Improve Link Reliability Research Articles

HCEL: Hybrid Clustering Approach for Extending WBAN Lifetime

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.

A Service-Driven Routing Algorithm for Ad Hoc Networks in Urban Rail Transit

Due to increased traffic pressure, traditional urban rail vehicle–ground communication systems are no longer able to meet the increasing communication requirements. In this paper, ad hoc networks are applied to urban rail transit vehicle–ground communication systems to improve link reliability and reduce transmission delay. In the proposed network, a service-driven routing algorithm is proposed, which considers the distance factor for cluster head selection and optimizes the routing transmission delay by service priority and congestion level. An auxiliary node-based routing maintenance mechanism is also proposed to avoid the problem of frequent breakage of communication links due to the high-speed movement of trains. Through the simulation, the proposed algorithm can effectively reduce the packet loss rate, end-to-end delay, and routing overhead of vehicle–ground communication compared with the traditional routing algorithm, which is more conducive to meeting the next generation of urban rail transit vehicle–ground communication requirements.

Hybrid Transmission Scheme for Improving Link Reliability in mmWave URLLC Communications

Ultra-Reliable and Low-Latency Communication (URLLC) represents a key ingredient of current 5G and future 6G mobile communication networks. The demanding reliability requirement set for URLLC claims for novel techniques that can deliver the necessary level of reliability without sacrificing the overall system capacity. In this context, this work presents and analyses a hybrid transmission scheme for improved reliability in millimetre wave bands with adaptive diversity combining. The proposed scheme is based on a hybrid approach that combines two links, one in the FR1 band (characterised by lower capacity but higher reliability due to more favourable propagation) and one in the FR2 band (offering higher capacity but experiencing a less reliable connectivity). The proposed scheme dynamically adapts the usage of both links in order to exploit the complementary characteristics of both bands (reliability of FR1 bands and capacity of FR2 bands) by switching between FR2-only and joint FR1-FR2 transmission according to the instantaneous channel quality in the main FR2 link. The performance is evaluated under two popular and well-known diversity combining techniques, namely Selection Combining (SC) and Maximal Ratio Combining (MRC). The obtained results demonstrate that the proposed scheme can achieve the same level of reliability as a continuous dual-link transmission scheme but with a much lower level of links usage and without sacrificing (and indeed enhancing) the capacity, thus making it a suitable candidate to deliver URLLC services in a resource-efficient manner.

Intelligent Hierarchical Network Slicing Based on Dynamic Multi-Connectivity in Cell-Free Distributed Massive MIMO Systems

In order to provide customized services for the future sixth-generation (6 G) mass business, we propose a two-timescale intelligent radio access network (RAN) slicing scheme under the architecture of cell-free distributed massive multiple-input multiple-output (MIMO) systems. Cell-free distributed massive MIMO systems have powerful macro diversity gain and multi-user interference suppression capabilities to improve the performance of different slices, and are more flexible in terms of service types based on network slicing. In the proposed scheme, we utilize the long-term and short-term trends of network to achieve adaptive resource allocation at different timescales, so as to utilize resources more effectively and meet performance requirements in parallel. Moreover, multi-connectivity is utilized to improve link reliability and further improve system performance, and user clustering reduces the impact of pilot contamination on system performance. In order to implement the RAN control strategy effectively, an efficient two-level deep reinforcement learning framework is proposed and the multi-agent reinforcement learning algorithm is used to realize efficient network resource interaction in multi-device scenarios. Simulation results further verify the effectiveness of the proposed intelligent RAN slicing scheme.

