Maximum Spectrum Efficiency Research Articles

A tunable narrow single-mode bandpass filter using graphene nanoribbons for THz applications

This paper presents a tunable, single-mode narrowband optical filter designed for terahertz applications utilizing graphene nanoribbons. To attain optimal conditions, the filter was devised in three steps. It is composed of two input and output waveguides and a T-shaped resonator with nanoscale dimensions. The transmission spectrum analysis employs the three-dimensional finite difference time domain and coupled mode theory methods. Tunability is achieved through the adjustment of the nanoribbon size and the chemical potential of graphene. The filter demonstrates remarkable performance metrics, including a maximum transmission spectrum efficiency of 99%, a full width at half maximum (FWHM) of 0.115 THz, a quality factor (Q-factor) of 100, and a free spectral range (FSR) of 45 THz. The presented structure holds significant promise for integrated optical components and compact optical devices, showcasing its applicability in the terahertz frequency range. Furthermore, the inherent sensitivity of this structure to changes in the refractive index of the substrate positions it as a potential sensor.

Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems

Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems.

Optimizing spectrum efficiency in 6G multi-UAV networks through source correlation exploitation

In the context of 6G and 5G communication networks, particularly in disaster-stricken areas with a surging demand for connectivity and the rapid evolution of the internet of everything (IoE), the imperative for augmenting spectrum efficiency in unmanned aerial vehicles (UAVs) communication-enabled wireless networks becomes ever more pronounced. This work embarks on the mission of significantly enhancing spectrum efficiency by capitalizing on redundancies, such as correlations inherent in data sources like co-located video sensors. While the intuitive potential of exploiting redundancies for spectrum efficiency enhancement is apparent, our approach is systematically structured. We introduce a linear programming framework designed to meticulously allocate bandwidth to individual links based on their respective demands, while judiciously adhering to spatial spectrum reuse constraints. Subsequently, we employ this linear program to empirically quantify the improvements in spectrum efficiency engendered by source correlations. Furthermore, we elucidate various strategies for harnessing these correlations to maximize spectrum efficiency gains, while navigating the trade-off terrain between computational complexity and precision. Our findings resoundingly underscore the trans-formative power of identifying the precise set of source correlations, resulting in spectrum efficiency enhancements of up to two orders of magnitude. In terms of network performance, the judicious exploitation of source correlations grants admission to nearly 100% of delay-intolerant traffic, alongside substantial reductions in mean delay for delay-tolerant traffic.”

AFC-CE Hybrid Precoding for Energy-Efficient mmWave MIMO Systems

In this letter, an energy-efficient hybrid precoding method based on cross-entropy (CE) is proposed for the adaptive fully-connected (AFC) structure in millimeter wave massive multiple-input multiple-output systems, named the AFC-CE algorithm. By the nonlinear Tomlinson-Harashima precoding method, the non-convex spectrum efficiency maximization problem is transformed into a matrix residual minimization problem between the hybrid precoding matrix and the optimal precoding matrix. Then, using CE theory, the matrix residuals minimization problem is transformed into a probability distribution optimization problem of switching precoding matrix elements. Finally, the probability distribution parameters are updated adaptively by minimizing the CE between the estimated value and the expected value, until the residual converges and the switching precoding matrix is obtained. On this basis, the generalized power method and the least square method are used to give the phase shifts precoding matrix and the digital precoding matrix, respectively. Theoretical analysis and simulation results verify that the proposed method not only has lower computational complexity but also can provide better spectral efficiency and energy efficiency.

Revisiting the modulation format selection problem in crosstalk-aware SDM-EONs

The problem of finding a lightpath to provision a connection request in space-division multiplexing elastic optical networks (SDM-EONs) involves routing, modulation, spectrum and core assignment (RMSCA). Aiming at maximizing spectrum efficiency, the lightpath is always established with the modulation format that gives the highest data rate within the transmission reach and crosstalk (XT) threshold. For SDM-EONs based on multi-core fibers (MCFs), highest rate modulation format leads to lowest XT tolerance/threshold, or more spectrum fragments in adjacent cores. We call this problem XT-fragmentation. XT-fragmentation has been ignored by all existing RMSCA algorithms. In this paper, we first propose a way to measure the potential spectrum loss due to more fragments. Then a new modulation format selection (MFS) algorithm is designed with XF-fragmentation in mind. Simulation results show that RMSCA algorithms with MFS achieves lower request blocking probability than that without MFS.

