Improve Spectrum Efficiency Research Articles

A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks

AbstractThe world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves the speeds and qualities of the mobile connection. To improve the energy and spectral efficiency of these types of networks, the non‐orthogonal multiple access (NOMA) technique is seen as the key solution that can accommodate more users and dramatically improve spectrum efficiency. The basic idea of NOMA is to achieve multiple access in the power sector and decode the required signal via continuous interference cancelation. A resource allocation approach is proposed for the B5G/6G‐NOMA network that aims to maximise system throughput, spectrum and energy efficiency and fairness among users while minimising latency. The proposed approach is based on reinforcement learning (RL) with the use of the Q‐Learning algorithm. First, the process of resource allocation as a problem of maximising rewards is formulated. Next, the Q‐Learning algorithm is used to design a resource allocation algorithm based on RL. The results of the simulation confirm that the proposed scheme is feasible and efficient.

Coexistence of 5G/6G and Digital Terrestrial Television Networks

This paper analyzes practical solutions for implementing 5G/6G and Digital Terrestrial Television (DTT) network coexistence in the UHF band. Two potential scenarios are explored using the case study of a nationwide DTT network: spectrum splitting and spectrum interleaving. Both scenarios are thoroughly examined based on current UHF spectrum usage and the DTT network structure based on a case study of Poland. The analysis includes potential future benefits and limitations of each scenario. The results presented in this paper can guide administrations in implementing decisions made at the International Telecommunication Union—Radiocommunication Sector, World Radiocommunication Conference 2023 (ITU-R WRC23), improving spectrum efficiency and 5G/6G networks development.

Distributed resource optimisation using the Q-learning algorithm, in device-to-device communication: A reinforcement learning paradigm

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era, where high data rates and low latency are critical, D2D communication is a pivotal technology that has many advantages. In D2D communication, the allocation of resources plays a critical role in achieving higher throughput while ensuring interference management, improved spectrum and energy efficiency, and system fairness. Conventional resource allocation methodologies encounter challenges in dynamically changing and diverse communication environments. To deal with the dynamic and unpredictable character of channel characteristics, we utilize a distributed iterative resource allocation technique based on the reinforcement learning (RL) approach that empowers the system to learn and adapt to the wireless environment autonomously. In this paper, we formulate a distributed Q learning-based RL method as its real-time learning capabilities, reduced communication overhead, and exploration efficiency make it well-suited for adapting to dynamic D2D environments compared to other RL methods. By applying the Q-learning method, the D2D devices act as learning agents striving to maximize the cumulative rewards. Through interactions with the environment and continuous learning from feedback, these agents adapt to real-time resource allocation decisions over time. Comparing our proposed Q-learning method to the state-of-the-art RL techniques, simulation results show improvements in energy and spectrum efficiency, latency, an increase in Jain's fairness index, and improvements in overall system throughput of about 6 %–8 %. The scalability is found to be 1.69 interpreting that Q-learning exhibits a good scalability as the throughput does not abruptly decrease for an increasing number of devices.

Advancing Green Communications: The Role of Radio Frequency Engineering in Sustainable Infrastructure Design

A thorough examination of the role of radio frequency (RF) engineering is crucial for promoting sustainability in communications infrastructure. This review explores the complex interplay between environmental concerns in communication systems and RF engineering. It examines RF engineering approaches and strategies that support the design, implementation, and preservation of environmentally friendly infrastructure, including the integration of renewable energy sources into RF systems, and the prospects and challenges associated with employing RF technologies for fostering sustainable actions in the communications industry. The major findings revealed the importance of RF engineering as it relates to reducing carbon footprints, lowering energy consumption, and enabling environmental sustainability in communication networks. RF engineering is an essential driver of sustainability in the communications industry, considering that it supports the integration of renewable energy sources, optimization of power usage, and improvement of spectrum efficiency. Therefore, the adoption of eco-friendly practices and utilization of RF technological innovations can potentially support a more sustainable and greener digital ecosystem.

