Channel Conditions Of Users Research Articles

Performance Analysis of NOMA Systems over Rayleigh Fading Channels in 5G Communication

This paper aims to analyse the performance of Non-vertical Multiple Access (NOMA) systems operating in radio communication systems, especially under Rayleigh fading channels. While NOMA technology improves spectrum efficiency by allowing users to share the same time and frequency resource, Rayleigh fading channels represent multipath propagation effects commonly encountered in real radio environments. In this research, the effects of different user power levels and channel conditions on performance metrics such as system capacity, error rate (BER) and energy efficiency are investigated. Using mathematical modelling and simulation methods, the performance advantages and limitations of NOMA under Rayleigh fading conditions are evaluated. The findings show that NOMA systems offer significant advantages in terms of spectrum efficiency and user fair sharing in Rayleigh fading channels, but require careful design for error rate and energy consumption optimisation. This study provides an important reference for more effective implementation of NOMA technology in radio communication systems.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

Rate-Diverse Multiple Access Over Gaussian Channels

In this work, we develop a pair of rate-diverse encoder and decoder for a two-user Gaussian multiple access channel (GMAC). The proposed scheme enables the users to transmit with the same codeword length but different coding rates under diverse user channel conditions. First, we propose the row-combining (RC) method and row-extending (RE) method to design practical low-density parity-check (LDPC) channel codes for rate-diverse GMAC. Second, we develop an iterative rate-diverse joint user messages decoding (RDJD) algorithm for GMAC, where all user messages are decoded with a single parity-check matrix. In contrast to the conventional network-coded multiple access (NCMA) and compute-forward multiple access (CFMA) schemes that first recover a linear combination of the transmitted codewords and then decode both user messages, this work can decode both the user messages simultaneously. Extrinsic information transfer (EXIT) chart analysis and simulation results indicate that RDJD can achieve gains up to 1.0 dB over NCMA and CFMA in the two-user GMAC. In particular, we show that there exists an optimal rate allocation for the two users to achieve the best decoding performance given the channel conditions and sum rate.

Echo State Learning for User Trajectory Prediction to Minimize Online Game Breaks in 6G Terahertz Networks

Mobile online gaming is constantly growing in popularity and is expected to be one of the most important applications of upcoming sixth generation networks. Nevertheless, it remains challenging for game providers to support it, mainly due to its intrinsic and ever-stricter need for service continuity in the presence of user mobility. In this regard, this paper proposes a machine learning strategy to forecast user channel conditions, aiming at guaranteeing a seamless service whenever a user is involved in a handover, i.e., moving from the coverage area of one base station towards another. In particular, the proposed channel condition prediction approach involves the exploitation of an echo state network, an efficient class of recurrent neural network, that is empowered with a genetic algorithm to perform parameter optimization. The echo state network is applied to improve user decisions regarding the selection of the serving base station, avoiding game breaks as much as possible to lower game lag time. The validity of the proposed framework is confirmed by simulations in comparison to the long short-term memory approach and another alternative method, aimed at thoroughly testing the accuracy of the learning module in forecasting user trajectories and in reducing game breaks or lag time, with a focus on a sixth generation network application scenario.

Multi-Armed Bandit for Link Configuration in Millimeter-Wave Networks: An Approach for Solving Sequential Decision-Making Problems

Establishing and maintaining millimeter-wave (mm-wave) links are challenging due to the changing environment and the high sensibility of mm-wave signals to user mobility and channel conditions. mm-Wave link configuration problems often involve a search for optimal system parameter(s) under environmental uncertainties from a finite set of alternatives that are supported by the system hardware and protocol. For example, beam sweeping aims at identifying the optimal beam(s) for data transmission from a discrete codebook. Selecting parameters such as the beam sweeping period and the beamwidth is crucial to achieving high overall system throughput. In this article, we motivate the use of the multi-armed bandit (MAB) framework to intelligently search out the optimal configuration when establishing the mm-wave links. MAB is a reinforcement learning framework that guides a decision maker to sequentially select one action from a set of actions. As an example, we show that within the MAB framework, the optimal beam sweeping period, beamwidth, and beam directions could be dynamically learned with sample-computational-efficient bandit algorithms. We conclude by highlighting some future research directions on enhancing mm-wave link configuration design with MAB.

