Link Selection Algorithm Research Articles

An Adjustable Wireless Backhaul Link Selection Algorithm for LEO-UAV-Sensor-Based Internet of Remote Things Network.

Internet of Remote Things (IoRT) networks utilize the backhaul links between unmanned aerial vehicles (UAVs) and low-earth-orbit (LEO) satellites to transfer the massive data collected by sensors. However, the backhaul links change rapidly due to the fast movement of both the UAVs and the satellites, which is different from conventional wireless networks. Additionally, due to the various requirements of IoRT multiservices, the system performance should be comprehensively considered. Thus, an adjustable wireless backhaul link selection algorithm for a LEO-UAV-sensor-based IoRT network is proposed. Firstly, an optimization model for backhaul link selection is proposed. This model uses Q, which integrates the remaining service time and capacity as the objective function. Then, based on the snapshot method, the dynamic topology is converted into the static topology and a heuristic optimization algorithm is proposed to solve the backhaul link selection problem. Finally, the proposed algorithm is compared with two traditional algorithms, i.e., maximum service time and maximum capacity algorithms. Numerical simulation results show that the proposed model can achieve better system performance, and the overload of the satellites is more balanced. The algorithm can obtain a trade-off between remaining service time and capacity by dynamically adjusting model parameters. Thus, the adjustable backhaul link selection algorithm can apply to multiservice IoRT scenarios.

Retracted: Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Retracted: Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Communication and Energy Efficient Decentralized Learning Over D2D Networks

Device-to-device (D2D)-assisted decentralized learning has been proposed for mobile devices to collaboratively train artificial intelligence networks without the centralized parameter server. However, a densely connected network will cause large learning latency and energy consumption due to the limited computation and communication resources. In addition, link selection and aggregation weight have a significant impact on the learning performance. To cope with these challenges, we propose a joint computing power adjustment, wireless resource allocation, link selection, and aggregation weight adaptation mechanism to improve both communication and energy efficiencies. Specifically, the learning performances including the convergence rate, per-iteration learning latency, and per-iteration energy consumption are first analyzed. Then, an optimization problem is formulated to minimize the total learning cost, which is defined as the weighted sum of total learning latency and energy consumption. Given a network topology, the computing power and wireless resource allocation are optimized by the alternating optimization algorithm. Moreover, the optimal aggregation weight is obtained by semidefinite programming. With respect to link selection, we propose a tabu search based meta-heuristic algorithm to approximately achieve feasible solutions with a low computational complexity. Finally, extensive experiments demonstrate that the proposed link selection algorithm can significantly reduce the learning cost under the given learning accuracy requirement.

Link-State Aware Hybrid Routing in the Terrestrial–Satellite Integrated Network

In this paper, we study data transmission in the Terrestrial-Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial-satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space-time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space-time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial-satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn-Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.

Enhanced Multilink Single-Radio Operation for the Next-Generation IEEE 802.11 BE Wi-Fi Systems

For the next-generation Wi-Fi systems, the Enhanced Multilink Single-Radio (EMLSR) operation has become a promising feature to improve the Wi-Fi system performance. The EMLSR enables dynamic switching among multiple bands with low-cost implementation and efficient power consumption. However, with realistic channelization of multiple links with 320 MHz bandwidth on 6 GHz and 160 MHz bandwidth on 5 GHz, the performance of EMLSR is jeopardized due to the crowded 5 GHz band with existing Wi-Fi systems. In addition, the unbalanced bandwidth configuration between an access point (AP) and stations (STAs) multilink devices (MLDs) may result in wasted secondary channels when STAs supporting smaller bandwidth stay on the primary channel only. We proposed the further enhanced EMLSR with a new MAC protocol and link selection mechanism, where a primary link (PL) is selected between AP and STA with the unbalanced Link/Band and one or multiple secondary links (SLs) with balanced Link/Band. The STA tunes the main radio on the PL and the scan radio to one of the SLs. The PL will be more prioritized than the SL based on the link selection algorithm to maximize the channel utilization. Compared with legacy EMLSR, the enhanced EMLSR can improve the utilization of the secondary channel. Even under heavy OBSS load on SL or both PL and SL, the further enhanced EMLSR can achieve 50% to 70% throughput gain.

