Heterogeneous Access Networks Research Articles

Energy Efficient User Association, Resource Allocation and Caching Deployment in Fog Radio Access Networks

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness.

Multi-Channel Opportunistic Access for Heterogeneous Networks Based on Deep Reinforcement Learning

This paper investigates a new medium access control (MAC) protocol for multi-channel heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to as multi-channel deep-reinforcement learning multiple access (MC-DLMA). Specifically, we consider a HetNet where different radio networks adopt different MAC protocols to transmit data packets to a common access point on different wireless channels. Three key challenges for the MC-DLMA node are (i) no environmental knowledge is known in advance; (ii) the channels in HetNets are allocated to nodes using different MAC protocols; (iii) the capacities of different channels may be different. The main goal of MC-DLMA is to find an optimal access policy to transmit on those pre-allocated channels and expedite more efficient spectrum utilization. Due to the complex temporal correlation of spectrum states in HetNets, the traditional DRL technique, e.g., original deep Q-network (DQN) algorithm, is no longer applicable to our problem. In our MC-DLMA design, an advanced class of recurrent neural network, termed as Gated Recurrent Unit (GRU), is embedded into the original DQN technique to aggregate observations over time and reason the underlying temporal feature in multi-channel HetNets. Furthermore, we analytically give the optimal spectrum access patterns and derive the optimal throughputs in various HetNet scenarios. With judicious definitions of the state, action, and reward function in the parlance of the DRL framework, simulation results show that MC-DLMA can (i) find the optimal spectrum access strategies in various HetNets, (ii) outperform the random access policy, the whittle index policy, and the original DQN, (iii) perform cooperative transmission in a fully distributed manner in the presence of multiple agents, and (iv) adapt well to the environmental changes.

Power consumption analysis of access network in 5G mobile communication infrastructures — An analytical quantification model

Energy consumption growth of the fifth-generation (5G) mobile network infrastructure can be significant due to the increased traffic demand for a massive number of end-users with increasing traffic volume, user density, and data rate. The emerging technologies of radio access networks (RAN), e.g., millimeter-wave (mm-wave) communication and large-scale antennas, make a considerable contribution to such an increase in energy consumption. The multiband 2-tier heterogeneous network (HetNet), cloud radio access network (C-RAN), and heterogeneous cloud radio access network (H-CRAN) are considered the prospective RAN architectures of the 5G mobile communication. This paper explores these novel architectures from the energy consumption and network power efficiency perspective considering the varying high volume traffic load, the number of antennas, varying bandwidth, and varying density of low power nodes (LPNs), integrated with mm-wave communication and large-scale multiple antennas. The architectural differences of these networks are highlighted and power consumption analytical models that characterize the energy consumption of radio resource heads (RRHs), base band unit (BBU) pool, fronthaul, macro base station (MBS), and small cell base stations (SCBs) in HetNet, C-RAN, and H-CRAN are developed. The network power efficiency with the consideration of propagation environment and network constraints is investigated to identify the energy-efficient architecture for the 5G mobile network. The simulation results reveal that the power consumption of all these architectures increases in all considered scenarios due to an increase in power consumption of radio frequency components and computation power. Moreover, CRAN is the most energy-efficient RAN architecture due to its cooperative processing and decreased cooling and site support devices and H-CRAN consumes most of the energy compared to other 5G RAN architectures mainly due to a high level of heterogeneity.

Machine Learning-Aided Energy Efficiency Strategy for Multiuser Cooperative Networks

Cooperative communication is widely seen as a promising key technology for improving the energy efficiency of battery-driven multiple mobile terminals (MTs). In this study, we investigate the use of machine learning (ML) in multiuser cooperative access networks. Because MT cooperation and bandwidth allocation are considered two main issues in such networks, we design an ML-aided method to solve the bandwidth issues so that the proposed method can maximize the network’s energy efficiency. Specifically, we use machine learning with artificial neural network (ANN) trained at base station (BS) (a) to decide whether MTs in the heterogeneous access network should cooperatively communicate and (b) to determine the optimal bandwidth allocation for this communication by distributing the trained ANN to all MTs. The computer simulation results show that under the described communication environment in this paper, the proposed method can provide 99.8% correct prediction for MT cooperation and output the optimal bandwidth allocation with at least 88% accuracy, which demonstrates the effectiveness of the proposed method. Besides, the simulations also show that the proposed method can provide about 14%–25% power consumption reduction, which validates the EE performance of the proposed method.

