Channel Usage Research Articles

EM-AMP-Based Joint Active User Detection and Channel Estimation in Cell-Free System

This article considers the massive access problem in an uplink cell-free multiple-input–multiple-output (MIMO) system where massive potential users communicate with a set of access points (APs), while all APs are connected to a central processing unit (CPU). By exploiting the sporadic traffic of users, we use expectation maximization-approximate message passing (EM-AMP) algorithm to implement channel estimation based on the aggregate received signal of all APs at the CPU. Then, the active user detection (AUD) can be accomplished with the corresponding posterior support probabilities. It should be noted that the a priori distribution of channel coefficients plays an important role in EM-AMP, which will make a significant difference to the system performance. Moreover, provided with different prior information about the users' location, the CPU will obtain different a priori distribution of the channel coefficients. Inspired by this, instead of using a priori distribution with hypothesised parameters as the original EM-AMP does, we assume that the CPU has some prior information about the users' location. Then, we propose a scheme that can efficiently obtain the a priori distribution of channel coefficients by fully exploiting this information. After that, we propose a modified algorithm based on the derived a priori distribution. Simulation results reveal that the proposed algorithm has a noticeable performance gain than the original EM-AMP when the pilot length is relatively small. More importantly, with our proposed scheme, given rough prior information, we can achieve almost the same system performance as when the users' location is precisely known.

Promoting Cashless Economy: The Use of Online Electricity Payment Channels in the Ibadan Metropolis, Nigeria

The study was designed to investigate the level of usage of online electricity payment channels (OEPC), as well as the influential factors determining the use of the channels. The correlation type of survey research design was adopted. The population consisted of all electricity consumers in the Ibadan metropolis, Nigeria. Out of the 236 responses, about two-thirds (65.3%) were users of OEPC, while 34.7% were not. A significant relationship, strong and positive correlation was found between usefulness, ease of use, convenience ( r = 0.710, 0.704, 0.623, respectively) and use of OEPC. The relationship between security and computer self-efficacy and the use of OEPC is also positive and significant but the correlation coefficients are low ( r = 0.279, 0.252, respectively), indicating a weak relationship. All the variables jointly predicted the use of OEPC, while a 24.0% variation in use was influenced by the variables. The study concluded that the use of OEPC by consumers in the Ibadan metropolis was moderate and that the usefulness of OEPC, ease of use, convenience of using, security of the channels and the computer self-efficacy of the users influenced the use of the channels. The implication is that these factors could be leveraged to promote an increase in awareness, adoption and use of OEPC in Nigeria.

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.

Joint Optimization Scheme of User Association and Channel Allocation in 6G HetNets

The sixth-generation (6G) wireless cellular network integrates several wireless bands and modes with the objectives of improving quality of service (QoS) and increasing network connectivity. The 6G environment includes asymmetrical heterogeneous networks (HetNets) with the intention of making effective use of the available frequencies. However, selecting a suitable gNB and a communication channel that works for users in the network is an enormous challenge in 6G HetNets. This paper investigates a joint user association (UA) and channel allocation (CA) problem in two-tier HetNets by considering the downlink scenario to improve QoS. Our study presents an innovative scheme for user association and channel allocation, wherein the user can be connected to either the macro base station (MBS) or a possible small base station (SBS) in a direct or relay-assisted link. Furthermore, the proposed scheme identifies the optimal channel to be allocated to each user so that the overall network QoS can be maximized. A symmetric matching game-based user association is proposed to find the optimal association for users. Moreover, a modified auction game is applied to allocate the optimal channel by considering the quota of each gNB. Regarding connection probability, throughput, energy efficiency (EE), and spectrum efficiency (SE), the simulation results show that the proposed approach performs well over the state-of-the-art techniques.

Spectrum Allocation and User Scheduling Based on Combinatorial Multi-Armed Bandit for 5G Massive MIMO.

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.