Alternating optimized RIS-assisted noma and nonlinear partial differential deep reinforced satellite communication

Satellite communication is crucial for 6 G industrial wireless networks due to its comprehensive communication services, but traditional methods face challenges such as high latency, limited bandwidth, high cost, and integration with terrestrial networks. This highlights the need for innovative communication methods to boost satellite communication efficiency, reliability, and coverage within the context of 6 G industrial applications, which is attempted to address by this research. RISs (Reconfigurable Intelligent Surfaces) are thin sheets with tiny antennas that are programmed to manipulate radio signals. The RIS has the potential to revolutionize industrial wireless communication by providing greater dynamic and goal-oriented control over radio signals. From there, the wireless environment is being transformed into a service. Besides, the NOMA (Non-Orthogonal Multiple Access) mechanism is a highly effective radio access method in advanced wireless communications that optimizes spectrum utilization and offers improved spectrum efficiency, lower latency, more reliability, and increased connectivity. The current industrial communication network faces limitations such as limited capacity, signal interference overload, complex and expensive infrastructure, and latency issues. The fusion of RIS and NOMA is seen as promising strategy to meet the stringent demands of next-generation networks for immense connectivity. It is particularly innovative for the demanding environment of smart factories by enabling efficient resource sharing, dynamic signal control, ultra-reliable, low-latency communication, cost-effective network, overcoming the signal interference and simplifying the network infrastructure. In this article, we propose a novel approach for device-to-device satellite communication in industrial networks. This approach leverages a RIS (Reconfigurable Intelligent Surface) -assisted cooperative network with NOMA (Non-Orthogonal Multiple Access) and NL-PDDRL (Nonlinear Partial Differential Deep Reinforced Learning) termed AONPDDR (Alternating Optimized and Nonlinear Partial Differential Deep Reinforced) communication. This article focuses on important aspects related to industrial communication networks such as power consumption, energy efficiency, data delivery rate, data loss rate, throughput, latency, coverage, false alarm rate, and bit error rate. The Alternating Optimization-based RIS configuration algorithm optimizes reflecting elements in RIS by maximizing channel vectors and providing a sophisticated upper bound on the highest number of reflective elements. Next, The NL-PDDRL algorithm is designed to function effectively despite environmental uncertainties and constraints. The results indicate that the proposed AONPDDR method outperforms previous benchmark methods in system-achievable aspects.

A Microwave Photonic 2 × 2 IBFD-MIMO Communication System with Narrowband Self-Interference Cancellation.

Combined in-band full duplex-multiple input multiple output (IBFD-MIMO) technology can significantly improve spectrum efficiency and data throughput, and has broad application prospects in communications, radar, the Internet of Things (IoT), and other fields. Targeting the self-interference (SI) issue in microwave photonic-based IBFD-MIMO communication systems, a microwave photonic self-interference cancellation (SIC) method applied to the narrowband 2 × 2 IBFD-MIMO communication system was proposed, simulated, and analyzed. An interleaver was used to construct a polarization multiplexing dual optical frequency comb with a frequency shifting effect, generating a dual-channel reference interference signal. The programmable spectrum processor was employed for filtering, attenuation, and phase-shifting operations, ensuring amplitude and phase matching to eliminate the two self-interference (SI) signals. The simulation results show that the single-frequency SIC depth exceeds 45.8 dB, and the narrowband SIC depth under 30 MHz bandwidth exceeds 32.7 dB. After SIC, the desired signal, employing a 4QAM modulation format, can be demodulated with an error vector magnitude (EVM) as low as 4.7%. Additionally, further channel expansion and system performance optimization are prospected.

Dual high-order QAM-modulated mm-wave signal transmission in the Q-band enabled by simple IM/DD architecture and bandpass delta-sigma modulation.

The intensity-modulation (IM)/direct-detection (DD) systems have been proven effective and low-cost due to their simple system architecture. However, the Mach-Zehnder modulator (MZM) of the IM/DD systems only reserves its driving signal intensity. Therefore, the IM/DD systems are generally unable to transmit vector signals and have a restricted spectrum efficiency and channel capacity. Similarly, the radio-over-fiber (RoF) transmission systems based on IM/DD are limited by their simple architecture and generally cannot transmit high-order quadrature amplitude modulation (QAM) signals, which hinders the improvement of their spectrum efficiency. To address the challenges, we propose a novel, to the best of our knowledge, scheme to simultaneously transmit the dual independent high-order QAM-modulated millimeter-wave (mm-wave) signals in the RoF system with a simple IM/DD architecture, enabled by precoding-based optical carrier suppression (OCS) modulation and bandpass delta-sigma modulation (BP-DSM). The dual independent signals can carry different information, which increases channel capacity and improves spectrum efficiency and system flexibility. Based on our proposed scheme, we experimentally demonstrate the dual 512-QAM mm-wave signal transmission in the Q-band (33-50 GHz) under three different scenarios: 1) dual single-carrier (SC) signal transmission, 2) dual orthogonal-frequency-division-multiplexing (OFDM) signal transmission, and 3) hybrid SC and OFDM signal transmission. We achieve high-fidelity transmission of dual 512-QAM vector signals over a 5 km single-mode fiber (SMF) and a 1-m single-input single-output (SISO) wireless link operating in the Q-band, with the bit error rates (BERs) of all three scenarios below the hard decision forward error correction (HD-FEC) threshold of 3.8 × 10-3. To the best of our knowledge, this is the first time dual high-order QAM-modulated mm-wave signal transmission has been achieved in a RoF system with a simple IM/DD architecture.