Dealing With Link Blockage in mmWave Networks: A Combination of D2D Relaying, Multi-Beam Reflection, and Handover

In this paper, we consider adaptive user equipments (UE) link selection and user association in millimeter-wave (mmWave) networks. We formulate a joint optimization of link selection, resource allocation, and user association, aiming to maximize the sum logarithmic rate of all UEs. The formulated problem is solved by decomposing it into two levels of subproblems. The lower-level subproblem is link selection and resource allocation with a given user association, which is solved by a three-stage process. In the first stage, we establish the D2D relaying architecture by assuming that all UEs are served via D2D relaying. Based on the relaying architecture, we derive the optimal resource allocation in the second stage. Finally, an adaptive link selection algorithm is proposed in the third stage to determine the set of UEs that switch from D2D relaying to multi-beam reflection. The high-level subproblem is user association, for which we solve it with a dual decomposition-based approach. Simulation results indicate that compared to benchmark schemes, the average data rate achieved by the proposed scheme is significantly higher than the benchmark schemes and is close to an upper bound. Besides, the proposed scheme achieves a good tradeoff between system performance and fairness.

Evolutionary Overlapping Coalitional Game-Based Link Selection for Distributed Cooperative Localization in Mobile Networks

With the increasing application of Internet of Things (IoT), the localization of IoT devices has been widely used. The distributed cooperative localization is expected to be applied in a large-scale dynamic network, such as IoT. It is located through the exchange of information among multiple nodes. For a large amount of battery-based users, the high-computational complexity and heavy communication overhead will lead to huge energy consumption. In this paper, we propose a link selection algorithm based on the evolutionary overlapping coalitional (EOC) game to mitigate the energy consumption for distributed cooperative localization in the dynamic network. The equivalent Fisher information matrix (EFIM) and the Cramér–Rao lower bound (CRLB) are employed to keep location accuracy. Numerical results verify that the distributed cooperative localization based on the EOC game achieves lower energy consumption while keeping localization accuracy in the dynamic networks.

Wireless Secret Key Generation for Distributed Antenna Systems: A Joint Space-Time-Frequency Perspective

Wireless secret key generation has emerged as a promising technique for Internet-of-Things (IoT) systems to establish shared encryption keys between the server and legitimate mobile user. This article focuses on the use of multidomain joint information to achieve a high key generation rate (KGR) and the implementation of a reliable, low-complexity secret key generation mechanism for distributed antenna systems (DAS) with orthogonal-frequency division multiplexing (OFDM). We present a space-time-frequency channel state information (CSI)-based key generation scheme based on a two-step approach of adaptive link selection and stepwise decorrelation algorithms. The performance is evaluated in terms of KGR, key disagreement rate (KDR), randomness, and computational complexity by using both a standardized channel model and real-world measurements. Numerical results show that our proposed low-complexity algorithms effectively utilize the space-time-frequency CSI to multiply the KGR in both indoor and outdoor environments. Through adaptive link selection in DAS, the KDR is maintained within a correctable range, thereby ensuring the validity of generated keys in dynamic environments. Further applying stepwise decorrelation reduces the computational complexity by more than half while satisfying all eight key generation randomness tests in the NIST test suite.

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework.

With the development of the Internet of Things (IoT), Low Data Rate-Personal Area Networks (LR-WPAN) have been deployed for different applications. Now comes the need to integrate these networks in search of greater connectivity, performances, and geographic coverage. This integration is facilitated by the recent deployment of low power wide area networks (LPWAN) in the licensed bands, especially narrowband IoT (NB-IoT) and long-term evolution for machine-type communications (LTE-M), which are standardized technologies that will continue evolving as part of the fifth generation (5G) specifications. This paper proposes a design methodology for combined networks using LR-WPAN and LPWAN technologies. These networks are combined at the device level using a cluster-tree topology. An example is shown here, where an existing IEEE 802.15.4 network is combined with NB-IoT. To this end, new dual nodes are incorporated, acting as cluster heads. The paper discusses the different aspects of formation and operation of the combined network. A dynamic link selection (DLS) algorithm is also proposed, based on which cluster headers dynamically determine the preferred link, depending on link quality and type of traffic. Extensive simulations show that the DLS algorithm significantly increases battery life on dual nodes, which are the nodes with the highest power demands.

Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Based on the millimetre-wave time-domain channel detector, this paper conducts channel measurement and modelling for different scenarios and antenna types in typical frequency bands and thus carries out a study on the propagation characteristics of millimetre-wave channels in typical frequency bands and scenarios for 5G. The time-varying characteristics and modelling methods of millimetre-wave clusters are studied, and the time-varying channel simulation strategy and linear regression fitting method are used to establish the time-varying generation and extinction model of clusters by combining the measured data of 26 GHz large-scale antenna channels. The algorithm is based on the correlation dictionary and achieves the efficient and optimal selection of large-scale antenna arrays in the millimetre-wave band. A low-complexity multiuser hybrid precoding design scheme is proposed based on the wideband millimetre-wave large-scale Multiple-Input Multiple-Output (MIMO) system. The scheme aims to maximize the system spectral efficiency and introduces the net spectral efficiency as a bridge to decouple the analogy precoding design from the digital precoding design, thus significantly reducing the computational complexity. In the analogy precoding stage, a Hungarian algorithm-based beam assignment method is proposed to avoid beam conflicts and maximize the net spectral efficiency of the system; in the digital precoding stage, the equivalent channel matrix of each subcarrier is diagonalized to eliminate multiuser interference. Finally, the effectiveness of the scheme is verified by simulation. In this paper, we focus on the radio frequency (RF) link and user selection algorithm of 5G mobile communication system. Through the study of these two aspects, the RF link of our communication system has high stability and reliability, and through the study of the algorithm used for selection, our results also have a strong practical value.

Optimization of the Enterprise Human Resource Management Information System Based on the Internet of Things

In this paper, the optimization of the enterprise HR information system is studied based on IoT first-off technology, the system demand phase is analysed, and the edge control system is designed and built. The hardware and software system and edge node management platform are implemented first, and then the communication scenarios between the edge layer of the system and the sensing layer, the edge layer, and the cloud layer are analysed, and the business type-driven link selection algorithm and the northbound multilink switching algorithm are designed and implemented, respectively, to guarantee the communication reliability between different layers of the system. Based on the implementation of the above functions, the edge control system can meet the intelligence, expandability, and security requirements of IoT applications. An in-depth investigation and research are launched mainly on the enterprise demand to determine the functional requirements and performance requirements of the enterprise and to achieve the basic logical structure; in the system design phase, the system architecture and other aspects of the design are realized. According to the conditions of the system function structure, a number of system module functions are designed in detail. The system is composed of the following modules, namely, personnel change management, organization management, and salary and benefits management. The system consists of the following modules, namely, personnel change management, organization management, compensation and benefits management, and personnel information management. The system modules run through the process of human resource management; in the system implementation stage, the system coding and page operation are realized based on the development tools and software development techniques. The system finally achieves the system design objectives and is put on a trial operation to meet its actual business requirements.

Joint Channel and Link Selection in Formation-Keeping UAV Networks: A Two-Way Consensus Game

This paper is the first to investigate both communication and control in traffic channel (TCH) and control channel (CCH) respectively when considering leader-follower formation keeping in UAV communication networks. In this paper, we analyze the relationship between the mutual interference and information exchange cost, and then formulate the joint channel and link selection problem as a two-way consensus game between CCH and TCH. To characterize the two-way choice of link selection, we creatively propose the generalized two-way consensus equilibrium (GTCE) to capture the stable state. Then, we prove that the formulated game has at least one pure-strategy GTCE which can maximize the UAV communication network utility. A distributed better reply based joint channel and link selection (BRJCLS) algorithm as well as two-dimensional minimum spanning tree (MST) based initialization (TMSTI) algorithm is proposed to achieve the GTCE. Simulation results are presented to show the convergence and effectiveness of the formulated two-way consensus game and proposed algorithms.