Multi‐agent reinforcement learning based transmission scheme for IRS‐assisted multi‐UAV systems

AbstractIn this paper, a transmission scheme based on multi‐agent reinforcement learning for intelligent reflecting surface (IRS)‐assisted multiple unmanned aerial vehicles (UAVs) systems is proposed. The proposed scheme is based on reinforcement learning and alternating optimization algorithm, which can effectively improve communication quality and ensure fairness. The scheme is divided into two parts. In the first part, the multi‐UAV cooperation problem is modeled as a markov decision process. The objective of each UAV is to maximize the minimum user channel gain. To achieve stable strategies for all agents, the Multi‐agent Deep Deterministic Policy Gradient (MADDPG) algorithm is applied to train UAVs trajectories to reach the Nash equilibrium. The MADDPG algorithm is centralized trained at the base station and executed in a distributed manner by each UAV, ensuring efficient and effective coordination among agents. In the second part, an alternating optimization algorithm is formulated to optimize active and passive beamforming. Considering the non‐convexity of the fairness objective, by using auxiliary variables and semi‐definite relaxation method, the problem of maximizing the minimum user achievable rate is transformed into a feasibility problem. Simulation results show that the proposed scheme can effectively train UAVs trajectories and improve the communication performance of all users fairly.

Channel Estimation for Spectrum Sharing in Massive MIMO Communications

In this paper, we investigate the problem of channel estimation in a multi-user massive multiple-input multiple-output (MIMO) secondary network (SN) aiming to access the licensed spectrum of a multi-user massive MIMO primary network (PN) using the underlay spectrum sharing approach. We estimate the channels of the single-antenna primary users (PUs) and those of the secondary users (SUs) at the secondary base station by exploiting a learning phase. To do so, we design the SN’s training phase with the priority of mitigating pilot contamination at the PN. This aim is pursued under the desired restriction that the PN is not meant to change its training phase length in the presence of the SN. The proposed estimator of PUs’ channels is based on the PUs’ data in addition to their pilots. To estimate the SUs’ channels, we present two seemingly different pilot-based approaches and prove rigorously that they result in the same estimator. Our numerical results illustrate that employing the proposed technique enables the SN to control the interference that it causes at the PN at the cost of slight performance degradation in terms of the quality of SUs’ channel estimates at the SN base station.

Spatial Modulation Aided Physical Layer Security for NOMA-VLC Systems

We consider the physical layer security (PLS) problem in multi-user non-orthogonal multiple access (NOMA) enabled multiple-input multiple-output (MIMO) visible light communication systems intercepted by a passive eavesdropper (Eve). We propose a novel transmit precoding scheme based on receive spatial modulation (RSM) to degrade the signal-to-interference-noise ratio (SINR) of Eve by exploiting only the slow-fading characteristics of the visible light channel of the legitimate users (Bobs). The proposed PLS precoder is reinforced with secret parameter exchange with Bobs and a CSI acquisition model is proposed to reduce the PLS algorithm's computational load substantially at the transmitter. The closed-form expressions for the achievable secrecy rates and their upper and lower bounds are derived. Via Monte Carlo simulations, we confirm that Bobs can successfully decode their information in various user configurations while Eve's received SINR is significantly worsened by the jamming signal induced by the proposed precoder with secret key exchange. It is also shown that Eve's bit error rate (BER) is increased to the 0.5-level for almost any position in the considered indoor environment. Finally, we corroborate the derived secrecy expressions by computer simulations and show that the proposed scheme provides PLS for Bobs.

Revealing Smart Selective Jamming Attacks in WirelessHART Networks

As a leading industrial wireless standard, WirelessHART has been widely implemented to build wireless sensor-actuator networks (WSANs) in industrial facilities, such as oil refineries, chemical plants, and factories. For instance, 54,835 WSANs that implement the WirelessHART standard have been deployed globally by Emerson process management, a WirelessHART network supplier, to support process automation. While the existing research to improve industrial WSANs focuses mainly on enhancing network performance, the security aspects have not been given enough attention. We have identified a new threat to WirelessHART networks, namely smart selective jamming attacks, where the attacker first cracks the channel usage, routes, and parameter configuration of the victim network and then jams the transmissions of interest on their specific communication channels in their specific time slots, which makes the attacks energy efficient and hardly detectable. In this paper, we present this severe, stealthy threat by demonstrating the step-by-step attack process on a 50-node network that runs a publicly accessible WirelessHART implementation. Experimental results show that the smart selective jamming attacks significantly reduce the network reliability without triggering network updates.