Time Synchronization and Signal Detection in Non-Orthogonal Unicast and Broadcast Networks

The next generation digital TV (DTV) broadcast, namely Advanced Television System Committee (ATSC) 3.0, relies on substantial single frequency networks (SFN), each of which requires a studio to transmitter link (STL) to transmit data from the broadcast gateway (BG), leading to significant construction costs. Therefore, the wireless in-band distribution link (IDL) technology is proposed, which uses layered-division multiplexing (LDM) to transmit the STL data via wireless links from the BG to the SFN transmitters within the same spectrum as the broadcast services. In this paper, we propose the time synchronization and signal detection algorithm for wireless IDL systems with both unicast and broadcast signals. We introduce two multiplexing schemes, N-LDM and 3-LDM, and demonstrate their superiority over traditional frequency division multiplexing (FDM). To overcome the challenge of multi-access interference and synchronization errors caused by multipath, we develop a unique word (UW) design algorithm for transmitted signals and a double threshold correlation detection algorithm (DTCDA) that outperforms traditional ones. We employ a second threshold to enhance detection performance at low signal-to-noise ratios, and utilizes successive interference cancellation (SIC) to mitigate multiple-access interference. More importantly, our approach requires no knowledge of the unicast user number or channel state information. Simulation results validate the effectiveness of the proposed scheme.

Deep Learning-Based User Activity Detection and Channel Estimation in Grant-Free NOMA

In the uplink machine-type communication (MTC) system, a combination of grant-free transmission and non-orthogonal multiple access (NOMA) emerges to reduce the control overhead and transmission latency. In the grant-free scenario, the base station needs to identify the active devices and estimate the channel state information before the data detection. However, due to the lack of a scheduling process, the user activity detection (UAD) and channel estimation (CE) are both challenging, especially when short non-orthogonal preambles are adopted. In this paper, by exploiting the framework of the compressive sensing-based algorithm, we propose a novel deep learning architecture, namely UAD and CE Neural Network (UAD-CE-NN), to effectively solve the joint UAD and CE problem for grant-free NOMA. In the proposed scheme, the user activity and channel state information hidden in the received data signals are also exploited to aid the preamble for higher detection accuracy. Specifically, UAD-CE-NN is composed of two stages: we first build a preamble detection neural network for a tentative UAD-CE; a data detection neural network is then deployed to exploit the data signals. Compared with the conventional schemes, the proposed scheme obtains much higher accuracy for both the UAD and CE, especially when short preamble sequences are employed.

Physical layer security performance of NOMA-assisted vehicular communication systems over double-Rayleigh fading channels

In this paper, we investigate the secrecy performance of a downlink non-orthogonal multiple access enabled V2V communication system wherein a source vehicle communicates with two authenticated user vehicles, i.e., far user and near user, in the presence of a passive eavesdropper vehicle. Moreover, we formulate two scenarios based on the eavesdropper’s decoding capabilities; (1) Scenario I: when the eavesdropper vehicle has comparable decoding capabilities as with the authorized user vehicles, and (2) Scenario II: when the eavesdropper is entirely capable of perfectly decoding the signals from both authorized user vehicles. For such a system configuration with Scenarios I &II, we deduce the analytical expressions for the secrecy outage probability (SOP) and ergodic secrecy capacity over independent but not necessarily identically distributed double-Rayleigh fading channels. Further, to obtain insights into the secrecy diversity order for the legitimate user vehicles under Scenarios I &II, we present the asymptotic SOP analysis by taking three cases into account; (1) Case 1: when the average transmit signal-to-noise ratio approaches infinity, (2) Case 2: when the average channel gains of the user vehicles tend to infinity with fixed average channel gains corresponding to the eavesdropper, and (3) Case 3: when the average channel gains pertaining to the user vehicles and the eavesdropper tend to infinity. From which, we can infer that the secrecy diversity order of the far user vehicle is zero for Cases 1, 2, & 3, whereas the secrecy diversity order of the near user vehicle is zero for Cases 1 & 3 and one for Case 2, under Scenarios I &II. The numerical and simulation results corroborate our theoretical investigations. Our results demonstrate the impact of transmit power, power allocation factor, channel conditions of legitimate users and eavesdropper on the system’s secrecy performance.

Cooperative Robust Video Multicast in Integrated Terrestrial-Satellite Networks

Integrated terrestrial-satellite networks (ITSNs) are the promising trends of future networks. However, there exist great challenges when performing video multicast in ITSNs due to the strong heterogeneity of users in comprehensive satellite coverage and the inter-system complex co-channel interference. To overcome these, we make the first attempt to propose an efficient cooperative robust video multicast (CRVM-ITSN) framework in ITSNs. The basic idea is to leverage the non-orthogonal multiple access technique in the cooperative transmission of ITSNs to achieve high-efficiency robust video multicast. The desired robust multicast performance realizes that the recovered video quality can adapt to diverse channel conditions of users. In CRVM-ITSN, to achieve the optimal cooperative transmission performance, power allocation and chunk scheduling of video data are jointly formulated as a distortion minimization problem, which is a non-convex mixed integer non-linear programming problem. To solve it, we design a provably convergent optimal algorithm by converting it to be convex. Besides, based on the theorem on optimal chunks selection of satellite cooperative transmission, a low-complexity chunk grouping algorithm is proposed to accelerate the optimal algorithm. Simulation results have demonstrated the superiority of proposed CRVM-ITSN against existing reference schemes, achieving about 4.1dB more gains in the recovered video quality.

