%, 9.92%, and 6.03% compared to the Orthogonal Frequency Division Multiplexing (OFDM), Group Successive Interference Cancellation assisted Semi-Blind Channel Estimation Scheme (GSIC_SBCE), Sector-Based Pilot Assignment Scheme (PAS), Convolutional Neural Network (CNN), User Segregation based Channel Estimation (USCE), Optimal Channel Estimation using Hybrid Machine Learning (OCE_HML), respectively.

OPTIMIZING EFFICIENCY AND PERFORMANCE IN 5G NETWORKS THROUGH A DYNAMIC RESOURCE ALLOCATION ALGORITHMIC FRAMEWORK

With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency.

Key Enabling-Technologies of 4G and 5G Network

This paper provides a comprehensive overview of Multiple Input Multiple Output (MIMO) technology and its pivotal role in enhancing 4G (LTE) networks. MIMO technology, characterized by the use of multiple antennas at both the transmitter and receiver, enables significant improvements in data rates, coverage, and spectral efficiency. The key functionalities of MIMO technology in 4G LTE networks are elucidated, including spatial multiplexing, improved signal quality through spatial diversity, increased coverage via transmit diversity, and enhanced spectral efficiency. MIMO technology enables increased throughput and supports bandwidth-intensive applications like file downloads and video streaming by broadcasting many data streams simultaneously over the same frequency range. Furthermore, the adaptive nature of MIMO systems is discussed, highlighting their ability to dynamically adjust antenna configurations and employ adaptive beam forming techniques. These features optimize performance in real-time, maximizing signal strength, reducing interference, and enhancing overall network reliability. In summary, this paper underscores the critical importance of MIMO technology in 4G LTE networks, showcasing its ability to deliver high-speed broadband services, improve coverage in challenging environments, and efficiently utilize the available spectrum. As the backbone of modern mobile communications, MIMO-enabled 4G networks provide users with seamless connectivity and support the growing demand for data-intensive applications

Performance analysis of OSSK-UWOC systems considering pointing errors and channel estimation errors.

In this paper, we present the bit error rate (BER) performance of the underwater wireless optical communication (UWOC) systems using the optical space shift keying (OSSK) on the gamma-gamma turbulent fading channel, which also considers pointing errors and channel estimation errors. Firstly, we develop the new expressions for the probability density function (PDF) based on the Gamma-Gamma distribution with error factors. Subsequently, we analyze the statistical characteristic of the difference in attenuation coefficients between two channels in the OSSK system, by which we provide analytical results for evaluating the average BER performance. The results show that the effective improvement of spectral efficiency (SE) and BER performance is achieved by rationally allocating the number of lasers and detectors in the system. The OSSK-UWOC system performs better when a narrow beam waist is used. Furthermore, the presence of channel estimation error brings the BER performance advantage to the system, and the system with a high channel estimation error (ρ = 0.7) shows a 4 dB improvement in signal-to-noise ratio (SNR) gain compared to the system with a low channel estimation error (ρ = 0.95). The findings in this paper can be used for the UWOC system design.

The Use of Deep Learning Techniques in OFDM Receivers for 5G NR: A Survey

The Fifth Generation (5G) New Radio (NR) wireless system is the most promising next-generation solution to meet the needs of the increasing demands of mobile market. Orthogonal Frequency Division Multiplexing (OFDM) is the fundamental transmission technique due to the great improvement in spectral efficiency and the flexibility in fast varying environments. The use of multiple-input-multiple-output (MIMO) OFDM and Massive MIMO (Ma-MIMO) OFDM promises the increase in quality, throughput, and capacity of the communication system. The use of Deep Learning techniques in wireless communication networks has shown significant achievements to overcome the rising challenges as compared to the conventional solutions. In this paper, first we give a brief background on OFDM, MIMO-OFDM and Ma-MIMO. Second, we provide a survey on the use of deep learning techniques in OFDM receivers. We categorize the state-of-the-art into Block based and End-to-End deep learning models. Third, we provide a survey on the use of deep learning techniques in MIMO-OFDM and Ma-MIMO-OFDM. We categorize the state-of-the-art as model driven and data driven deep learning models. Finally, we present future research directions.