An adaptive algorithm for performance enhancement of long-haul MMW/RoF system for next generation mobile communications

Radio over fibre (RoF) and millimetre wave (MMW) are regarded as the most promising solutions to provide high-capacity, long-distance data transport between base stations and core network in the future heterogeneous radio access networks that support both cloud and edge computing paradigms. In this paper, we propose an adaptive algorithm to optimise and determine a set of important link parameters for a MMW/RoF integrated system based on 10 Gb/s QPSK modulation format and heterodyne coherent detection. We develop analytical models of main components and derive a closed-form expression of system performance such as Q, SNR and BER. The adaptive algorithm is successfully applied to determine important link parameters including laser power, local oscillator power, optical amplifier location and gains so that the overall performance could be enhanced at an acceptable level corresponding to the varied propagation length from 200 km to 300 km. BERs of well below the 10-9.

Heterogeneous Wireless Network Access Selection Algorithm for Power Multi-Service

Heterogeneous Wireless Network Access Selection Algorithm for Power Multi-Service

Smart Real-Time Recommendation of Mobile Services

In this paper, a new vision is presented for highly personalized, customized, and contextualized real-time recommendation of services to mobile users (consumers) by considering the current consumer-, network-, and service context. A smart service recommendation system is elaborated, which builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers’ choices, thereby achieving the best quality of experience (QoE) as perceived by those consumers when utilizing different mobile services. The algorithm-driven recommended mobile services, accessible anytime-anywhere-anyhow through any kind of mobile devices via heterogeneous wireless access networks, range from typical telecommunication services (e.g., outgoing voice calls) to Internet services (e.g., multimedia streaming). These algorithms also may be further enriched by their being adapted and expanded to cover more sophisticated services such as helping the consumer’s health and security needs, an example being the finding (with subsequent dynamic changing, if required) of the most 'healthy' or 'secure' driving/biking/jogging/walking route to follow so as to avoid areas posing particular, consumer-specific, health or safety risk.

A Miniaturized Planar Monopole Antenna Based on a Coupling Structure for Compact Mobile Internet of Things (IoT) and Electric Vehicles (EVs) Device Applications in 5G, LTE, WLAN, WiMAX, Sirius/XM Radio, V2X, and DSRC Wireless Systems

A miniaturized internal antenna with monopole structure is constituted, including three radiating strips of a compact prototype and a back-coupling pad to improve the impedance matching, which achieves a wide bandwidth of 2.972 GHz between the operating frequencies of 2315–5285 MHz, and is introduced and researched. There is an urgent need for a complete frequency-continuous and large bandwidth design in the current wireless communication design to achieve a multimode, multifrequency, physical phenomenon design with large bandwidth and continuous operating frequency. The recommended antenna provides a broadband operation in an electric vehicles (EVs) and Internet of Things (IoT) devices application embedded in the wireless communication standard for 5G, LTE, V2X, WLAN, WiMAX, Sirius/XM Radio, V2X, and DSRC to support the multiband application. This design is embedded side edge of overall placement in the device and is integrated applicable to the trend of heterogeneous wireless multiple access networks in electric vehicle and Internet of Things system devices, which covered the 5G with supporting the band of n7/n38/n40/n53/n77/n78/n79/n90, the 4G with supporting the band of 7/38/40/41/42/43/48/67, the V2X and DSRC for the operating frequencies between 2500 and 5000 MHz, the Sirius/XM Radio for the operating frequencies of 2320–2345 MHz, the ISM band of WiFi and BT covering the band of 2450–2483.5 and 5150–5350 MHz, and the WiMAX also supporting the band of 2300–2690 and 3400–3690 MHz. Moreover, the compact antenna manufactured an FR4 material with the antenna area of 5 × 10 × 0.8 mm3 and the ground area of 33.5 × 10 × 0.8 mm3. The proposed design better benefits a narrow space on the PCB with a low profile and is easy to make with a circuit board design.