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

Optimizing Multichannel Path Scheduling in Cognitive Radio Ad Hoc Networks using Differential Evolution

One important area of study in cognitive radio ad hoc networks is multi-channel path scheduling. Cognitive radio networks have trouble communicating and using the spectrum effectively because of weather and dispersion. Optimizing multichannel path scheduling enhances network performance and reliability in a cognitive radio ad hoc net. The Optimizing Multichannel Path Scheduling (OMPS) model methodically tackles various scheduling problems. For the OMPS model, this domain is new. It effectively resolves multichannel path scheduling. The computer method used in the study is called Differential Evolution. During optimization, several factors are carefully considered, including Channel Fade Margin, Cross-Correlation and Coherence Time, Spectral Efficiency, Interference Level, Power Consumption, Retransmission Rate, Access Probability, and Propagation Delay. To increase the scheduling efficiency of the DE algorithm, many steps are meticulously planned: initialization, mutation, crossover, fitness evaluation, selection for iteration evolution, and termination. Latency, Packet Delivery Ratio (PDR), Spectrum Utilization, Interference Level, Energy Efficiency, and Established Path Success Rate are all assessed by the OMPS model. These indicators assess the effectiveness and dependability of the network. OMPS performs better in crucial simulations than the existing model. The demonstration demonstrates decreased latency for real-time applications, greater packet delivery ratios (PDRs), improved spectrum efficiency, channel interference, energy efficiency, and connection formation odds, as well as increased throughput that enhances network resource utilization. To do this, a variety of multichannel path scheduling situations are simulated.

Joint optimization of sampling point and sensing threshold for spectrum sensing

AbstractWith the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

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.”

Dynamic resource allocation in IRS-assisted UAV wideband cognitive radio networks: A DDQN-TD3 approach

Intelligent reflecting surface (IRS) technology has found extensive application in the wireless communication domain, offering significant enhancements in communication performance by manipulating the reflection of electromagnetic waves. This research article delves into the domain of UAV-assisted wideband cognitive radio networks augmented by IRS and underpinned by sensing-based spectrum sharing techniques. We introduce UAVs as alternative solutions for both primary and secondary base stations to optimize the management of spectral resources. Our primary objective centers around the joint optimization of both the trajectories of the primary and secondary UAVs, power allocation at the secondary UAVs, reflection coefficients of intelligent reflecting surfaces, sensing time, and subcarrier allocation, all aimed at maximizing the achievable rate of secondary users. Given that the problem at hand is non-convex, we employ deep reinforcement learning algorithms for resolution. To address the challenge of mixed-action spaces, we implement a novel Dueling DQN-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm. The simulation outcomes illustrate that the methodology introduced in this paper substantially amplifies the performance of CR system when contrasted with benchmark methods. This is evident in the improved spectrum efficiency, elevated data transmission rates, and the minimization of interference with the primary network.

Secure STBC-Aided NOMA in Cognitive IIoT Networks

The Industrial Internet of Things (IIoT) has been recognised as having the potential to offer substantial benefit to a wide range of industrial sectors. However, the widespread development and deployment of IIoT pose a set of challenges, including the shortage of spectrum resources and network security. Given the heterogeneity of IIoT devices, conventional cryptographic security techniques are not sufficient, since they suffer from challenges including computation, storages, latency and interoperability. In this paper, we present a physical layer security analysis for cognitive IIoT networks. In our system, IIoT devices opportunistically utilize the primary spectrum; thereby improving spectrum efficiency and allowing access to a large number of devices. Specifically, the considered network uses space-time block coding (STBC) in conjunction with non-orthogonal multiple access (NOMA) in underlay cognitive mode to realize data transmission for IIOT devices with high spectrum efficiency. The secure STBC-NOMA transmission model is established by taking into account the interference from a primary user. New approximate and asymptotic expressions of secrecy outage probability (SOP) for the secondary users (SUs) are derived to characterize the system’s secrecy performance. In addition, the SU’s closed-form secrecy ergodic rate (ER) is provided. The proposed STBC-NOMA approach, when compared to the classic single antenna (SA) based NOMA, achieves better SOP performance for all SUs, as confirmed by both analytical and simulation findings. Furthermore, we show that the proposed STBC-NOMA framework improves the SOP performance of weaker users. Additionally, the STBC-NOMA framework outperforms the SA-NOMA framework in terms of secrecy ER performance for all SUs.