Photonics millimeter wave bidirectional full-duplex communication based on polarization multiplexing.

We have proposed and experimentally implemented a photonics-aided large-capacity long-distance mm-wave bidirectional full-duplex communication system at the W-band based on polarization multiplexing. The same radio frequency (RF) carrier source is shared by both the uplink and the downlink, and a pair of orthomode transducers (OMTs) are used to separate the dual orthogonally polarized channels. To achieve the maximum spectrum efficiency and throughput, 10-Gbaud probabilistically shaped 256-level quadrature-amplitude-modulation (PS-256QAM) signals with 7.07 bit/symbol/Hz are transmitted in Ch. H and Ch. V. The system can support the bidirectional transmission with 103-Gbps data rate over 4600-m RF wireless distance. To the best of our knowledge, based on a photonics-aided bidirectional full-duplex system, this is the first time to realize a record-breaking bit rate-distance product at the W-band, i.e., 103 Gbps × 4.6 km = 473.8 Gbps•km.

Physical Layer Famous Waveform Approaches for Fifth Generation Cellular Network

Many developments are taking place in the network to meet the 5G (Fifth generation) cellular network requirements; one of the very important research areas is the physical layer architecture and techniques. Studies related to the spectrum efficiency maximization indicate that the 5G air interface technology will also need to be flexible and capable of mapping various services to the best suitable combinations of frequency and radio resources.This paper investigates the physical layer waveform approaches as part of enhanced mobile broadband use case in 5G, and finds the Cyclic-Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) as a suitable approach by comparing performance parameters where the filtering and windowing can result in degradation of the quality of the signal which is measured via Error Vector Magnitude(EVM). It also attempts to enhance the down link performance between the base station and user equipment by using power amplifier clipping technique, while modelling the downlink channel by implementing channel coding, synchronization signal burst generation, different subcarrier spacing and frame numerologies for different propagation channel and different waveform approaches.

Spectrum and Energy Efficiency Optimization in IRS-Assisted UAV Networks

Unmanned aerial vehicles (UAVs) have been widely employed in wireless communications, and the performance can be enhanced with the assistance of intelligent reflecting surface (IRS). However, the finite energy of UAVs greatly limits the endurance and becomes a bottleneck for IRS-UAV communications. In this paper, an integrated IRS-UAV communication scheme is proposed where the IRS is mounted on the UAV to connect the base station and the ground user. We present two schemes to maximize the spectrum efficiency (SE) and the energy efficiency (EE) of the system by jointly optimizing the active beamforming, passive beamforming and UAV trajectory. First, to tackle the SE maximization problem, we divide it into three subproblems to optimize the variables iteratively. For the active and passive beamforming, the closed-form solutions can be directly derived. The suboptimal trajectory design can be obtained by utilizing the successive convex approximation. Furthermore, considering the limited on- broad energy of UAV, a scheme to maximize the EE is proposed. The optimal active beamforming and the passive beamforming can be similarly obtained. For the non-convex fractional programming of trajectory optimization, it can be solved via the Dinkelbach’s method. Numerical results demonstrate that the proposed algorithms are effective for the IRS-UAV networks to maximize the SE and EE, respectively.

Wavelet-Transform-Based Sparse Code Multiple Access for Power Line Communication

This paper presents Discrete Wavelet Transformed Sparse Code Multiple Access (DWT-SCMA) in Power Line Communication (PLC) systems. In the present internet of things era, PLC provides an established infrastructure for low-cost and reliable indoor connectivity. PLC systems can benefit from the Sparse Code Multiple Access (SCMA) technique, which allows multiple users to access a frequency slot simultaneously to maximize spectrum efficiency. However, interuser interference arises in SCMA when numerous users map their data to the same frequency resource; this, in turn, is likely to be enhanced by the noisy PLC channel. This article adopts the intriguing aspects of DWT to address the interference difficulties. A mathematical model of the proposed technique is also presented and compared with Fast Fourier Transformed SCMA (FFT-SCMA). In the PLC environment, DWT-SCMA is found to outperform FFT-SCMA.

Double IRSs Aided Massive MIMO Channel Estimation and Spectrum Efficiency Maximization for High-Speed Railway Communications

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme.