A Proposed Channel Estimation Framework for Reconfigurable-Intelligent-Surface-Aided MIMO Communication System

Reconfigurable intelligent surface (RIS) is considered a promising technology for dense Internet of Things (IoT) networks to enhance spectral efficiency and coverage due to its lower energy consumption and decreased hardware cost. However, channel estimation is a significant challenge for two reasons; first, RIS reflects signals without any signal processing capabilities, and second, it contains large numbers of reflected elements, making the pilot overhead extremely high. In this paper, we propose a two-phased RIS channel estimation framework for MIMO uplink transmissions with lower pilot overhead. In phase I, we take advantage of the fact that RIS elements reflect all IoT devices through the same BS-RIS channel and propose a dual-link time division duplex (TDD) pilot transmission scheme for estimating the BS-RIS channel where the pilots are transmitted from BS to RIS and then reflected back to BS. Afterward, we propose an algorithm to obtain the BS-RIS channel from the dual link BS-RIS-BS cascaded channel. In phase II, we estimate the user channels between RIS and devices based on the knowledge of the BS-RIS channel obtained in phase I. Simulation results indicate that the proposed framework attained a lower normalized mean square error (NMSE) and higher sum rate than other channel estimation frameworks with lower pilot overhead and complexity overhead.

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-channel Scheduling with Interference Avoidance and Precedence Based Algorithm in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) is the wireless networking domain that has been preferentially attracted by many researchers. Multi radio and multichannel WMN has limited channels due to the constraints given by interferences caused internally and externally within the channels. The research is focused on maximizing the performance in multi radio and multi channel WMNs. The channel overlapping that happens partially can overcome this limitation of resources. Interference Avoidance and Precedence Based Algorithm(IAPB) is proposed to reduce the interferences that occur in WMNs and improve the performance.The interference involved during the usage of channels can be reduced by using the channels with less interference. The interference involved is reduced by consideration of precedence level of interference during assignment of channel. The proposed algorithm results in performance improvement in terms of network throughput, the packet loss, delay involved in the reduction of interference.

OWL: A data sharing scheme with controllable anonymity and integrity for group users

As our society becomes digital and communication technology develops, global collaboration is an inevitable trend, where data-sharing is a critical component of cooperation across organizations. Identity privacy and data integrity are vital issues in data-sharing. Existing works struggle to address these problems simultaneously, either privacy leaking or privacy abuse. In this work, we proposed OWL, a data-sharing scheme that (1) provides users on-demand anonymity and (2) allows users to verify data integrity while preserving anonymity. To achieve (1) OWL enables controllable anonymity that allows de-anonymity for the malicious while keeping anonymity to the honest providers based on traceable ring signature technology. To achieve (2), OWL designs a data integrity auditing scheme that uses vector commitment to verify data integrity without privacy leakage. Furthermore, OWL employs the blockchain to store immutable auxiliary information for the integrity and controllable anonymity. We also employ the state channel to resolve the performance bottleneck of blockchain and design methods to improve the usage of the state channel for group users. We prove that OWL achieves controllable anonymity and integrity. Finally, we implement the experiment to evaluate the efficiency of OWL.

Sensing User's Channel and Location With Terahertz Extra-Large Reconfigurable Intelligent Surface Under Hybrid-Field Beam Squint Effect

This paper investigates the sensing of user's uplink channel and location in terahertz extra-large reconfigurable intelligent surface (XL-RIS) systems, where the unique hybrid far-near field effect and the beam squint effect caused by the XL array aperture as well as the XL bandwidth are overcome. Specifically, we first propose a joint channel and location sensing scheme, which consists of a location-assisted generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-OMP) algorithm for channel estimation (CE) and a complete dictionary based localization (CDL) scheme, where a frequency selective polar-domain redundant dictionary is proposed to overcome the hybrid field beam squint effect. The CE module outputs coarse on-grid angle estimation (respectively observed from the BS and RIS) to the localization module, which returns the fine off-grid angle estimation to improve CE. Particularly, with RIS, CDL can obtain user's location via line intersection, and a polar-domain gradient descent (PGD) algorithm at the base station is proposed to achieve the off-grid angle estimation with super-resolution accuracy. Additionally, to further reduce the sensing overhead, we propose a partial dictionary-based localization scheme, which is decoupled from CE, where RIS is served as an anchor to lock the user on the hyperbola according to time difference of arrival and the user's off-grid location can be obtained by using the proposed PGD algorithm. Simulation results demonstrate the superiority of the two proposed localization schemes and the proposed CE scheme over state-of-the-art baseline approaches.

Acquisition of Agricultural Information from the Social Media and Interpersonal Channels by Farmers

The primary goal of this research is to discover and explain how farmers in Faisalabad utilise social media and interposed channels to receive agricultural knowledge. Furthermore, the study was intended to investigate farmers' socio-demographic traits and their subsequent link with the usage of social media and interpersonal communication channels. Data for this research study were gathered from 257 farmers in the Faisalabad district. A structured questionnaire was utilised to collect data as part of the survey approach. Among the social media, Facebook is the most used media and among interpersonal channels Cosmopolite's character of famers was rated highest.

