Spectrum Efficiency Research Articles

Enhancing Large-Area Scintillator Detection with Photonic Crystal Cavities

Scintillators are materials that emit visible photons when bombarded by high-energy particles (X-ray, γ-ray, electrons, neutrinos, etc.) and are crucial for applications, including X-ray imaging and high-energy particle detection. Here, we show that one-dimensional (1D) photonic crystal (PhC) cavities, added externally to scintillator materials, can be used to tailor the intrinsic emission spectrum of scintillators via the Purcell effect. The emission spectral peaks can be shifted, narrowed, or split, improving the overlap between the scintillator emission spectrum and the quantum efficiency (QE) spectrum of the photodetector. As a result, the overall photodetector signal can be enhanced by over 200%. The use of external PhC cavities especially benefits thick and large-area scintillators, which are needed to stop particles with ultrahigh energy, as in large-area neutrino detectors. Our findings should pave the way to greater versatility and efficiency in the design of scintillators for applications, including X-ray imaging and positron emission tomography.

Deep Learning-Based Massive MIMO CSI Acquisition for 5G Evolution and 6G

Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25\% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6% -- 26% compared with DL-based receivers and 33% -- 58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.

Unlicensed band sharing based on bargaining game for heterogeneous networks

Currently, limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more. It is necessary to extend wireless communication networks to unlicensed spectrum. In this paper, we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency, where one radio access technology (RAT) (such as macro cellular network) "competes" the unlicensed bands with multiple other RATs (such as small cellular networks or Wi-Fi) virtually. Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom, we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks, where bargaining loss and time dissipation loss for virtual "price" of unlicensed bands are mainly considered. In the one-to-many bargaining process, we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner. Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.

Energy-Efficient Power Control and Beamforming for Reconfigurable Intelligent Surface-Aided Uplink IoT Networks

Recently, reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiencies by reconfiguring the wireless propagation environment. In this paper, we propose an RIS phase shift and BS beamforming optimization technique that minimizes the uplink transmit power of the RIS-aided IoT network. Key idea of the proposed scheme, referred to as Riemannian conjugate gradient-based joint optimization (RCG-JO), is to jointly optimize the RIS phase shifts and the BS beamforming vectors using the Riemannian conjugate gradient technique. By exploiting the product Riemannian manifold structure of the sets of unit-modulus phase shifts and unit-norm beamforming vectors, we convert the nonconvex uplink power minimization problem into the unconstrained problem and then find out the optimal solution over the product Riemannian manifold. From the performance analysis and numerical evaluations, we demonstrate that the proposed RCG-JO technique achieves 94% reduction of the uplink transmit power over the conventional scheme without RIS.

Rate-Splitting Multiple Access in a MISO SWIPT System Assisted by an Intelligent Reflecting Surface

Rate-splitting multiple access (RSMA) and the intelligent reflecting surface (IRS) are novel technologies to improve spectrum and energy efficiencies in multi-antenna networks. In this paper, we investigate a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted with an IRS and using RSMA. In particular, the IRS assists with data and energy transmission from the base station (BS) to power-splitting (PS) users under a non-linear energy harvesting (EH) model. Our objective is to minimize the transmission power subject to rate and EH requirements at the users. To solve the non-convex problem, we propose a bilevel programming problem in which a genetic algorithm is used to solve the outer optimization problem, and the inner optimization problem is addressed with the semidefinite relaxation (SDR) method. In addition, to study a practical scenario of imperfect channel state information (CSI), we consider a robust beamforming design based on imperfect cascade channels at the BS. Then, we propose a solution based on the S-procedure with the SDR technique. Simulation results show that our proposed RSMA-based scheme provides a significant reduction in transmission power compared to the space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) methods under perfect and imperfect CSI.