Spectrum efficiency maximization for multi-hop D2D communication underlaying cellular networks: Machine learning-based methods

Multi-hop D2D communication has been proposed with the purpose of improving the coverage, quality of service (QoS), and flexibility and adaptability of single-hop D2D communication. However, multi-hop D2D communication often experiences obstacles caused by interference from the shared channel, which makes spectrum efficiency in multi-hop D2D networks an important issue to tackle. In this paper, we study the optimization of spectrum efficiency in multi-hop D2D communication underlaying cellular networks. First, we use iteration-based optimization techniques such as exhaustive search (ES) and gradient search (GS) with barrier function to find the global and local optimal solutions, respectively. More importantly, we propose two machine learning (ML) techniques, the unsupervised deep neural network (DNN) and deep Q-learning (DQL) algorithms and evaluate the performances of both algorithms compared to iteration-based optimization methods. The simulation results verify that both algorithms achieve near-global optimums compared to GS. Moreover, it is verified that the DQL outperforms the unsupervised DNN in terms of optimal spectrum efficiency, while the DQL algorithm has higher time complexity than the unsupervised DNN.

Spectrum and Energy-Efficiency Maximization in RIS-Aided IoT Networks

New device capabilities, supporting spectrum and energy efficient internet of things (IoT) deployments, are expected to form the evolution of 5G and beyond. In IoT networks, every IoT pair interferes with each other due to operating in the same spectrum resources. This paper utilizes reconfigurable intelligent surfaces (RISs) to mitigate inter-node interference in an IoT network through directional beamforming by adjusting the phase shifts of the passive elements of the RIS. We consider an RIS-assisted IoT network consisting of multiple pairs of IoT devices, that utilize both direct paths and one-hop reflected paths. Two resource allocation problems, namely spectrum-efficiency maximization (SEM) and energy-efficiency maximization (EEM) are studied. Since the formulated optimization problems are non-convex in nature, we divide them into two sub-problems and solve them alternately. To obtain the optimal phase shifts of the elements of the RIS for both SEM and EEM, we extend the conjugate gradient technique to Riemannian manifolds. On the other hand, to obtain the optimal transmit power, we solve the transmit power allocation sub-problem as a difference of concave functions for the SEM and use a pricing based technique for the EEM. Numerical results validate the effectiveness of deploying RISs to assist IoT networks, albeit with respect to RIS’s location, its number of elements, and the number of IoT node pairs. In particular, considerable spectrum and energy efficiency gains are achieved in comparison to baseline state-of-the-art networks.

Spectrum efficiency maximization for cooperative power beacon-enabled wireless powered communication networks

We consider a spectrum efficiency (SE) maximization problem for cooperative power beacon-enabled wireless powered communication networks (CPB-WPCNs), where each transmitter harvests energy from multi-antenna power beacons (PBs) and transmits data to the corresponding receiver. For data transmission, both orthogonal transmission, i.e., the time splitting (TS) mode, and non-orthogonal transmission, i.e., the interference channel (IC) mode, are considered. Aiming to improve the system SE, the energy beamformers of PBs, the transmit power, and the transmit time duration of transmitters are jointly optimized. For the TS mode, the original non-convex problem is transformed into a convex optimization problem by means of variable substitution and semidefinite relaxation (SDR). The rank-one nature of this SDR is proved, and then a Lagrange-dual based fast algorithm is proposed to obtain the optimal solution with much lower complexity. For the IC mode, to conquer the strong non-convexity of the problem, a branch-reduce-and-bound (BRB) monotonic optimization algorithm is designed as a benchmark. Furthermore, a low-complexity distributed successive convex approximation (SCA) algorithm is presented. Finally, simulation results validate the performance of the proposed algorithms, achieving optimality within only 1%∼2% computation time compared to the CVX solver in the TS mode and achieving 98% of the optimal performance in the IC mode.

