Increase In Spectral Efficiency Research Articles

Design of an Improved Model for Spectrum Sharing and Resource Management Using Deep Q-Network, Federated Averaging, and Ant Colony Optimization

Indeed, the ever-evolving wireless communication requires DSS and resource management for effectiveness. The old methods of spectrum allocation have a strategic shortfall since they are static and thus not adapted to real-time network conditions. Besides, more important challenges include issues over privacy and inefficiency of resource utilization. In the light of such problems, this paper proposes a new method by bringing together three deep-learning techniques, which are Deep Q-Networks, Federated Averaging, and Ant Colony Optimisation. Each is chosen because of its various competencies and complementary capabilities. First, DQN is chosen because it has the capability to handle high-dimensional action space. DQN applies deep learning to approximate the Q Value function, which provides the optimal decision of spectrum allocation based on real-time network states, such as spectrum usage and interference levels. With this scheme, up to a 20% gain in network throughput and up to a 15% cut in wasted spectrum compared to traditional methods are achievable. The second component FedAvg tackles the model training collaboration issue with privacy. It enables FedAvg for privacy-preserving resource management, where multiple devices update the global model collaboratively by communicating model updates without exchanging raw data samples. For this approach, the resulting method leads to a further increase in data privacy by reducing data leakage as high as 40%, with an improvement in resource management accuracy of up to 15%. ACO was employed to optimize resource allocation and improve spectrum efficiency. ACO emulates the natural foraging behavior of ants to solve combinatorial optimization problems. Consequently, it has led to up to 25% increase in spectral efficiency and up to 20% improvement in resource allocation balance sets. All these methods put together will provide a wholesome solution that not only optimizes spectrum and resource management but also promises privacy and adaptability. This represents severe improvement in overcoming the weaknesses of the existing approaches and will provide a strong foundation for next-generation wireless networks.

Deep Learning based Channel Estimation and Hybrid Beamforming for 5G Massive MIMO Wireless Communications

Hybrid beamforming (BF), which divides beamforming operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave(mmWave) frequencies. This paper also introduce offline training and prediction schemes for channel estimation and hybrid beamforming. The aim of this paper is that to increase spectral efficiency over more data streams by leveraging the deep learning based LSTM network. The LSTM network is used to train the numeric values from sequence data and predict on new sequence data. The performance is evaluated under different parameters including number of data streams (1, 2, 3 and 4) with different signal-to-noise ratio (SNR) for different carrier frequencies (28GHz, 38GHz, 60GHz and 73GHz) through computer simulation using MATLAB. The simulation results verified that the proposed method can achieve higher spectral efficiency when the number of data streams increases and the value of SNR-Test increases too.

Применение голографического кодирования для повышения надежности передачи информации в зашумленных каналах связи

Two options for using previously developed holographic coding to increase the reliability of information transmission in a communication channel with an unmanned aerial vehicle are considered – digital channel coding and spectral coding. It is shown that the holographic code has high noise immunity and provides a probability of correct decoding of more than 0.9999 with a probability of error in the channel of 0.34. Spectral holographic coding ensures reliable communication in the presence of noise and intentional interference, the power of which is 10 dB higher than the signal power, which in turn ensures the secrecy of the communication channel. An additional advantage of spectral coding is increased resistance to intersymbol interference, which makes it possible to eliminate the introduction of guard intervals and increase spectral efficiency, and reduces the requirements for stability of the sampling frequency when performing FFT in the receiver demodulator.

Overloaded Spread Spectrum OFDMA in Outdoor Environment in the More Interleaving Scenario

The advancement of mobile communication technology is driven by innovations or modifications in the latest Radio Access Techniques to meet user demands and enhance capacity. This paper focuses on improving spectral efficiency and average throughput using an OFDM-based multiple access technique. Specifically, we have chosen Overloaded Spread Spectrum OFDMA for this study. Our research findings indicate that implementing a 24% overload leads to an approximate 33% increase in spectral efficiency. This significant improvement demonstrates the potential of Overloaded Spread Spectrum OFDMA in optimizing the performance of mobile communication systems. By increasing spectral efficiency, we can support a larger number of users and higher data rates, which are crucial for the evolving demands of modern mobile networks. This study highlights the importance of adopting advanced access techniques to sustain the growth and efficiency of mobile communications

Overloaded Spread Spectrum OFDMA in Outdoor Environment in the Low Interleaving Scenario