Performance Analysis of Optical Reflecting Surface-Assisted Optical Space Shift Keying-Based MIMO-FSO System

Recently, the use of reconfigurable intelligent surfaces (RIS) has gained popularity and is emerging as a promising technique to provide improved link reliability and enhanced coverage area. In this paper, we propose an optical reflecting surface (ORS)-assisted free space optics (FSO) communication system, which is based on optical space shift keying (OSSK) technique. Specifically, the closed-form expression for probability density function (PDF) of the ORS-assisted FSO channel is derived over Malaga turbulence model. Further, we have obtained the moment generating function (MGF) of the instantaneous signal-to-noise ratio (SNR) of the overall OSSK-based multiple-input multiple-output (MIMO)-FSO system. Using the derived channel statistics, an upper bound expression for the average bit error rate (BER) and a lower bound for the ergodic capacity are derived. Further, the asymptotic BER is utilized to calculate the diversity gain of the system. Numerical results are provided to corroborate the theoretical analysis of the system, along with insightful discussions. It is observed from the numerical results that the atmospheric turbulence and pointing errors have a negligible effect on the performance of the proposed system. Finally, a trade-off is noticed with respect to the average BER performance versus the spectral efficiency of the proposed system.

Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks

In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent’s retraining time with the satellite visibility intervals to investigate the effectiveness of the agent’s learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.

A multiple‐input, multiple‐output broadcasting system with space, time, polarization, and labeling diversity

AbstractEnhancing the reliability of wireless links plays an important role in addressing the digital divide between under‐developed and developed nations. Diversity techniques are used in wireless communication to improve link reliability. This article presents a broadcasting system that incorporates space, time, polarization and labeling diversity. The key challenge in incorporating labeling diversity into a system is the design of appropriate bit‐to‐symbol mappers. The latest technique to designing bit‐to‐symbol mappers is to use a genetic algorithm approach, which is applicable to any generic modulation scheme. This article presents a modified genetic algorithm mapper design algorithm based on recent advances in labeling diversity evaluation theory. The proposed system is studied under ideal (uncorrelated) conditions, as well as in the presence of inter‐beam and inter‐antenna inference (correlated conditions). Analytical expressions are presented to model both the correlated and uncorrelated systems, and are verified via Monte Carlo simulations. When compared to the best comparable scheme at a bit‐error‐rate of , results show that the proposed system improves performance by 7 dB for a 16APSK system configuration, and by 5 dB for both 32APSK and 64APSK system configurations. Results also show that the proposed system is highly sensitive to correlation at the transmitting node. In particular, transmit‐side correlation degrades link reliability by 4 orders of magnitude for the 8APSK configuration studied at 25 dB.

Enhancing 5G MIMO Core Spectral Efficiency with Higher-Resolution Multi-User MIMO and Multi-Beam Operation

In this article, the evolution of two core multiple-input multiple-output (MIMO) features of the 5G New Radio standardization is described along with their motivations, salient designs, and demonstrated benefits. The first feature enables high spectral efficiency with reduced overhead and complexity in the sub-6 GHz regime by exploiting frequency-domain compression and angle-delay reciprocity inherent in high-resolution precoding codebooks for multi-user MIMO. The second feature, intended for the over-6 GHz regime, facilitates streamlined beam management with greatly reduced signaling overhead and latency as well as improved link reliability in the presence of challenging scenarios such as high mobile speed, large number of analog beams, and unpre-dictable signal blocking. The presented features are the outcome of a five-year work and expected to continue evolving to meet a growing number of challenges from new deployment scenarios.

PSO-CCO_MIMO-SA: A particle swarm optimization based channel capacity optimzation for MIMO system incorporated with smart antenna

With the radio channels physical limits, achieving higher data rate in the multi-channel systems is been a biggest concern. Hence, various spatial domain techniques have been introduced by incorporating array of antenna elements (i.e., smart antenna) in recent past for the channel limit expansion in mobile communication antennas. These smart antennas help to yield the improved array gain or bearm forming gain and hence by power efficiency enhanmaent in the channel and antenna range expansion. The use of smart antenna leads to spatial diversity and minimizes the fading effect and improves link reliability. However, in the process of antenna design, the proper channel modelling is is biggest concern which affect the wireless system performance. The recent works of MIMO design systems have discussed the issues in number of antenna selection which suggests that optimization of MIMO channel capacity is required. Hence, a Particle Swarm Optimization based channel capacity optimzation for MIMO system incorporated with smart antenna is introduced in this paper. From the outcomes it is been found that the proposed PSO based MIMO system achieves better convergenece speed which results in better channel capacity.