Wireless Resource Scheduling for High Mobility Scenarios: A Combined Traffic and Channel Quality Prediction Approach

The rapid growth of mobile data traffic in a high-speed scenario imposes great challenges on wireless system design and optimization, where the inherent Doppler shift, frequent cell switching, and large penetration loss will significantly degrade the quality of transceiver signals. Wireless resource scheduling dynamically allocates wireless resources according to the user channel conditions and quality of service requirement, which is an enabling approach to maximize the resource utilization. In this paper, we develop a new wireless resource scheduling method in high mobility scenario to uplift the system performance. We first propose a feature extraction algorithm to extract the spatial traffic characteristics. On this basis, several time-series prediction algorithms are leveraged to predict the traffic volume. Then, we introduce cell switching indicator (CSI) into channel quality prediction to improve the signal-to-noise ratio (SNR) prediction performance. Based on the predicted results of service traffic and user channel quality variations, a resource scheduling strategy is proposed, which compromises the total system throughput and user satisfaction. Simulation results validate the effectiveness of the proposed scheduling algorithm.

A Context-Aware Radio Resource Management in Heterogeneous Virtual RANs

New-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. Here, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness, we develop a testbed for proof-of-concept. Experimental results demonstrate that CAREM enables an efficient radio resource allocation under different settings and traffic demand. Also, compared to the closest existing scheme based on neural network and the standard LTE, CAREM exhibits an improvement of one order of magnitude in packet loss and latency, while it provides a 65% latency improvement relatively to the contextual bandit approach.

Multi-Agent Deep Reinforcement Learning-Empowered Channel Allocation in Vehicular Networks

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles’ mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department.

Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends

Rate-splitting multiple access (RSMA) has emerged as a novel, general, and powerful framework for the design and optimization of non-orthogonal transmission, multiple access (MA), and interference management strategies for future wireless networks. Through information and communication theoretic analysis, RSMA has been shown to be optimal (from a Degrees-of-Freedom region perspective) in several transmission scenarios. Compared to the conventional MA strategies used in 5G, RSMA enables spectral efficiency (SE), energy efficiency (EE), coverage, user fairness, reliability, and quality of service (QoS) enhancements for a wide range of network loads (including both underloaded and overloaded regimes) and user channel conditions. Furthermore, it enjoys a higher robustness against imperfect channel state information at the transmitter (CSIT) and entails lower feedback overhead and complexity. Despite its great potential to fundamentally change the physical (PHY) layer and media access control (MAC) layer of wireless communication networks, RSMA is still confronted with many challenges on the road towards standardization. In this paper, we present the first comprehensive overview on RSMA by providing a survey of the pertinent state-of-the-art research, detailing its architecture, taxonomy, and various appealing applications, as well as comparing with existing MA schemes in terms of their overall frameworks, performance, and complexities. An in-depth discussion of future RSMA research challenges is also provided to inspire future research on RSMA-aided wireless communication for beyond 5G systems.

Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish missed detection probability of 1% in the Rayleigh channel, the false alarm probability obtained is almost 0.8 however to obtain the same missed detection probability in the Weibull channel, false alarm probability is less than 0.1 which is very favorable for both indoor and outdoor scenarios. Numerical analyses are carried out here to predict Primary User (PU) channel condition using Hidden Markov Model with the help of Time series forecasting learning method. It is evident that the prediction performance has reached 100% as the result of using the Weibull Fading Model for a period of 200 ms when compared to the Rayleigh model which is achieving only 84.5% accuracy in prediction.