Joint Inter-Core Crosstalk- and Intra-Core Impairment-Aware Lightpath Provisioning Model in Space-Division Multiplexing Elastic Optical Networks

Recently, space-division multiplexing (SDM) has been incorporated with elastic optical networks (EONs) to enhance the fiber capability, which forms space-division multiplexing-based elastic optical networks (SDM-EONs). During transmission of optical signals through multi-core fibers, inter-core crosstalk (XT) and intra-core physical layer impairments (PLIs) arise, which deteriorate the signal quality. Existing models typically handle inter-core XT and intra-core PLIs separately and set a single XT threshold for each modulation format, which results in an unacceptable lightpath due to the degradation of signal quality or leads to inefficient spectrum utilization. This paper proposes a routing, modulation, spectrum, and core allocation (RMSCA) model for SDM-EONs to consider inter-core XT and intra-core PLIs jointly. For each modulation format, it sets different XT thresholds and transmission reaches according to inter-core XT and intra-core PLIs. An optimization problem is formulated as an integer linear programming (ILP) problem. We prove that the RMSCA decision problem is NP-complete. We introduce a heuristic algorithm when the ILP problem is not tractable. Numerical results demonstrate that, by setting several XT limits for each modulation format, the proposed model increases spectrum efficiency compared to a benchmark model based on the literature.

On Robust Millimeter Wave Line-of-Sight MIMO Communications With Few-Bit ADCs

This work focuses on providing robust line-of-sight (LoS) spatial multiplexing at flexible communications distances and directions. Considering oblique LoS uniform linear arrays, we first derive the rank-deficient and orthogonal conditions for the LoS MIMO channel matrices. With this discovery, the topology of high spatial-resolution on one side of the link is shown with a wide full-rank-channel guarantee interval over distance and direction variations. Additionally, to reduce the implementation costs and power consumption, we propose to use low amplitude-resolution quantizers at the side of high spatial-resolution. With numerical evaluations on systems having few-bit analog-to-digital converters (ADCs), the proposed system design is shown to simultaneously achieve a higher spectrum efficiency and higher energy efficiency compared to a conventional single-stream high-amplitude-resolution over a wide signal-to-noise-ratio (SNR) range. Furthermore, we investigate channel equalization under the extreme case of using 1-bit ADCs. After providing a new viewpoint on the generalized approximate message passing (GAMP) algorithm from constrained Bethe free energy minimization, our simulations on bit-error-rates show that the GAMP algorithm can significantly reduce the performance degradation due to coarse quantization and can significantly outperform the Bussgang decomposition based linear minimum-mean-square-error estimator, especially at high SNRs.

Dynamic Channel Selection and Transmission Scheduling for Cognitive Radio Networks

Cognitive radio networks (CRNs) are expected to be promising techniques for improving the spectrum efficiency of wireless network utility in the squeezed sub-6-GHz frequency bands. Nevertheless, frequency allocation and transmission scheduling for secondary users (SUs) in CRNs suffer from no prior knowledge of other SUs’ network behaviors or the distribution of the amount of data generated at each SU. As a countermeasure, this article develops a protocol for the joint channel selection and transmission scheduling such that SUs with heterogeneous data transmission demands could be served with limited spectrum resources. Then, we formulate the dynamic optimization of the protocol as mutually embedded Markov decision processes (MDPs). To address the intractable MDPs, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning-based channel selection and transmission scheduling based on reinforcement learning with basis function approximation are, respectively, proposed. It is shown that compared with various baselines, the proposed channel selection algorithm enables each SU to select the best frequency-domain channel that does not interfere with other SUs. In particular, the proposed transmission scheduling algorithm outperforms algorithms based on off-the-shelf approaches, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning and Lyapunov optimization, in terms of both energy efficiency and long-term accumulative amount of bits at each SU.