Joint Beamforming Design in Reconfigurable Intelligent Surface-Assisted Rate Splitting Networks

Reconfigurable Intelligent Surface (RIS) is an emerging technology that can improve the spectrum and energy efficiency of next-generation wireless networks. However, attaining accurate channel state information (CSI) for the cascaded RIS channel is particularly challenging. Imperfect CSI is a major bottleneck to achieving the spectral efficiency benefit of RIS-assisted networks. Rate splitting (RS), a promising multiple access technology, has been shown to be able to achieve an improved spectrum efficiency and be robust to channel uncertainties. This paper investigates the interplay between RIS and RS by considering a RIS-assisted RS beamforming problem. Active beamforming at the base station (BS) and passive beamforming at the RIS are jointly considered to maximize the minimum user rate under both the perfect CSI case and the imperfect CSI case. A block coordinate descent (BCD) algorithm is developed to solve this non-convex problem. Compared with the conventional semi-definite relaxation (SDR) approach, the proposed method does not require that the covariance matrix of the common beamforming vector be rank-one. Moreover, we present theoretical results to help reveal the impact of the system parameters and explain the performance gain resulting from the integration of RIS and RS. Extensive simulation results are also provided to show that RIS-assisted RS can improve the max-min rate performance significantly compared with conventional multiple access technologies, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) with/without RIS, especially in overloaded systems. With the proposed method, RS shows great potential in combating realistic CSI errors in RIS-assisted networks.

Energy efficiency performance in RIS-based integrated satellite–aerial–terrestrial relay networks with deep reinforcement learning

Integrated satellite–aerial–terrestrial relay networks (ISATRNs) play a vital role in next-gen networks, particularly those with high-altitude platforms (HAP). This study introduces a new model for hybrid optical/RF-based HAP-enabled ISATRNs, incorporating reconfigurable intelligent surfaces (RIS) on unmanned aerial vehicles (UAVs) to optimize access in dense urban areas. Non-orthogonal multiple access is employed for improved spectrum efficiency. The objective is to jointly optimize UAV trajectory, RIS phase shift, and active transmit beamforming while considering energy consumption. A deep reinforcement learning approach using LSTM-DDQN framework is proposed. Numerical results show the effectiveness of our algorithm over traditional DDQN, with higher single-step exploration reward and evaluation metrics.

Secondary User Access Control in Cognitive Radio Networks: Concept, Design, and Analysis

Cognitive radio (CR), an important part of nextgeneration wireless communications networks, has been extensively studied to improve the spectrum utilization. The spectrum sensing and access functions are especially crucial for ensuring the secure and efficient spectrum sharing needs of CR networks. In this article, the secondary user access control (SUAC) based spectrum access technique is investigated to improve spectrum efficiency and network safety, by which a jamming signal is carefully designed to break down unauthorized secondary users (UA-SUs) sensing result and keep reliable sensing result from authorized secondary users (A-SUs). First, SUAC techniques are described, and a jamming cancellation method based on an oblique projection approach is proposed to guarantee the sensing result of authorized secondary users. Second, a prioritized SUAC approach is examined to ensure A-SUs with distinct spectrum access levels. Third, quadratic programming in massive multipleinput multiple-output based SUAC is presented to effectively design a jamming signal that combines minimizing jammer's influences on A-SUs and maximizing influences on UA-SUs. Finally, some open research areas and challenges in SUAC are discussed.