Application of hart concentrator to save the number of input analogue channel usage in PLC PAC system on SCADA cimplicity

The types of inputs and outputs of a control system that exist today are quite varied with their respective advantages and disadvantages that can be adapted to existing needs. So with the complexity of the system, the price is also very expensive. As one option to overcome this problem is to utilize Highway Addressable Remote Transducer (HART) data through the application of the HART Concentrator System (HCS). HART data has four variables, namely Primary Value (PV), Secondary Value (SV), Third Value (TV) and Fourth Value (FV). All this data can be sent simultaneously to HCS and collected. Then the data on the HCS will be sent via RS485 serial communication to the Modbus Gateway TCPIP. At this Modbus TCPIP gateway, Modbus data can be sent via an Ethernet network so that it can be accessed flexibly by SCADA.

Performance Analysis of Radio Resource Allocation to Maximize Power Efficiency in Device-To-Device Communication


 
 
 Technological developments in the field of telecommunications have progressed. However, the more cellular network users, the more traffic on the Base Station (BS) will be. One way to overcome this is to implement a Device-To-device (D2D) communication system. However, when D2D User Equipment (DUE) reuses Cellular User channel resources Equipment (CUE), then interference will occur. This research is conducted to address interference problems and maximize energy efficiency for CUEs and DUEs by implementing the Greedy algorithm with additional power control. The aim is to block unnecessary resources and optimize resource allocation. The Optimal Power Control (OPC) scheme will utilize the transmit power threshold. The simulation scheme for the D2D communication system used is the uplink and underlay mode single-cell system model. Where the DUE pair will use the same resources as the CUE that has been given by the BS. Resources in CUE can only be reused by one pair of DUEs, and vice versa. The greedy algorithm using power control gets superior performance at a sum rate of 1.79 × 107 bps with an increase of 36.03%. Spectral efficiency of 2.49 bps/Hz with an increase of 36.03%. The power efficiency of 2.08 × 103 bps/mw with an increase of 118.47%. Based on the result the greedy algorithm without power control gets superior performance at CU Fairness of 1 with an increase of 50.14%.
 
 

Engineering Ultra-Permeable and Antifouling Water Channel-based Biomimetic Membranes toward Sustainable Water Purification

Water channel-based biomimetic membranes (WBMs) are gaining increasing attention due to the effectiveness of water channels in enhancing water permeability and breaking the permselectivity trade-off. However, the ultra-permeable WBMs may suffer from severe membrane fouling issue because a high-water flux tends to result in an accelerated fouling and thus compromises the benefits gained from the usage of water channels. Herein, a novel in-situ modification protocol was proposed to enhance the antifouling performance of ultra-permeable WBMs. The nanovesicles incorporated with aquaporin (AQP) water channels were functionalized with polyethylene glycol brushes (i.e., PEGylation) via a facile self-assembly approach and subsequently encapsulated in the selective layer of thin-film composite membranes through interfacial polymerization. The modification had minimal impact on the function of AQPs, resulting in WBMs with a high water permeance (∼8.2 LMH/bar) and good NaCl rejection (96.4%) comparable to the unmodified WBMs. Moreover, the in-situ modification drastically enhanced the surface hydrophilicity, which endowed the membrane with a superior fouling resistance to organic foulants. The improved fouling resistance ensured a more sustainable operation of ultra-permeable WBMs, particularly in scenarios that favor high water fluxes. This facile modification strategy provides an efficient way to fabricate ultra-permeable and antifouling WBMs for sustainable water purification.

The Effect of Pilot Reuse Factor on Massive MIMO Spectral Efficiency

Due to their much increased spatial resolution and array gain, large-scale antenna arrays have the potential to significantly enhance the spectral efficiency of wireless systems. Recent research in the area of massive Multiple-Input Multiple-Output (MIMO) antennas shows that user channels are enhanced as the number of antennas at the Base Stations (BSs) rises, allowing achieving high signal improvement with minimum inter-user interference. The combined effects of the pilot reuse factor and the BS number of antennas on average overall spectral efficiency in huge MIMO antennas will be investigated. To examine the SE of these receivers, several channel estimators will be utilized, including LMMSE, ZF, and MR methods. Theoretically, BS could offer a substantially higher average total SE if it had more antennas.