User grouping and optimization-based pilot scheduling for mitigating the pilot contamination in massive multi cell MIMO systems

Massive multiple-input multiple-output (massive MIMO) is a promising approach in wireless communication systems for providing improved link reliability and spectral efficiency and it helps several users. The main aim is to solve pilot contamination issue in massive MIMO systems; this research paper utilizes two approaches for reducing the contamination. This paper presents the user grouping approach based on sparse fuzzy C-means clustering (sparse FCM), which groups user parameters based on parameters such as large-scale fading factor, SINR, and user distance. Here, same pilot sequences are assigned to center users in which the impact of pilot contamination is limited, while the algorithm assigns orthogonal pilot sequences to the edge users that suffer severely from pilot contamination. Therefore, the proposed user grouping keeps away from the inappropriate grouping of users, enabling effective grouping even under the worst situations of the channel. Secondly, pilot scheduling is done based on elephant spider monkey optimization (ESMO), which is designed by integrating elephant herding optimization (EHO) into spider monkey optimization (SMO). The performance of pilot scheduling based on grouping-based ESMO is evaluated based on achievable rate and SINR. The proposed method achieves maximal achievable rate of 41.29[Formula: see text]bps/Hz and maximal SINR of 124.31[Formula: see text]dB.

Wireless backhaul networks: centralised vs. distributed scenario

The ultra-dense network (UDN) has been considered as a promising candidate for future 5G network to meet the explosive data demand. To realise UDN, a reliable, gigahertz (GHz) bandwidth and cost-effective backhaul network connecting ultra-dense small-cell BSs (SBS) and macrocell BS (MBS) are pre-requisite. Millimetre wave (mmWave) spectrum can be used to obtain potential gigabit-per-second (Gbps) traffic for wireless backhaul. Moreover, mmWave can easily integrate with massive MIMO for improved link reliability. In this research work, we analyse the wireless backhaul traffic in two different scenarios (e.g., centralised and distributed) using small cell and mmWave communication. Furthermore, the energy efficiency (EE) of wireless backhaul network is compared for different network architecture and frequency bands. Numerical comparison results provide some guidelines for deploying future 5G wireless backhaul network in economical and highly energy-efficient way.

Wireless backhaul networks: centralised vs. distributed scenario

The ultra-dense network (UDN) has been considered as a promising candidate for future 5G network to meet the explosive data demand. To realise UDN, a reliable, gigahertz (GHz) bandwidth and cost-effective backhaul network connecting ultra-dense small-cell BSs (SBS) and macrocell BS (MBS) are pre-requisite. Millimetre wave (mmWave) spectrum can be used to obtain potential gigabit-per-second (Gbps) traffic for wireless backhaul. Moreover, mmWave can easily integrate with massive MIMO for improved link reliability. In this research work, we analyse the wireless backhaul traffic in two different scenarios (e.g., centralised and distributed) using small cell and mmWave communication. Furthermore, the energy efficiency (EE) of wireless backhaul network is compared for different network architecture and frequency bands. Numerical comparison results provide some guidelines for deploying future 5G wireless backhaul network in economical and highly energy-efficient way.