RIS-Assisted Full-Duplex Relay Systems

Reconfigurable intelligent surfaces (RISs) have recently been presented as a revolutionary technique and recognized as one of the candidates for beyond fifth-generation wireless networks. The RIS technology becomes especially attractive due to its capability to operate in a full-duplex (FD) mode, which is able to ideally double the half-duplex radio spectrum efficiency. This article analyzes the performance of an RIS-aided multihop FD relaying system, where an intermediate FD relay is employed to defeat the far-field path-loss effect inherent to RIS communication links. To this end, we analytically derive exact and asymptotic closed-form expressions for the outage probability, average spectral efficiency, and average bit error rate. The RIS-aided system demonstrates effective performance improvement for the proposed network. However, from the results, it is noticed that the increase in the number of RIS units within a certain communication link does not provide linear performance improvement due to the RIS channel's far-field path-loss model. Thus, mixing active FD relaying techniques with passive RIS platforms can be an ideal solution to improve further system performance, such as outage probability, average spectral efficiency, and average bit error rate in RIS-enabled networks.

Energy-efficient resource allocation for NOMA heterogeneous networks using feedback water cycle algorithm

In recent years, nonorthogonal multiple access (NOMA) has grasped the attention of all researchers in both industrial and academic fields because it has been regarded as an effective solution for 5G technologies to maximize spectral efficiency and connectivity of the system. Also, it has sufficient potential to maximize the performance of a network. Besides, the deployment of NOMA in heterogeneous networks (HetNets) satisfies the requirements of user’s explosive data traffic. However, the increasing demand for energy consumption of the wireless network provokes the researchers to establish an energy-efficient resource allocation scheme. The applications of NOMA provide better utilization of spectrum efficiency and minimize the cost of resource allocation. The resource allocation problem in wireless networks still remains a challenging task as the HetNets are suffered from mutual cross-tier interference. Hence, this research proposes a new effective hybrid optimization-based energy-efficient resource allocation scheme for NOMA HetNets by introducing a newly proposed method called Feedback Water Cycle Algorithm (FWCA). The method evaluates the user-pairing and sub-channel issue for reducing computational complexity. In addition to this, the network is analyzed for determining the power consumption and energy model with dynamic coefficients. Moreover, the developed FWCO obtained the maximum achievable rate of 25.992[Formula: see text]Mbps/Hz, maximum energy efficiency of 77.398%, maximum sum rate of 31.748[Formula: see text]Mbps/Hz, and maximum throughput of 8.888[Formula: see text]Mbps.

A Deep Learning-Based Framework for Low Complexity Multiuser MIMO Precoding Design

Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.

Multi-hop transmission method of 5G millimeter wave relay based on network coding

With the rapid development of the Internet technology, millimeter wave (mmWave) will be used as a supplement to 5G low frequency bands to meet the extremely high system capacity requirements of 5G in hot spots. Although 5G mmWave communication can adapt to the needs of 5G network and carry a large amount of transmitted data, transmission stability has become one of the key technical issues of 5G network mmWave communication due to problems such as strong attenuation and poor penetration of mmWave. In order to improve the efficiency of the mmWave multi-hop transmission, we propose a 5G mmWave multi-hop transmission method based on network coding, which can adapt to the current wireless network environment, improve spectrum efficiency and increase network throughput. Based on MATLAB simulation experiments, it is verified that the proposed method can greatly improve the transmission efficiency and reduce the signal loss under the premise of ensuring the accurate signal transmission.

Efficient Online Learning Based Cross-Tier Uplink Scheduling in HetNets

Heterogeneous cellular networks (HetNets), where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs), can significantly improve the spectrum efficiency by Pico- and Macro base station collaboration. Due to cross-tier interference, joint detection of uplink signals is widely adopted so that Pico-BS can either detect the uplink signals locally or forward them to Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process. We propose an efficient algorithm, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> , that can deal with the exponentially growing state and action space. Furthermore, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> is online learning-based which does not require any prior knowledge on user behavior or channel characteristics. We prove the convergence of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> and derive an upper bound on its approximation error. Extensive experiments in various scenarios show our algorithm outperforms existing methods in reducing delay and power consumption.