Wireless Resource Management and Resilience Optimization of the M2M‐Oriented Mobile Communication System

Machine‐to‐machine (M2M) communication technology is an emerging technology that can connect smart wireless devices. The most obvious feature of M2M is that the communication between devices does not require human intervention. Therefore, ensuring the low‐energy consumption of M2M devices is a necessary condition for prolonging the survival time of their devices. This paper first considers the coexistence of M2M and H2H scenarios. Aimed at the energy efficiency of M2M equipment and the channel capacity of H2H equipment, a multiobjective problem is constructed for joint spectrum and power resource management, and a weighted Chebyshev algorithm is proposed to solve this problem. Secondly, in view of the additional interference problems caused by the introduction of M2M communication, the intercell cooperative link selection algorithm is used to optimize its resilience. The effectiveness of the algorithm is proven by simulation results.

Cooperative and efficient content caching and distribution mechanism in 5G network

This paper considers encoding caching and collaborative distribution issues jointly, and proposes a collaborative and efficient content caching and distribution mechanism; and based on the proposed scheme, an energy consumption model is established from the perspective of content caching and content distribution to optimize Energy efficiency of content caching and content distribution. In addition, this paper proposes a cache placement algorithm based on heuristic greedy algorithm to solve this energy efficiency optimization problem by optimizing cache placement. Finally, the simulation results show the performance of the scheme. This paper proposes a content clustering distribution scheme for the problems of random user distribution and limited cache. This solution uses the user’s media cloud and communication technology to complete the distribution of video content. In addition, the use of an efficient adaptive media cloud clustering mechanism and a minimized hops algorithm has enabled the reasonable deployment of 5G network caches based on the popularity of video content and user distribution. The simulation results show that the system content request response time is shortened by 50%, which effectively reduce the impact of content popularity changes on system performance. This paper analyses the important performance indicators such as cache hit rate, optimal communication radius, and maximum link access in 5G networks for multi-layer popular multimedia content distribution in response to the heterogeneity and transmission interference problems for 5G networks in 5G communications. Under the premise of noise ratio and signal-to-interference ratio threshold, related service link selection and scheduling algorithms is further proposed obtaining simulation results that approximate theoretical analysis, which effectively reduces the number of interference by 40% and improves the system throughput by 10%.

A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications

Non-orthogonal multiple access (NOMA) has become a promising technology for 5G. With the support of effective resource allocation algorithms, it can improve the spectrum resource utilization and system throughput. In this article, a new resource allocation algorithm in the NOMA-enhanced cellular network with device-to-device (D2D) communications is proposed, in which we use two new searching methods and an optimal link selection scheme to maximize the system throughput and limit the interferences of the NOMA-based cellular network. In the proposed joint user scheduling, tree-based search power allocation and link selection algorithm, we simplify the solving process of previous methods and set up the optimization function, which does not need to be derivable. With successive interference cancellation (SIC) technology, we give conditions for the D2D devices accessing into the network. We also propose a suboptimal scheme to schedule cellular users and D2D devices into multiple subchannels, which reduces the complexity of the exhaustive search method. Through consistent tree-based searching for the power allocation coefficients, we can get the maximum arithmetic average of the system sum rate. Meanwhile, for the existence of the part of interferences from larger power users which can be canceled by the SIC in NOMA systems, the search options are decreased for increasing the search rate of the power allocation algorithm. Moreover, we propose a distance-aware link selection scheme to guarantee the quality of communications. In summary, the proposed algorithm can improve the system throughput, has a low complexity cost and potentially increases spectral utilization. Numerical results demonstrate that the proposed algorithm achieves a higher data transmission rate than some of the traditional methods and we also investigate the convergence and the computational complexity cost of the joint algorithm.

Link Selection in Buffer-Aided Cooperative Networks for Green IoT

In this paper, the link selection algorithm in buffer-aided cooperative networks with multiple co-channel interferers for green internet of things (IoT) is investigated. In the considered system, the relay node selects the better link from the first and the second hop with the help of buffers. We evaluate the effects of the system parameters by deriving the analytical analysis as well as the asymptotic results on the exact expressions of the outage probability. The impacts of system parameters, such as the predefined target rate, the priority-outage tradeoff parameter, the buffer length and the number of interferers are evaluated in different setup scenarios. Considerable performance gains can be obtained compared with the classical decode-and-forward relaying networks. Simulation results are provided to verify the theoretical analysis.