HBBRGSO: an energy and spectral efficiency improvement by novel hybrid meta-heuristic development in massive MIMO communication system

HBBRGSO: an energy and spectral efficiency improvement by novel hybrid meta-heuristic development in massive MIMO communication system

Joint MU-mMIMO-OFDM with Hybrid Beamforming System Spectral Efficiency Improvement based NOMA Technique

Joint MU-mMIMO-OFDM with Hybrid Beamforming System Spectral Efficiency Improvement based NOMA Technique

Adaptive refined random orthogonal matching pursuit algorithm for FBMC/OQAM MIMO framework

The fifth generation of wireless communication is anticipated to provide improved quality of service and enhanced data rates to the end users. One such technology that stands out as a potential transmission scheme for 5G systems is Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (FBMC/OQAM) with an effective channel estimation technique for improved performance. However, due to the inherent imaginary interference, channel estimation methods relying on preamble structures in FBMC/OQAM systems exhibit sub-optimal performance, particularly within Multiple-Input Multiple-Output (MIMO) setups. For channel estimation schemes based on compressed sensing, the inherent sparsity of wireless channels can be exploited for accurate channel reconstruction and overall performance improvement.We propose a novel compressed sensing based algorithm namely, Adaptive Refined Random Orthogonal Matching Pursuit (ARROMP), for MIMO-FBMC system with Coordinated MultiPoint (CoMP) scheduling. This algorithm adaptively selects a support set by utilizing a double threshold for the minimization of mean squared error and for accurate channel reconstruction. The proposed algorithm's performance is compared with existing Orthogonal Matching Pursuit (OMP) schemes such as random OMP, refined random OMP, and least square-based estimation. The numerical simulations suggest that the proposed adaptive algorithm provides performance improvement in terms of reduced Mean Squared Error (MSE) of channel reconstruction and Bit Error Rate (BER). Moreover, the proposed ARROMP algorithm for MIMO-FBMC is rigorously tested with CoMP scheduling for a cellular network using frequency division duplex mode. The proposed system presents significant improvements in throughput and spectral efficiency for all types of cell users, including cell-edge users. The simulation results validate the improved performance of the proposed algorithm with CoMP scheduling over the existing single-cell system with no coordination.

Index modulation-based efficient technique for underwater wireless optical communications

This paper presents and experimentally demonstrates energy and spectral efficient index modulation (IM) scheme for underwater wireless optical communications (UWOC). The proposed scheme utilizes IM with direct-current optical orthogonal frequency division multiplexing which is based on discrete Hartley transform (DHT-OFDM) instead of conventional discrete Fourier transform-based OFDM (DFT-OFDM). Although DHT-OFDM has been previously studied in various applications such as optical and wireless communications, this is the first work that practically investigates it with the UWOC system. In this paper, we first demonstrate that DHT-OFDM can inherit all advantages of conventional DFT-OFDM with less computational complexity and higher robustness to the inter-carrier interference (ICI). Therefore, the DFT process is employed at receiving end instead of DHT to guarantee low-complexity frequency-domain estimation and equalization processing and ensure that shorter required pilot overheads are required to track the UWOC channel effects. Furthermore, IM-DHT-OFDM has been presented to provide better flexibility of spectral and energy efficiency (SE/EE) to UWOC and offers higher robustness to the ICI effect as the transmitted data is modulated and physically transmitted via some active subcarriers while others must be kept idle. For instance, IM-DHT-OFDM provides up to 50% spectral efficiency improvement compared to DFT-OFDM and DHT-OFDM with better performance over the UWOC system. Simulation and experimental tests, performed over 2-meter UWOC, are provided to demonstrate the outperformance of the proposed schemes against the existing benchmarks in terms of bit error rate (BER), achieved SE, peak-to-average ratio (PAPR), and computational complexity.