Guest Editorial Special Issue on Cybertwin-Driven 6G: Architectures, Methods, and Applications

Internet of Everything (IoE) brings unprecedented challenges regarding scability, mobility, availability, and security to wireless communications. Cybertwin emerges as a promising paradigm for the next-generation mobile network, i.e., 6G. Basically, it serves as the communication anchor of a user at the edge and performs fundamental authentication and network resources control functionalities. Cybertwin is also an indispensable enabler of the cloud native network paradigm and can efficiently support the digital twin and metaverse. With cybertwin, heterogeneous access networks can be easily exploited in a synergic manner, such that advanced applications, such as multiscreen multistream rich media delivery, can be realized with guaranteed QoS. Furthermore, a user’s activities in cyberspace can be recorded naturally which becomes his/her/its digital asset. In the future, cybertwin may become the personal assistant and even an immortal second life of the user.

Edge-Aware Remote Radio Heads Cooperation for Interference Mitigation in Heterogeneous C-RAN

Heterogeneous cloud radio access network (H-CRAN) has been deemed as a promising architecture for the fifth generation wireless networks. In H-CRAN, small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to boost the spectral efficiency. H-CRANs with dense RRHs suffer from severe inter-cell interference due to the random deployment of dense SRRHs, which limits its performance. In this paper, we propose an Edge-Aware RRH-Cooperation (EARC) scheme to mitigate the impact of both the cross-tier and co-tier interference. We partition the users into two groups, Cell Edge Device (CED) and Non-cell Edge Device (NED). These two groups, CED and NED, are mapped into two association modes, dual-association and single-association mode, respectively. In single-association mode, the NED associates with the RRH that provides the maximum received signal. On the other hand, in the dual-association mode, the CED associates with the two strongest RRHs where the two RRHs may belong to the same or different tier(s). Using tools from stochastic geometry, we quantify the performance gains of the proposed EARC scheme in terms of outage probability and ergodic rate. To further show the efficacy of the proposed scheme, we compare it to four schemes developed in the literature.

RETRACTED ARTICLE: Air dust pollution and online music teaching effect based on heterogeneous wireless network

This article first introduces management technology and handover technology in heterogeneous wireless networks and summarizes common network access selection strategies and algorithms. Aiming at the shortcomings of long switching delay in existing fuzzy logic network selection algorithms, a low-complexity fuzzy logic-based heterogeneous wireless network access selection algorithm is proposed. Therefore, this paper uses the WRF-Chem model of the Urban Forest Consortium to carry out a numerical simulation study on the air and dust pollution of mountain city L. Combining the single-layer urban forest model with the WRF-Chem model, this model has an improved simulation function after testing, which can simulate the air pollution process in the L city center and analyze the factors of the urban canopy on the distribution of atmospheric dust pollutants and influences. Finally, we have conducted a comprehensive research on the educational effects of online music education. With the rapid development of Internet communication technology, online education has gradually become popular with the public. As a part of it, online music teaching has also been rapidly developed and promoted. It breaks the time and space limitations of traditional music education and is changing the way and concept of music learners. This article summarizes the background and development of online music teaching, analyzes the advantages and limitations of online music teaching, and briefly introduces future development trends. This paper applies the research results of air dust pollution based on different wireless networks to the research on the effect of online music education, which has successfully promoted the development of online music education.

Joint beamforming and user association with reduced CSI signaling in mobile environments: A Deep Q-learning approach

Heterogeneous Cloud Radio Access Network (H-CRAN) is considered as a cost-efficient network solution to meet 5G data traffic requirements. In this paper, we consider the problem of beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have heterogeneous mobility profiles. Given the rapidly time-varying nature of the mobile wireless environment, it is challenging to offer an optimal beamforming and user association performance during a long-term allocation process without incurring large Channel State Information (CSI) and signaling overheads. For that purpose, we proposed in Ha et al. (2019) an Adaptive Beamforming and User Clustering (ABUC) algorithm which resolves the joint beamforming and user clustering problem when considering CSI cost and imperfectness under user mobility assumptions. In this paper, we design a deep reinforcement-learning framework which enables the proposed ABUC algorithm to select on-the-fly its best feedback scheduling parameters, namely the period and type of CSI feedback, given each user mobility profile. The proposed ABUC-DQL approach can overcome the scalability limitation of the Q-learning approach (Ha et al., 2019) and better handle the problem when formulated using a POMDP (Partially Observable Markov Decision Process) model. The simulation results show that the convergence time is mainly impacted by the number of users in the network, and the online-learning ability of the framework can quickly adapt to the changes of users mobility.