Generalized Buffer-State-Based Relay Selection in Cooperative Cognitive Radio Networks

In this paper, we propose a novel buffer-state-based relaying selection scheme in the context of cooperative cognitive radio networks (CRNs), supporting the primary and secondary networks. In the proposed scheme, both the effects of inter-network interference and fading are successfully suppressed by introducing a flexible link selection algorithm in the secondary network. More specifically, by relying on the broadcast nature of wireless communication channels between a source node and relay nodes in the secondary network, the associated source packet is shared among multiple relay nodes. This allows us to benefit from the additional degree of freedom. Furthermore, we consider the priority for link selection based on the buffer state of each relay node in the secondary network. This contributes to the avoidance of detrimental empty and full buffer states. Moreover, analytical bounds of the outage probability and the average packet delay are derived for the proposed scheme based on the Markov chain model, in order to verify the numerical results. The overhead required for a central coordinator of the secondary network to monitor channel state information and buffer states are also investigated. Our numerical results demonstrate that the proposed scheme achieves better outage and packet-delay performance than the conventional max-ratio-based scheme in the buffer-aided CRN.

Dynamic Link Selection for Interference Mitigation in D2D Multicast Content Sharing Networks

In this letter, we study the link selection problem for interference mitigation under dynamic channel conditions in D2D multicast content sharing networks. We firstly transform the problem into a stochastic game, in which the utility of each player (i.e., cluster head) is designed as the maximum expected weighted interference suffered by the receivers in the cluster. Then, we propose a simple and uncoupled link selection algorithm based on stochastic learning algorithm to obtain the Nash Equilibrium points. Importantly, the proposed algorithm only requires little intra-cluster information feedback. Theoretical analyses and numerical results validate the effectiveness of our proposed algorithm under dynamic channel conditions.

A Novel Low-Latency V2V Resource Allocation Scheme Based on Cellular V2X Communications

In vehicular ad hoc networks (VANETs), cellular vehicle-to-everything (C-V2X) is an emerging technology for communications between vehicle-to-infrastructure, vehicle-to-pedestrian, and vehicle-to-network which improves traffic efficiency, road safety, and the availability of infotainment services. Herein, a novel V2V-enabled resource allocation scheme based on C-V2X technology is proposed to improve the reliability and latency of VANETs. The key idea is that V2V communications based on cellular-V2X technology among vehicles remove the contention latency and can assist for longer distance communications. Particularly, we propose a hybrid architecture, where the V2V links are controlled by the cellular eNodeB in the overlay scheme. In this scheme, every vehicle periodically checks its packet lifetime and requests the cellular eNodeB to determine V2V links. The optimum resource allocation problem at the cellular eNodeB is to choose optimum receiver vehicles to determine V2V links and allocate suitable channels to minimize the total latency. This problem is equivalent to the maximum weighted independent set problem (MWIS-AW) with associated weights, which is NP-hard. In order to compute the weights, an analytical approach is developed to model the expected latency and packet delivery ratio. Moreover, a greedy cellular-based V2V link selection algorithm is proposed to solve MWIS-AW problem and develop a theoretical performance lower bound. Simulation results show that the proposed scheme significantly outperforms the existing schemes in terms of latency, throughput, and packet delivery ratio.

Optimized Link Distribution Schemes for Ultrareliable and Low-Latent Communications in Multilayer Airborne Networks

Ultrareliable and low-latency communications (uRLLC) is one of the most significant requirements for future wireless networks. The conventional terrestrial base stations cannot always provide the required uRLLC for emerging applications and scenarios, e.g., Tactile Internet services or when a large number of users get connected during an event. Therefore, multilayer airborne networks with low/medium/high altitude platforms can be deployed as an effective solution to offer capacity and coverage along with required latency and reliability for wireless networks. In this article, we propose three layers of the airborne network to support the uRLLC requirement in wireless networks. Optimized link selection schemes have been provided based on polychromatic sets (PSets) theory to focus on the uRLLC. With the optimized link selection algorithm, multiple properties of the airborne platforms are exploited, and the links are selected based on the multiconstrained requirements to support the desired performance of the airborne network. Moreover, two links distribution schemes have been proposed as distributed greedy scheme and centralized greedy scheme to demonstrate the deployment of the proposed airborne network. Numerical results show that both PSets-based links distribution schemes outperform the general distribution schemes on average latency and overall reliability also known as the unassociated ratio, which strongly supports the uRLLC in considered airborne networks.