Mobility Support for MIMO-NOMA User Clustering in Next-Generation Wireless Networks

Non-Orthogonal Multiple Access (NOMA) is a promising technology for future-generation wireless systems, with potential to contribute to the improvement of spectral efficiency. NOMA groups users into clusters, based on channel gain-difference. However, user mobility continuously changes the channel gain, which often requires re-clustering. In this paper, we study a set of re-clustering methods: arbitrary, one-by-one and Kuhn-Munkres assignment algorithm (KMAA), that expedite link re-establishment and keep the clusters interference-free, taking into account the mobility of users. The methods are applied to automatically dissociate identified users within clusters, when the gain-difference is lower than a given threshold, followed by re-association procedure, which integrates users into different clusters, maintaining an appropriate gain-difference. Experimental results show that the KMAA method improves efficiency and capacity through minimizing the number of re-clustering events, improving resource utilization, and lowering signaling overhead. Other sets of results highlight the throughput and outage probability gains of the KMAA method across a wide range of mobility scenarios. We also provide an analysis of the KMAA algorithm when applied to MIMO-NOMA, encompassing link resiliency and maintenance of average gain-difference, among users in clusters.

Spectral Efficiency Improvement in Downlink Fog Radio Access Network With Deep-Reinforcement-Learning-Enabled Power Control

Fog radio access network (F-RAN) is a promising architecture that leverages edge computing and caching to improve devices’ latency and quality of service. However, interference, which arises when multiple devices are concurrently scheduled on the same radio resource block (RRB), limits the performance of a dense F-RAN. This paper considers a multi-cell F-RAN in which the devices of each small cell receive data from the associated fog access point (F-AP) over the same RRB(s). The F-APs transmit data to the associated devices using rate-splitting multiple access (RSMA) schemes to manage co-channel interference within the small cells efficiently. A transmit power control scheme is proposed to maximize the network’s spectral efficiency (SE) while considering the devices’ hardware impairments (HWIs). The considered transmit power control scheme is an NP-hard problem, which is highly challenging to solve using the legacy optimization approach. To address this challenge, we propose a distributed deep reinforcement learning (DRL)-based power allocation (DDPA) scheme that takes the time-varying dynamics of the network and the HWIs of devices into account. Each F-AP in the proposed framework is equipped with a DRL agent that collects signal-to-interference-plus-noise ratio and channel state information from connected devices and adapts the transmit power allocation each scheduling interval. In addition, the ensemble learning framework is exploited to further improve the proposed DDPA scheme’s performance. We use extensive simulations to demonstrate that the DDPA scheme achieves greater SE than contemporary transmit power control schemes. In particular, the proposed DDPA scheme is especially suited to scenarios with non-negligible HWIs-induced distortion.

Identification and practical validation of spectrally efficient non-orthogonal frequency shaping waveform

Signal waveform is the basic physical layer element that fundamentally determines the most important key performance indicator in communication systems, namely spectral efficiency. Traditional waveforms rely on orthogonal Nyquist shaping but result in restricted spectral efficiency. Non-orthogonal waveforms have been proposed to enhance spectral efficiency via compressing either time or frequency resources. However, beyond 25% efficiency improvements in this way, classical Mazo theory states that performance will start to degrade. Here we use a robust and low-complexity neural network modulator to create asymmetric sub-carrier shapes, for multi-carrier non-orthogonal frequency shaping (NOFS) signals. The result is a non-Nyquist waveform which achieves 150% spectral efficiency improvement and still operates well with simple receiver processing. We set up a hardware communication link with over-the-air image transmission that practically validates the spectral efficiency improvement. This spectral efficiency will save communication resources for future communications systems such as 6G services.