A High Speed Underwater Wireless Communication Through a Novel Hybrid Opto-Acoustic Modem Using MIMO-OFDM

For efficient underwater opto/acoustic communication, this research proposes the use of MIMO in conjunction with OFDM. OFDM (Orthogonal Frequency-Division Multiplexing) and MIMO (Multiple Input Multiple Output) systems may be widely used in wireless networks to provide high data transfer rates, resistance to multipath fading, and an increase in the channel's Spatial Multiplexing and Spatial Diversity Gain. Transmission speed can be increased by altering bandwidth or spectral efficiency (or both) in wireless data transmission systems. Systems that use Multi-Input Multi-Output (MIMO) technologies have the potential to improve spectral efficiency by employing several transmitters and receivers in tandem. To maximize spectrum efficiency and minimize inter-symbol interference, Orthogonal Frequency Division Multiplexing (OFDM) divides signals into a number of narrow band channels (ISI). In other words, combining the benefits of MIMO with OFDM will boost spectral efficiency while also increasing the link's dependability and spectral gain. MIMO and OFDM approaches are integrated in this research to increase opto-acoustic modem performance. MATLAB Simulink tool was used to design and simulate the proposed hybrid opto-acoustic modem with MIMO-OFDM for optical and acoustic (EM) signal transmission and reception. The simulation results verify the viability of the proposed method, and the measured bit-error rate (BER) for acoustic (EM) signal is 0.4958 and optical signal is 0.5101. The overall bandwidth of the system is from -150 MHz to +150 MHz.

Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA.

Opportunistic beamforming (OBF) is an effective technique to improve the spectrum efficiencies (SEs) of multiple-input-multiple-output (MIMO) systems, which can obtain multiuser diversity gains with both low computation complexity and feedback information. To serve multiple users simultaneously, many multiple-access schemes have been researched in OBF. However, for most of the multiple-access schemes, the SEs are not satisfactory. To further improve the SE, this paper proposes a downlink multiuser OBF system, where both orthogonal frequency division multiplexing (OFDM) and non-orthogonal multiple-access (NOMA) methods are applied. The closed-form expressions of the equivalent channels and SE are derived in frequency selective fading channels. Then, an optimization problem is formulated to maximize the SE, although the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into two suboptimal issues, and then a joint iterative algorithm is applied. In the proposed optimization scheme, the subcarrier mapping , user pairing and allocated power are determined to maximize spectrum efficiency (SE) and reduce bit error ratio (BER). According to numerical results, the proposed method achieves approximately 5 dB gain on both SE and BER, compared to the existing beamforming methods with low feedback information. Moreover, the SE of the proposed method is approximately 2 (bps/Hz) higher than sparse code multiple-access (SCMA), when the number of waiting users and the ratio of transmit power to noise variance are respectively 10 and 20 dB. It is indicated that the proposed scheme can achieve high and low BER with the limited feedback and computation complexity, regardless of the transmit power and the number of waiting users.

A novel adaptive code block group hybrid automatic repeat request scheme for low earth orbit satellite networks

SummaryLow earth orbit (LEO) satellite networks provide important support to expand 5G‐and‐beyond networks to realize global coverage. Hybrid automatic repeat requests (HARQs) can greatly improve the reliability of transmission through dynamic satellite channels. Due to the large propagation delay in LEO satellite channels, the biggest challenges of HARQ in satellite networks are the delayed channel state information (CSI) and overflow of receiver memory. In this paper, we propose a novel adaptive code block group HARQ (ACBG‐HARQ) scheme that achieves lower retransmission rates with reliable data receiving. First, we build a new channel state model (CSM) to describe the relationship between the packet error rate (PER) and global channel degradation. Three states of receiver memory allocation for HARQ are designed with respect to the channel state and memory occupation: idle state, non‐full state, and full state. Then, we propose an optimal HARQ scheme for LEO satellite networks with the Markov decision process (MDP) based on the fuzzy Q‐learning (FQL) algorithm to improve the accuracy of ACBG‐HARQ state transfer, which contributes to adaptive selection of the HARQ code block group size. Simulation results show that the ACBG‐HARQ scheme can achieve a maximum spectrum efficiency gain of 10% and that the probability of receiver memory overflow is reduced by 1%∼6%.