The development of mobile communication technology starts with creating or modifying a Radio Access Technique (RAT) to meet user needs and increase capacity. This paper focuses on enhancing spectral efficiency and average throughput using an OFDM-based multiple access method, specifically overloaded spread spectrum OFDMA. The study found that with 24% overloading for BPSK modulation, there is approximately a 30% increase in spectral efficiency in outdoor scenarios. There is a significant improvement using higher modulation as well. This improvement highlights the potential of Overloaded Spread Spectrum OFDMA to optimize mobile communication systems, ensuring better resource utilization and system performance to handle the growing demand for data and connectivity effectively

A Temporal Methodology for Assessing the Performance of Concatenated Codes in OFDM Systems for 4K-UHD Video Transmission

The communication channel is a critical part of the process of information degradation. In the 4K ultra-resolution video transmission domain, the communication channel is a crucial part where information degradation occurs, inevitably leading to errors during reception. To enhance the transmission process in terms of fidelity, advanced technologies such as digital video broadcasting terrestrial (DVB-T) and its evolutionary successor, digital video broadcasting terrestrial second generation (DVB-T2), are utilized to mitigate the effects of data transmission errors. Within this scenario, this research presents an innovative methodology for the temporal analysis of 4K ultra-resolution video quality under the influence of additive white Gaussian noise (AWGN) and Rayleigh channels. This analytical endeavor is facilitated through the application of concatenated coding schemes, specifically, the Bose–Chaudhuri–Hocquenghem concatenated low-density parity check (BCH-LDPC) and Reed–Solomon concatenated convolutional (RS-CONV) coders. A more comprehensive understanding of video quality can be attained by considering its temporal variations, a crucial aspect of the ongoing evolution of technological paradigms. In this study, the Structural Similarity Index (SSIM) serves as the main metric for quality assessment during simulations. Furthermore, the simulated Peak Signal-to-Noise Ratio (PSNR) values validate these findings, exhibiting consistent alignment with the SSIM-based evaluations. Additionally, the performance of the BCH-LDPC significantly outperforms that of RS-CONV under the 64-QAM modulation scheme, yielding superior video quality levels that approximate or surpass those achieved by RS-CONV under QPSK (Quadrature Phase Shift Keying) modulation, leading to an increase in spectral efficiency. This enhancement is evidenced by SSIM gains exceeding 78% on average. The computation of average gains between distinct technologies in video quality analysis furnishes a robust and comprehensive evaluation framework, empowering stakeholders to make informed decisions within this domain.

Genetic Algorithm for Mode Selection in Device-to-Device (D2D) Communication for 5G Cellular Networks

The widespread use of smart devices and mobile applications is leading to a massive growth of wireless data traffic. With the rapidly growing of the customers’ data traffic demand, improving the system capacity and increasing the user throughput have become essential concerns for the future fifth-generation (5G) wireless communication network. In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular spectrum and all communications take place through the base stations (BS) and core network. Device-to-Device (D2D) communication refers to a technology that enables devices to communicate directly with each other, without sending data to the base station and the core network. This technology has the potential to improve system performance, enhance the user experience, increase spectral efficiency, reduce the terminal transmitting power, reduce the burden of the cellular network, and reduce end to end latency. In D2D communication user equipment’s (UEs) are enabled to select among different modes of communication which are defined based on the frequency resource sharing. Dedicated mode where D2D devices directly transmit by using dedicated resources. Reuse mode where D2D devices reuse some resources of the cellular network. Outband mode where D2D communication uses unlicensed spectrum (e.g. the free 2.4 GHz Industrial Scientific and Medical (ISM) band or the 38 GHz millimetre wave band) where cellular communication does not take place. Cellular mode where the D2D communication is relayed via gNode B (gNB) and it is treated as cellular users. In this work, the target was to reach the optimal mode selection policy and genetic algorithm method was used with the objective of maximizing the total fitness function. Optimal mode selection policy was presented and analysed amongst cellular, dedicated, reused and outband mode. In the present study of mode selection issues in D2D enabled networks, genetic algorithm was proposed for the case when the cellular user equipment (UE) moves in the network. Quality of service (QoS) parameters, mobility parameters and Analytic Hierarchy Process (AHP) method were used to define the mode selection algorithm. To evaluate the performance of the proposed genetic algorithm, a study of the convergence of the algorithm and the signal-to-interference plus noise ratio (SINR) was done.