MIMO-OFDM in a Cooperative Frequency Selective Fading Channel: An Analysis

Diversity is an integral technique in the advancement of wireless communications. Multiple-input multiple-output (MIMO) technology continues to occupy a pivotal position in the fourth-generation (4G) standards and beyond. With the advent of full-dimension (FD) MIMO and massive MIMO, new paradigms are being targeted in the disruptive technology. Third-generation partnership project (3GPP) has also emphasized the advanced MIMO technology for achieving its vision of International Mobile Telephony (IMT) 2020. However, implementing multiple antennas can be cumbersome due to limiting factors such as size, cost, and hardware limitations. In such a scenario, where implementing many antennas is not feasible, the benefits of diversity can be reaped by employing cooperative communication. Single antenna mobiles can share their mobile stations giving rise to a virtual MIMO system. In this paper, the authors have analyzed the performance of cooperative mobile stations in a frequency selective channel. Orthogonal frequency division multiplexing (OFDM), known for its robustness against multipath fading, is the modulation technique employed to circumvent the hazardous effects of intersymbol interference (ISI). MIMO is the key technology for achieving capacity advantage and improving link reliability. The blend of these two cutting edge technologies in a cooperative relay system presents encouraging results and inferences. Maximal Ratio Combining (MRC) is employed at the relay and the receiver to maximize the signal-to-noise ratio (SNR). Decode and forward (DF) protocol is used at the relay node owing to its regenerative nature and adaptability. The closed-form expressions for bit error rate (BER) and outage probability (Pout) are derived and compared with the experimental results. The effect of power decay factor is analyzed and simulation is done for 2 × 2, 2 × 3, and 2 × 4 transmit and receive antennas. The number of antennas at the relay is kept as 2. The analytical results agree with the simulation results.

Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems

Massive Multiple-input Multiple-output (MIMO) is an emerging technology for the 5G wireless communication systems which has the potential to provide high spectral efficient and improved link reliability and accommodate large number of users. Aiming at the problem of pilot contamination in massive MIMO systems, this paper proposes two algorithms to mitigate it. The first algorithm is depending on the idea of Path Loss to perform User Grouping (PLUG) which divide the users into the center and edge user groups depending on different levels of pilot contamination. It assigns the same pilot sequences to the center users which slightly suffer from pilot contamination and assign orthogonal pilot sequences to the edge users which severely suffer from pilot contamination. It is assumed that the number of users at the edge of each cell is the same. Therefore, to overcome such limitations of PLUG algorithm, we propose an improved PLUG (IPLUG) algorithm which provides the decision parameters for user grouping and selects the number of central and edge users in each cell in a dynamic manner. Thus, the algorithm prevents the wrong division of users in good channel conditions being considered as an edge user which causes large pilot overhead, and also identifies the users with worst channel conditions and prevents the wrong division of such users from the center user group. The second algorithm for pilot decontamination utilizes the idea of pseudo-random codes in which orthogonal pilot are assigned to different cells. Such codes are deployed to get a transmission pilot by scrambling the user pilot in the cell. Since the pilot contamination is generated because different cells multiplex the same set of orthogonal pilots and the pseudo-random sequences have good cross-correlation characteristics, this paper uses this feature to improve the orthogonality of pilots between different cells. Simulation results show that the proposed algorithms can effectively improve channel estimation performance and achievable rate as compared with other schemes.

Resource Size Control for Reliability Improvement in Cellular-Based V2V Communication

In vehicle-to-vehicle (V2V) communication, link reliability has been regarded as an important performance metric, especially for safety-critical broadcast services. In this paper, we analyze the link reliability of the centralized mode (Mode 3) for long-term evolution (LTE)-based V2V (LTE-V2V) from the PHY/MAC perspectives. Moreover, we derive the statistical distribution of the interference distance and interference to noise ratio (INR) for LTE-V2V. Based on this analytical framework, we propose a resource size control (RSC) method for improving link reliability. The proposed RSC adapts the resource size according to the macroscopic network parameters such as vehicle density, communication range, and message size. Numerical results show that the proposed method improves link reliability compared with the fixed resource size setting in a highway scenario. Moreover, it is observed that larger-sized resources are preferred when the vehicle density decreases, the message size increases, or the communication range decreases.

Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes

Protein interactomes are epitomes of incomplete and noisy networks. Methods for assessing link-reliability using exclusively topology are valuable in network biology, and their investigation facilitates the general understanding of topological mechanisms and models to draw and correct complex network connectivity. Here, I revise and extend the local-community-paradigm (LCP). Initially detected in brain-network topological self-organization and afterward generalized to any complex network, the LCP is a theory to model local-topology-dependent link-growth in complex networks using network automata. Four novel LCP-models are compared versus baseline local-topology-models. It emerges that the reliability of an interaction between two proteins is higher: (i) if their common neighbours are isolated in a complex (local-community) that has low tendency to interact with other external proteins; (ii) if they have a low propensity to link with other proteins external to the local-community. These two rules are mathematically combined in C1*: a proposed mechanistic model that, in fact, outperforms the others. This theoretical study elucidates basic topological rules behind self-organization principia of protein interactomes and offers the conceptual basis to extend this theory to any class of complex networks. The link-reliability improvement, based on the mere topology, can impact many applied domains such as systems biology and network medicine.

A distributed relay selection algorithm for two-hop wireless body area networks

This paper investigates two-hop extension communication in wireless body area networks. Many previous studies have demonstrated that two-hop extended topology outperforms single-hop topology. Although many researchers have proposed using two-hop extension communication to improve link reliability, no one has considered using a relay selection algorithm or provided a suitable solution for wireless body area networks. The design goal of the proposed algorithm is selecting a proper relay node to retransmit failed packets distributively. The proposed algorithm configures the carrier sensing period to choose one relay node promptly without requiring additional interaction. We analyze the link conditions corresponding to various body postures and investigate which factors are proper to determine the carrier sensing period. The empirical results show that the proposed algorithm reduces the expected number of transmissions required to deliver a packet successfully.

Designs of MIMO Amplify-and-Forward Wireless Relaying Networks: Practical Challenges and Solutions Based on MSE Decomposition

Amplify-and-forward (AF) relaying is an efficient way to extend radio range and improve link reliability with a low implementation cost. While the research on the AF relaying in a single-antenna scenario has matured, its application to broadband radio transmission utilizing multiple-input multiple-output techniques still needs more effort to overcome several practical challenges, such as large overheads for channel estimation and vulnerability to the channel estimation errors. This paper reviews some recently proposed linear transceiver designs based on the minimum mean squared error (MSE) criterion and shows that the proposed MSE decomposition and relaxation method can lead to an efficient solution for those challenges. Insightful observations and comprehensive discussions are also made on both analytical and numerical results from practical implementation perspectives.

Analytic Performance Model for State-Based MAC Layer Cooperative Retransmission Protocols

Cooperative retransmission can significantly improve link reliability over lossy and time-varying wireless links. However, comparing retransmission protocols is challenging, and generally requires simplistic assumptions specific to each protocol. In this paper, we develop a general model to evaluate cooperative retransmission protocols with distributed, slot-based contention algorithms. Specifically, we propose to calculate the relay time-out probabilities at a MAC time-slot scale, formulate retransmission outcomes as functions of the time-out probabilities, and derive the probability of a retransmission process for every data frame. We also propose a Markov extension of our model to characterise the dependency between retransmissions of multiple frames. This enables our model to analyse continuous retransmissions of successive frames. Validated by QualNet simulations, our model can analytically predict the probabilities of cooperative retransmissions with an accuracy of $\pm 1$ %. As a result, direct comparisons between cooperative retransmission protocols become tangible, without implementing the full protocol in a state-based simulator.

MmWave massive-MIMO-based wireless backhaul for the 5G ultra-dense network

Ultra-dense network (UDN) has been considered as a promising candidate for future 5G network to meet the explosive data demand. To realize UDN, a reliable, Gigahertz bandwidth, and cost-effective backhaul connecting ultra-dense small-cell base stations (BSs) and macro-cell BS is prerequisite. Millimeter-wave (mmWave) can provide the potential Gbps traffic for wireless backhaul. Moreover, mmWave can be easily integrated with massive MIMO for the improved link reliability. In this article, we discuss the feasibility of mmWave massive MIMO based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. Especially, we propose a digitally-controlled phase-shifter network (DPSN) based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macro-cell BS can simultaneously support multiple small-cell BSs with multiple streams for each smallcell BS, which is essentially different from conventional hybrid precoding/combining schemes typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospect to enable the mmWave massive MIMO based wireless backhaul for 5G UDN are discussed.