Pilot Allocation and Power Optimization of Massive MIMO Cellular Networks With Underlaid D2D Communications

Pilot pollution and limited power have become two important factors limiting the throughput of massive multi-input–multioutput (MIMO) systems. To improve system performance, we consider device-to-device (D2D) communication underlay massive MIMO (denoted “massive MIMO-D2D” for short) cellular networks and then perform pilot allocation and power optimization under this network. To begin with, the closed-form spectrum efficiency (SE) expressions for different types of users are derived in the massive MIMO-D2D cellular network. Then, we analyze the deficiencies of the existing pilot allocation schemes and propose a new pilot allocation problem, i.e., the SE product is maximized for enhancing the system SE and ensuring fairness among the users simultaneously. To solve the maximum SE product problem, we develop a pilot gray wolf prey (PGWO) algorithm by designing the fitness value used to measure pilot pollution and the global objective function used to evaluate the quality of pilot allocation. The PGWO algorithm can find the optimal SE accurately through a global search, and it is suitable for the pilot allocation of different models from single cell to multicell. Besides, we formulate the maximum–minimum fairness problem for power optimization and prove that the power objective function conforms to linear programming, and a bisection algorithm is provided to optimize the power. Simulation results show that the proposed SE product problem with the proposed PGWO algorithm promotes fairness for users while further enhancing SE compared to the existing pilot allocation schemes, and joint pilot allocation and power optimization achieves great sum SE over only pilot allocation.

Satellite Communication System adopts OFDM-MIMO Technology with High Speed Connectivity

The space communication development has strong urgent requirement for technology upgrading and evolution. The satellite communication capacity can be advanced by the application of higher data transmission efficiency. It will be contented with diversified High Throughput Satellite (HTS) communication application requirements. A satellite communication system architecture design featured by higher transmission efficiency using Multi-Input & Multi-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) technology which has been wildly used in next generation of terrestrial wireless communications has been presented. It is proposed to solving problems on multiplexing data transmission, receiving signal’s anti-interference, and supporting various multi-user terminals. The throughput and application analysis was done to evaluate system performance in terms of diverse HTSs. A comprehensive evaluation and simulation analysis of space communication application efficiency are carried out to verify the feasibility of basic space application under the diverse simulation conditions. The total throughput of the communication system can reach up to 259Gbps as extremely high speed connectivity, and the maximum spectrum efficiency is close to the ultimate performance of 100bits/s/Hz.

Sub-Array Hybrid Precoding for Massive MIMO Systems: A CNN-Based Approach

In order to reduce the computation time of hybrid precoding processing while improving the spectrum efficiency (SE) of massive multiple-input multiple-output (MIMO) systems, in this letter, we investigate the sub-array hybrid precoding based on the convolutional neural network (CNN). A constraint-relaxation alternating minimization (CR Alt-Min) algorithm is proposed to create the training set of the CNN. To reduce the computation time caused by iterations in the Alt-Min algorithm, a CNN-based algorithm is proposed. Simulation results show that the CNN-based algorithm reduces the computation time in hybrid precoding processing by an order of magnitude. Moreover, the maximum SE is improved by 26.64% by the CNN-based algorithm, compared with the Alt-Min algorithm.

Energy Harvesting in Cognitive Networks Using Feasible Channel Selection Assignment

Background: The importance of this paper is to achieve maximum spectrum efficiency and proper channel allotment between Primary and Secondary User. The licensed and unlicensed users gets promoted as the channel allotment is properly carried out. To improve energy capability and spectral proficiency consider energy collecting cognitive radio systems to update both energy feasibility and spectral viability. Energy Harvesting Provides possibility of sharing energy in wireless networks which improves the performance of channel capacity. Methods: In this paper an Token Passing algorithm is proposed that switches the channels between Primary User and Secondary User. The energy efficiency decision is taken according to when primary user is idle or not. When the primary user is idle the secondary user cannot harvest any energy and when the primary channel is occupied the secondary channel harvest energy from primary user so that the harvested energy will be used by the secondary user during channel allotment. This proposed algorithm provides energy harvesting and spectrum efficiency. Results: The result shows that the most extraordinary achievable throughput R (eh) of the energy harvesting cognitive radio. The State Transition will move from busy to idle and idle to busy which is represented as S0 and S1. The other parameters are Sensing Energy es, Sampling frequency fs, Primary Signal which accepts a noise SNR γp. As Token Passing Algorithm provides tokens for primary and secondary user it takes lesser time and achieves better throughput than the FDMA and suboptimal algorithm. Conclusion: This paper achieves the maximum spectrum efficiency and energy harvesting by properly allotting spectrum for both primary and secondary user. The primary user and secondary user and spectrum management perform the channel allotment efficiently through the idle and busy state and Token Passing Algorithm does energy harvesting. An efficient scheme is developed for allocating energy in energy harvesting cognitive radio systems.