Joint energy and spectral optimization in Heterogeneous Vehicular Network

With the latest developments in both the automotive and communications industries, especially concerning the emerging 5G networks, IoV, and the adoption of Vehicle-to-Everything (V2X) connectivity, there has been a shift towards the establishment of Heterogeneous Vehicular Networks (HetVNets) environments. The rapid growth of data traffic and the drastic expansion of heterogeneous network infrastructure have resulted in a significant increase in energy consumption within wireless communication systems. Balancing energy efficiency and spectral efficiency has become a major challenge in Heterogeneous Vehicular networks, particularly concerning energy optimization, making the design of network systems considerably more challenging. Therefore, this paper attempts to optimize the energy utilized for each packet transmission, considering its stochastic nature and the optimized control parameters of two meta-heuristic algorithms-Particle Swarm Optimization and Artificial Bee Colony Optimization. The optimization process is executed using the Particle Bee Colony Swarm algorithm. Subsequently, a comparison is made with other proposed algorithms, namely LDOD, FO, RO, and MATO, in terms of energy efficiency and spectral efficiency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating a 30.32% increase in spectral efficiency and 73.25% increase in energy efficiency.

Indexed-channel estimation under frequency and time-selective fading channels in high-mobility systems

<p><span lang="EN-US">Index modulation (IM) techniques have been employed in different communication systems to improve bandwidth efficiency by carrying additional information bits. In high-mobility communication systems and under both time-selective and frequency-selective fading channels with Doppler spread, channel variations can be tracked by employing pilot-aided channel estimation with minimum mean-squared error estimation. However, inserting pilot symbols among information symbols reduces the system's spectral efficiency in pilot-aided channel estimation schemes. We propose pilot-aided channel estimation with zero-pilot symbols and an energy detection scheme to tackle this issue. Part of the information bit-stream is conveyed by the indices of zero-pilot symbols leading to an increase in the system's spectral efficiency. We used an energy detector at the receiver to detect the transmitted zero-pilot symbols. This paper examines the impacts of diversity order on the zero-pilot symbol detection error probability and the mean-squared of error estimation. The impacts of pilot symbols number and the zero-pilot symbol number on the mean-squared error of the minimum mean-squared error (MMSE) estimator and the system error performance are also investigated in this paper.</span></p>

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall.

Matched-filter design to improve self-interference cancellation in full-duplex communication systems

A new method for capacity and spectral efficiency increases is a full-duplex (FD) communication, where sending and receiving are done simultaneously. Hence, severe interference leaked from the transmitter to the receiver, which can disrupt the system's operation completely. For interference reduction, the transceiver tries to estimate the interfering symbols to remove their effects. A typical method is to use the Hammerstein model. In this method, nonlinear power amplifier (PA) and multipath channel are modeled with a successive nonlinear system and a finite impulse response (FIR) filter. Then, the model parameters are adjusted, and interference symbols are estimated from the transmitted symbols. In the Hammerstein method, the interference symbols are estimated directly from the transmitted symbols. But practically, the transmitted symbols first pass through the pulse-shaping filter and become a signal. Then, this signal passes through the nonlinear PA and communication channel. Finally, the received signal is filtered by the matched filter (MF) at the receiver and converted to the symbols again. In this procedure, the amplifier and the communication channel affect the transmitted signal directly and distort transmitted symbols indirectly. Therefore, in the practical situation, when we consider the transmitter's pulse-shaping filter and the receiver's MF, the estimated symbols with the Hammerstein method are erroneous. To solve this problem, a new MF at the receiver is proposed and adjusted according to the interfering signal. We have shown that this method is far better than the Hammerstein method.

Spectral Efficient Modem Design With OTFS Modulation for Vehicular-IoT System

A 5G network’s use-case in the Internet of Things (IoT) is a breakthrough, offering networks the ability to handle billions of connected devices with the proper blend of speed, latency, and cost. The IoT networks implemented in high-speed scenarios like intra and intervehicular communications in autonomous driving vehicles, high-speed vehicles, and trains will experience a Doppler effect. Orthogonal frequency-division multiplexing, the popular transmission technology for existing new-radio IoT (NR-IoT), is limited in providing reliable connections in high-speed vehicular scenarios. The performance of such system degrades with higher-order antenna configuration due to the lack of channel state information in highly mobile environments. The recently proposed orthogonal time-frequency space (OTFS) modulation is a strong contender that can handle high mobility but requires efficient transceiver design to be deployed in vehicular NR-IoT (V-IoT) systems. To conserve the resources and minimize the air time of the devices, we have proposed an embedded pilot design in the Delay–Doppler domain for the V-IoT systems. In the designed frame structure, the pilot’s position is optimized as per the vehicle speed to maximize the spectral efficiency of the system. The increase in spectral efficiency is at the cost of interference in the channel search region of the received Delay–Doppler domain OTFS signal. So a new joint estimator and the low-complex detector are proposed to handle the interference. The proposed efficient transceiver design with the spectral efficient pilot patterns allows us to conserve resources and remove complex encoder–decoders like the low-density parity check in NR-IoT.