A Learning Based Framework for Enhancing Physical Layer Security in Cooperative D2D Network

Next-generation wireless communication networks demand high spectrum efficiency to serve the requirements of an enormous number of devices over a limited available frequency spectrum. Device-to-device (D2D) communication with spectrum reuse offers a potential solution to spectrum scarcity. On the other hand, non-orthogonal multiple access (NOMA) as a multiple-access approach has emerged as a key technology to re-use a spectrum among multiple users. A cellular users (CUs) can share their spectrum with D2D users (DUs) and in response, the D2D network can help relay the CU signal to achieve better secrecy from an eavesdropper. Power optimization is known to be a promising technique to enhance system performance in challenging communication environments. This work aimed to enhance the secrecy rate of the CUs where the D2D transmitter (DT) helps in relaying the CU’s message under the amplify and forward (AF) protocol. A power optimization problem is considered under the quality of service constraints in terms of minimum rate requirements at the receivers and maximum power budgets at the transmitters. The problem is a non-convex complex optimization. A deep learning-based solution is proposed and promising results are obtained in terms of the secrecy rate of CU and the rate of D2D users.

Exploring Partially Overlapping Channels for Low-power Wide Area Networks

Supporting a massive amount of Internet of Things applications requires a large pool of spectrum. DSM is a promising ecosystem to improve the spectrum efficiency. In the era of LoRaWAN, the physical hardware constraints, along with the bandwidth-hungry applications pose new challenges. In this article, we investigate a novel deep-reinforcement-learning-based spectrum-sharing paradigm, termed Intelligent Overlapping, that explores partially overlapping channels for concurrent spectrum access in LoRaWAN. Our key insight is to leverage the coding redundancy to expand the available spectrum without complicated data processing algorithms. In particular, we learn the extra coding redundancy from the data on the non-overlapping spectrum via a deep-Q-learning network, and we apply such redundancy to recover the data on the overlapping spectrum. In the Media Access Control layer, we predict the channel condition and strategically learn and assign the appropriate overlapping portion to the concurrent access end devices. In the Physical layer, we harness interleaving to randomize the mutual interference to ensure that all the data remains decodable. Simulation results demonstrate that Intelligent Overlapping greatly improves the spectrum efficiency with a fast convergence rate compared to the conventional DSM mechanisms.

UOWC performance based on GMSK modulation

Abstract This paper studies and investigates the performance of Gaussian minimum shift keying (GMSK) for underwater optical wireless communication (UOWC). Various scatterings may result in inter-symbol interference (ISI) and degrade system error performance in UWOC links. Bit-error rate (BER) is evaluated using different water environment models such as clear seawater, coastal, and harbor for a 20 m link range. Simulation results indicate the BER performance is degraded due to the temporal dispersion of impulse response for high data rates in turbid water. In addition, the bandwidth-symbol time product (BT) is an essential parameter in GMSK, affecting BER performance and power efficiency. Therefore, BT must be chosen with a value that balances BER and power efficiency. The results show that GMSK with BT = 0.5 is a good choice that satisfies BER = 10−5 for SNR = 10 dB with an efficient power spectrum due to constant envelope and low out-of-band radiation.

The preparation of Co/γ-Al2O3 and treatment of cartap pesticide wastewater by microwave catalytic oxidation

Cartap is a moderately toxic insecticide, widely used because of its high efficiency, rapidness, long duration, and broad insecticidal spectrum. The production of cartap has so many synthetic steps and complex intermediate products, which results in serious environmental and health risks for the cartap containing wastewater. However, there are few studies on the treatment of cartap bearing wastewater. Herein, we prepared the catalyst by using γ-Al2O3 as a support, loaded by single metal (Cu, Fe, Co) through the dipping-sedimentation method. Then we characterized the catalysts by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). Considering the treatment efficiency, experimental conditions, and cost reductions, Co/γ-Al2O3 was found to be the best catalyst. Results demonstrated that the Co/γ-Al2O3 catalyst has thermal stability, sintering resistance, and a certain mechanical strength. The optimum experimental conditions were as follows: pH — 6, microwave power — 300 W, H2O2 dosage — 0.2 mL, and catalyst dosage — 0.5 g. Based on these experimental results, we proposed plausible degradation pathways for cartap removal by microwave-responsive catalysts technology.