Selective Resource Offloading in Cloud-Edge Elastic Optical Networks

With the rapid development of Internet of Things (IoT), IoT mobile devices host more computation-intensive and real-time applications, such as face recognition, online gaming, and augmented reality/virtual reality services. This paper mainly addresses the problems of the selective resource offloading in order to achieve resource optimization in cloud-edge elastic optical networks (CE-EONs). We firstly propose two integer linear program (ILP) models to minimize both the end-to-end (E2E) latency and the total number of frequency slots by considering the latency sensitivity, followed by introducing three corresponding heuristic approaches, namely resource priority offloading (RPO), distance priority offloading (DPO), and coordinated distance and resource offloading (DRO). For comparison, we also introduced an existing resource offloading (ERO) approach in CE-EONs. On one hand, simulation results show that the DRO approach greatly approximated to the optimized solutions of ILP models in the static traffic scenario. Meanwhile, the DRO approach achieves the better performance in terms of average E2E latency. On the other hand, the proposed DRO approach can significantly reduce the blocking probability owing to much improved spectrum efficiency and achieves a graceful tradeoff between the computing resources and E2E latency compared to the RPO, DPO, and ERO approaches in the dynamic traffic scenario.

Multi-domain blind source separation in-band full-duplex technique considering RF impairments

In-band full-duplex (IBFD) technology can potentially improve spectrum efficiency compared to half-duplex technology. The cancellation of the self-interference (SI) signal is the key to implementing the IBFD system to achieve accurate detection of the signal of interest. In this work, we fully account for the effect of IQ imbalance, phase noise (PN), additional Gaussian white noise (AWGN), and the nonlinear caused by the RF impairments of power amplifier, and adopt the method of canceling SI signal combined with the propagation, analog, and digital domain to cancel the SI signal, but the residual SI signal is still mixed with the signal of interest in the same time and frequency domain. In addition, a novel multi-domain blind source separation (MDBSS) self-interference cancellation method implemented by using the off-the-shelf components is proposed in this paper, which can realize the separation of residual self-interference signals from the signal of interest (SOI). Additionally, the multi-domain blind source separation (MDBSS) self-interference cancellation technique is used to achieve 75 dB of self-interference cancellation performance, which is proven through numerical simulation analysis, and the SOI is successfully separated in the receiver. Moreover, the multi-domain blind source separation (MDBSS) method in this paper can be applied to radar, jammer, and communication systems in the future.

5G-RCOLAB: A system level simulator for 5G and beyond in rural areas

This paper describes 5G-RCOLAB, an open source simulation tool for performance evaluation of 5G/6G wireless networks in rural areas. The tool is designed with a layered architecture, based on three modules: (i) a link-level simulator of a communication channel for remote areas, with a mapping Link to System (L2S) interface; (ii) a Long-Term Evolution (LTE)-based physical layer adapted for remote areas, together with a Medium Access Control (MAC) layer that includes a novel Collaborative Spectrum Sensing (CSS) technique with security features for Byzantine attacks and spectrum usage efficiency; and (iii) an application oriented network layer that emulates the behavior of typical users in rural areas. The tool has the goal to reduce simulation complexity of large scale 5G rural networks and at the same time, to represent coherently the features of the physical and MAC layers, that include new channel models that cover long distances with the use of TV White Spaces (TVWS) in the Ultra High Frequency (UHF) and Very High Frequency (VHF) bands, as well as novel CSS techniques to improve spectrum efficiency. The 5G-RCOLAB was validated both by field test measurements from a Proof of Concept (PoC) prototype and by the simulation and evaluation of three 5G common core use cases in rural areas. The results show that the 5G-RCOLAB features allow a better evaluation of novel technologies implemented in the physical and MAC layers and provide paths for improvements and deployments of cost effective solutions of 5G in rural or remote areas.

Direct detection system for independent triplet-sideband signals based on a single photodiode.

This paper proposes a novel, to the best of our knowledge, independent triplet-single-sideband (triplet-SSB) transmission system scheme aimed at increasing channel capacity and improving spectrum efficiency. The conventional independent multiband transmission systems are limited by their complexity and high computational requirements, which hinder the improvement of spectrum efficiency and channel capacity. To address these challenges, this scheme uses three independent signals, modulated by an in-phase/quadrature (I/Q) modulator, and transmits them over a 5-km standard single-mode fiber (SSMF). At the receiver end, a single photodiode (PD) is used for signal reception, and the signals are separated using digital signal processing (DSP) algorithms. Through simulation and verification, the feasibility and reliability of the system are demonstrated, with the bit error rates (BERs) of all three signals below the hard decision forward error correction (HD-FEC) threshold value of 3.8 × 10-3. This independent triplet-SSB transmission system scheme effectively improves spectrum efficiency and channel capacity, providing a valuable solution for meeting the growing demands of data transmission.