Spectrally Efficient Backscatter Systems: A Hardware-Oriented Survey

The growing interest in backscatter communications as a low-powered solution in various fields necessitates pushing the envelope of current backscatter systems in multiple frontiers, among which is spectral efficiency. The increase in spectral efficiency can drive real-time applications such as augmented reality. In most of backscatter-system applications such as radio-frequency identification, backscatter modulation is implemented using binary schemes realized with square pulses, which are not spectral efficient. To address the spectral efficiency concern, this article reviews—with a scope limited to prototyped systems—some of the existing works in the literature that pertain to increasing the spectral efficiency of backscatter systems. The prototyped systems can be grouped—based on the implementation technique used—into three groups: High-order modulation, single-sideband modulation, and pulse shaping. Based on the current trends and studied literature, the article concludes with discussions on some future directions and open-ended research problems such as optimal pulse shaping, the use of artificial intelligence, multi-carrier modulation, and agile modulation; which are all aim for overcoming the spectral-efficiency limit of contemporary backscatter systems.

An Effective Kalman Based Hybrid Beamforming for Millimeter Wave Massive MIMO System by Using 2D Overlapped Partially Connected Sub-Array Structure

In 5G mobile communication networks, millimeter-wave (mmWave) technology plays a key role. To deal with the problem of path loss that happens in the mmWave band, competent beamforming employing a large antenna array is required. Fully digital beamforming approaches currently require separate radio frequency chains (RF chains) for each antenna, which adds to the computational complexity and hardware expense. In a multi-user environment, fully digital beamforming suffers from a larger antenna array layout, whereas analog beamforming solutions are prone to numerous performance concerns. Hybrid beamforming, on the other hand, offers a promising solution for multi-user mmWave communication. This work proposes a two-dimensional overlapping partially connected (2D-OPC) sub-array structure to reduce the antenna structure's hardware complexity and cost. The suggested technique comprises several sub-arrays that are overlapped in 2D space in the form of a Uniform Planar Array (UPA). The suggested scheme's performance is assessed using Kalman-based hybrid beamforming, which exhibits a considerable increase in spectral efficiency when compared to existing hybrid beamforming techniques. The suggested technique, which uses Kalman based hybrid beamforming and 2D-OPC provides 3.14% and 4.96% improvement over the MMSE hybrid beamforming and ZF pre-coding technique respectively.

OFDM-based 2D generalized optical quadrature spatial modulation for MIMO-OWC systems

An innovative scheme, named orthogonal frequency division multiplexing (OFDM)-based two-dimensional (2D) generalized optical quadrature spatial modulation (GOQSM), is proposed for bandlimited multiple-input multiple-output optical wireless communication (MIMO-OWC) systems. In this scheme, by way of combining two consecutive time slots into one time block, 2D GOQSM mapping can be executed in spatial domain and time domain. Therefore, more spatial bits can be transmitted, resulting in the increase of system spectral efficiency. Furthermore, discrete Fourier transform (DFT) spreading is further introduced in OFDM-based 2D GOQSM systems, so as to substantially reduce the peak-to-average power ratio (PAPR) of the transmitted signals. To evaluate and compare the performance of OFDM-based 2D GOQSM systems with other traditional systems, numerical simulations are conducted under various spectral efficiencies. The obtained simulation results prove that the proposed OFDM-based 2D GOQSM scheme with DFT spreading can achieve better bit error rate performance and lower PAPR than other benchmark schemes.

Routing in Elastic Optical Networks Based on Deep Reinforcement Learning for Multi-Agent Systems

Reinforcement Learning (RL) has become a valuable strategy in artificial intelligence and it has showed some success in real-world scenarios. Nonetheless, most of the progress achieved in research is often hard to harness in real-world systems given the theoretical assumptions, which are rarely aligned with practical settings. This work focuses on the assignment of resources within elastic optical networks, as a solution to their ever-increasing traffic. This document performs an assessment of two multi-agent reinforcement learning algorithms to solve Routing, Modulation, Spectrum, and Core Assignment (RMSCA), seeking to optimize availability for resource assignment over a network topology and increase the overall capacity, while considering the variability of traffic-related demands. Simulations are carried out in a 14-node topology. The results evidence a 50% increase in spectral efficiency and a blocking probability below 10%. After the system training process, low latency, high speed, and high availability are ensured, thus improving quality for the end user.