Shifting Towards 6G from 5G Wireless Networks – Advancements, Opportunities and Challenges

Radio waves are used in wireless communication to transmit information from one location to another. Beginning in the twenty-first century, the advancement of electronic chips, antenna, and microcontrollers caused radio wave communication to proceed more quickly, which led to the widespread use of wireless voice communication. While 6G communications gained a lot of attention from the business and academic worlds, 5G communications are still being universally adopted. Compared through 5G, 6G will have a wider frequency band, greater transmission speed, spectrum efficiency, higher connection ability, lesser delay, broader exposure, and more strong anti-interference capacity to gratify a range of network necessities. This survey presents an perceptive understanding of 6G wireless communications via introducing necessities, features, serious technologies, challenges, opportunities and applications. 6G is estimated to do better than existing wireless communication systems concerning the transmission speed, latency, global coverage etc. Even though the 5G standard have obtained various advancements, there are still a few limits within it. 6G along will provide the necessities of a entirely related planet and offers ubiquitous wireless connectivity designed for everyone. The analysis of the existing condition of wireless communication in various states, together with the projection of future research potential, are both included in this study.

Adaptive clustering with continuous phase modulation in NOMA systems

ABSTRACT Power-domain NOMA can enlarge the spectrum efficiency and capacity via serving multiple users in the same time slot. Up to now, there are still some issues to be addressed in the NOMA system with multiple users: how many users the NOMA system can accommodate with limited power, how to mitigate the interference among the users, what’s the achievable bound of capacity with limited power at the base station, and how to tackle with two or more users having similar distances to the base station. In the paper, the approximated CPM capacity in the NOMA system over Rayleigh channel is derived. Then, an adaptive user-clustering is proposed in downlink NOMA system, such that the achievable sum-rate will get high enough. Moreover, the optimal allocation of power for each user in each cluster is derived and evaluated. In the end, the performances such as outage probability, capacity, peak-to-average power ratio (PAPR), and bit error ratio (BER) are simulated as comparison.

Low-complexity soft-output signal detector based on AI-SSOR preconditioned conjugate gradient method over massive MIMO correlated channel

Massive multiple-input multiple-output (MIMO) techniques with a large number of antenna elements at base station (BS) have been proved as an alternative to provide potential opportunity to increase the spectrum and energy efficiency. However, in the system, there generally exists a spatial correlation effect due to insufficient antenna elements spacing and/or the lack of rich scattering at BS. The minimum mean square error (MMSE) method performs signal detection at the expense of large-scale matrix inversion operation. Thus, the conjugate gradient (CG) method has received a lot of attentions to realize the MMSE detection efficiently. Unfortunately, this efficiency can be compromised due to the ill-conditioned equalization matrix of MMSE method over the correlated channel environments. Moreover, the hard output signal detection exhibits a sharply degradation in performance for higher-order quadrature amplitude modulation (QAM). Therefore, the modern communication systems use the soft-output information, i.e., log-likelihood ratio (LLR) along with the forward error-correcting code (FEC) to achieve satisfactory performance. The LLR computation along with a higher-order QAM remains challenging due to the exhaustive search of symbol in the modulation constellation. In this paper, a low-complexity soft-output signal detector based on approximate inverse symmetric successive over-relaxation preconditioned conjugate gradient (AI-SSOR-CG-SOD) method is proposed to realize MMSE method detection for uplink multiuser massive MIMO correlated channel. In the proposed method, a new preconditioner, an AI-SSOR, which is based on the Neumann series approximation of the inverse of the conventional SSOR preconditioner is firstly developed to handle ill-conditioned matrix, and then incorporated with CG method to improve the convergence rate and performance. According to the characteristic of the Gray-coding that adjacent symbols in the constellation set have only one different bit, the constellation set is divided multiple times based on the bits of the inphase and the quadrature components of the symbol, which reduces the complexity of the LLR computation of the transmitted bits by avoiding the exhaustive search process. Simulation results show that the AI-SSOR preconditioner is robust against spatial correlation effect, and the proposed detector converges at 3 iterations. Simulation results also show that the proposed detector achieves a better trade-off between the complexity and the performance compared to other existing detectors.