On Optimal Orchestration of Virtualized Cellular Networks With Statistical Multiplexing

Wireless network virtualization is emerging as a promising technology for cellular networks. A key advantage of introducing virtualization in cellular networks is that wireless services can be decoupled from network resources (e.g., infrastructure and spectrum) so that multiple virtual networks can be built using a shared pool of network resources. This paper develops an optimization framework for orchestrating virtualized cellular networks while enabling and exploiting statistical multiplexing. Our proposed framework has two phases: virtual network deployment (static) and statistical multiplexing (adaptive). In the virtual network deployment phase, network resources are aggregated, sliced, and allocated to the virtual networks considering the presence of uncertainty in user equipment (UE) locations and channel conditions, without knowing which realization of UE locations and channel conditions will occur. Once the virtual networks are deployed, each of the aggregated base stations (BSs) performs statistical multiplexing, i.e., allocates excess resources from the over-satisfied slices to the under-satisfied slices, according to the realized channel conditions of associated UEs. Our numerical results demonstrate that the proposed framework outperforms existing virtualization frameworks in terms of probabilistically satisfying virtual networks’ rate and coverage demands while minimizing resource over-provisioning, in the presence of uncertainty in UE locations and channel conditions.

Mobility-Aware Offloading and Resource Allocation in a MEC-Enabled IoT Network With Energy Harvesting

Mobile-edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate the EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras, such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this article, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and EH. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the central process unit frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on the Lyapunov optimization and semidefinite programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of the user mobility, EH model, and channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Fairness for Freshness: Optimal Age of Information Based OFDMA Scheduling With Minimal Knowledge

It is becoming increasingly clear that an important task for wireless networks is to minimize the age of information (AoI), i.e., the timeliness of information delivery. While mainstream approaches generally rely on the real-time observation of user AoI and channel state, there has been little attention to solve the problem in a complete (or partial) absence of such knowledge. In this article, we present a novel study to address the optimal blind radio resource scheduling problem in orthogonal frequency division multiplexing access (OFDMA) systems towards minimizing long-term average AoI, which is proven to be the composition of time-domain-fair clustered round-robin and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic solutions that are near-optimal as shown by simulation results are also proposed to effectively improve the performance upon presence of various degrees of extra knowledge, e.g., channel state and AoI.

A Joint Optimization Framework for Network Deployment and Adaptive User Assignment in Indoor Millimeter Wave Networks

Millimeter wave (mmW) systems typically use beamforming techniques to compensate for the high pathloss. However, directional communications in the presence of uncertainty in user equipment (UE) locations and channel conditions make maintaining coverage and connectivity challenging. In this context, we propose a joint optimization framework to determine the minimum number of required access points (APs), their optimal locations, their optimal beam directions, and their optimal assignments to individual UEs in order to maintain a network-wide signal-to-noise ratio (SNR) coverage and stable connections. The network deployment decisions (i.e., the required number of APs, their placements, and their beam directions) are static and are taken before UE locations and channel conditions are revealed. The UE assignment decisions are taken under each realization of UE locations and channel conditions considering the availability and stability of the mmW beams. We develop our joint optimization framework following a two-stage chance-constrained stochastic optimization model. Our numerical results demonstrate the gains brought by our proposed framework in terms of reducing cost of network deployment while ensuring a network-wide SNR coverage and stable connections under various UE distributions and system parameters.

Cooperative Rate-Splitting for Downlink Multiuser MISO Systems With Partial CSIT

Multiple antenna technology and cooperative relaying are two efficient methods to improve the capacity and reliability of wireless networks. By marrying cooperative relaying with a Rate-Splitting (RS) approach, Cooperative Rate-Splitting (CRS) shows certain benefits compared to several non-cooperative and cooperative schemes in multi-antenna Broadcast Channel (BC). In this paper, we further investigate the CRS with partial Channel State Information at the Transmitter (CSIT), as partial CSIT is an inevitable issue in practical downlink Multi-user Multiple Input Single Output (MU-MISO) systems. The proposed CRS structure includes multiple users, and one user could be selected as a relay to help the other users. With the flexible design, CRS leverages the benefits of user cooperation and relay (or user) selection for cooperative network, and also retains the superiority of RS for multi-antenna BC. Considering the Weighted Average Sum Rate (WASR) performance, we aim to solve the stochastic optimization that jointly involves the precoder design, the time resource allocation, and the relay selection issue. To address the above non-convex problem, a dedicated algorithm for CRS that exploits the Weighted Minimum Mean Square Error (WMMSE) approach and the Convex-Concave Procedure (CCP) is proposed, such that the WASR is maximized by jointly updating the precoding matrix and time allocation factor according to the incoming user channel state estimate. The proposed algorithm then enables us to solve the relay selection through an exhaustive search. Since the optimal relay selection algorithm has a high computational complexity when the number of users is large, we additionally provide a suboptimal heuristic algorithm to strike a balance between complexity and performance. Numerical results demonstrate that the proposed CRS scheme is capable of achieving a larger WASR than its non-cooperative counterpart and several existing beamforming schemes in a wide range of propagation conditions, and the heuristic algorithm achieves a performance close to the optimal one but with lower complexity.