Adaptation of Signal with NOMA and Polar Codes to the Rayleigh Channel

Polar codes are one of the most effective methods of error correction coding. Currently, these codes are used in 5G communication systems and are the main candidate for 6G. Symmetry plays an important role in coding and decoding algorithms for polar codes. Modulation and multiple access methods are the basis for a physical layer along with error correction codes. Non-Orthogonal Multiple Access (NOMA) methods are a promising technology for future telecommunication systems. They allow the increase of both spectral efficiency and the quantity of subscribers of a communication system. In this paper, we consider a communication system with polar codes, traditional orthogonal multiple access (OMA), and NOMA. The channel with multipath propagation, which can be defined by the Rayleigh channel model, is especially difficult to transmit. We propose a method for adapting signals with polar codes to a channel state based on the analysis of channel matrix and permutation of logical subchannels. The results obtained demonstrate efficiency compared to classical solutions and do not really increase the computational complexity of signal processing and decoding.

Spectral Efficiency Evaluation of an NR-Based 5G Terrestrial Broadcast System for Fixed Reception

LTE-based 5G terrestrial broadcasting, standardized in 3GPP Release 16, allows broadcasters to reach cellular devices. In this paper, toward the development of a future terrestrial broadcast system in Release 18 or later called 5G-Advanced, a specification for New Radio (NR) based 5G terrestrial broadcasting is presented for a 6 MHz-channel raster, which is used in current digital terrestrial television broadcasting. The transmission performance is evaluated in physical layer simulations. In particular, the spectral efficiency is evaluated and compared with a second-generation DTTB system at a required carrier-to-noise ratio (CNR) of 20 dB in a fixed-reception scenario with a rooftop receiving antenna. The evaluation in an additive white Gaussian noise channel indicates that the spectral efficiency of NR-based 5G terrestrial broadcasting can be improved by 0.83 (bps/Hz) by enhancing the code length of low density parity check code, the carrier modulation scheme, guard band ratio, and cyclic prefix ratio from what available so far in Release 16. Furthermore, when frequency interleaving is incorporated, the spectral efficiency is increased by 0.75 (bps/Hz) in a short echo channel with a desired-to-undesired signal power ratio of 0 dB. The introduction of time interleaving increases spectral efficiency by 0.97 (bps/Hz) in an impulsive interference channel with a carrier-to-interference signal power ratio of -20 dB. For each interleaving, the increase in spectral efficiency tends to be higher as the required CNR increases.

Characterization, Monitoring, and Mitigation of the I/Q Imbalance in Standard C-Band Transceivers in Multi-Band Systems

Next-generation optical communication networks aim to vastly increase capacity by exploiting a larger optical transmission window covering the S-C-L-band. Simultaneously, the clear market trend is to maximize capacity per wavelength to reduce operational costs. This approach requires an increase in spectral efficiency, resulting in stringent requirements on the transceivers, which may not be satisfied in a multi-band (MB) scenario by current commercial components designed for operation in C-band. Transceiver specifications for MB operation can be relaxed through additional digital signal processing (DSP), at the cost of additional complexity, and by more resource-intensive calibration procedures. In this context, we experimentally characterize the wavelength-dependent frequency-resolved in-phase/quadrature (I/Q) imbalance of a standard C-band IQ-modulator and coherent receiver operating in an S-C-L-band system utilizing receiver-side DSP. This operation allows us to understand the nature of the wavelength-dependency of I/Q imbalance in MB systems. In the considered scenario, we validate the effectiveness of a cost-effective strategy for transceiver impairments mitigation and monitoring based on standard wavelength-independent calibration and reduced-complexity DSP.

Deep Neural Network: An Alternative to Traditional Channel Estimators in Massive MIMO Systems

Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations: LS exhibits a poor estimation when inadequate channel conditions (i.e., low- signal-to-noise ratio (SNR) region) and MMSE requires prior statistical knowledge of the channel and noise (complex to implement in practice). We present a deep learning framework based on deep neural networks (DNNs) for fifth-generation (5G) MaMIMO channel estimation. After a first preliminary scheme with which we verify the good estimation capacity of our DNN-based approach, we propose two different models, which differ in the information processed by the DNN and benefit from lower computational complexity or greater flexibility for any reference signal pattern, respectively. The results show that, compared to the LS-based channel estimation, the DNN approach decreases the mean square error (MSE) and the system’s spectral efficiency (SE) increases, especially in the low- SNR region. Our approach provides results close to optimal MMSE estimation but benefits from not requiring any prior channel statistics information.