Computing-Aware Base Station Sleeping Mechanism in H-CRAN-Cloud-Edge Networks

In this paper, a power minimization problem using base station sleeping is proposed for heterogeneous cloud radio access networks (H-CRANs) taking into account the computing delay constraints. In the proposed system, which is modeled using M/M/k queues, the edge device coexists with the small base station (SBS) to provide computing capabilities beside the central cloud. In general, the SBS sleeping is governed by the availability of resources provided the macro base station (MBS) which is in charge of accommodating offloaded users from sleeping SBSs. However, switching off lightly loaded SBSs can impose significant burdens on cloud servers. Here, the proposed sleeping scheme allows SBSs serving more computing tasks to remain active in order to fulfill the task completion deadlines requested by mobile users and to keep the cloud response time within a predefined limit. In other words, the proposed scheme aims to save power by undertaking a centralized selection of active and sleeping SBSs taking into account the delay constraints of both cloud and mobile devices. First, we consider a disjoint cloud-edge system, where computing services can be provided by either the cloud or the edge device, and aim to minimize the number of active SBSs. The problem is formulated as a 0-1 knapsack problem with SBS utilization considered as the weight while the ratio of computing tasks to all incoming tasks is considered as the value of that SBS. In this problem, which is solved using dynamic programming, SBSs processing less computing tasks are given higher values; and as a result, higher chance to sleep compared to others. Second, a shared computing system is proposed whereby active SBSs (edge devices) contribute to the total computing capability. Here, an exhaustive search approach is used to achieve the optimal power saving. We also proved that the shared computing system performs better in terms of response time compared to the disjoint system depending on the number of active SBSs.

Energy Efficient Downlink Resource Allocation in Cellular IoT Supported H-CRANs

The cloud computing supported heterogeneous cloud radio access network (H-CRAN) is one of the promising solutions to support cellular IoT devices with the legacy cellular systems. However, the dense deployment of small cells with fractional frequency reuse in orthogonal frequency division multiple access (OFDMA) based H-CRANs increases intra- and inter-cell interference, turning the resource allocation into a more challenging problem. In general, the macro cell users are considered as the legacy users, whereas the cellular IoT devices and small cell users share the macro cell users' resource blocks in an underlaid approach. In this paper, we investigate an underlaid approach of resource allocation for small and macro cell users to improve the energy efficiency (EE) in H-CRANs. The solution approaches are derived with the Dinkelbach, Lagrange multiplier and Alternating Direction Method of Multipliers (ADMM) methods by considering the maximum power, resource block allocation, fronthaul capacity and quality of service (QoS) constraints of macro cell users. A two-step energy efficient underlaid cellular IoT (UC-IoT) supported H-CRAN method is proposed and evaluated with the overlaid cellular IoT (OC-IoT) supported H-CRAN and underlaid H-CRAN without cellular IoT devices. The proposed method is evaluated in terms of energy efficiency and the Jain's fairness index, considering the effect of number of cellular IoT density in each small cell of the H-CRAN. The simulation results demonstrate the effectiveness of the proposed approach compared to earlier approaches.

Heterogeneous Non-Orthogonal Multiple Access With Distributed Alamouti Space-Time Coding

Full frequency reuse with nonorthogonal multiple access (NOMA) in heterogeneous networks (HetNets) can promisingly satisfy the fast-growing wireless transmission requirements, as the data of different users can be simultaneously delivered over the same frequency band. In HetNets, the transmissions from a micro base station (BS) to its cell-edge users often suffer from strong interference from the macro BS, which severely degrades the communication quality. The macro BS and the micro BS can transmit simultaneously to a common far-user using the distributed Alamouti coding technique to improve the communication robustness. Meanwhile, each BS also transmits data to a near-user using the NOMA protocol. The common far-user adopts the maximal ratio combining (MRC) technique to decode its desired data by treating the data of near users as interference. Each near-user decodes the data of far-user using the MRC technique and cancels it to decode its own data by treating the remaining undesired data as interference. Numerical results show that the Alamouti coding based NOMA transmission can greatly improve the throughput of HetNets.

Energy-Efficient Multi-Access Mobile Edge Computing With Secrecy Provisioning

Thanks to the wide deployment of heterogeneous radio access networks (RANs) in the past decades, the emerging paradigm of multi-access mobile edge computing, which allows mobile terminals to simultaneously offload the computation-workloads to several different edge-computing servers via multi-RANs, has provided a promising scheme for enabling the computation-intensive mobile Internet services in future wireless systems. The broadcasting nature of radio transmission, however, may lead to a potential secrecy-outage during the offloading transmission. In this paper, we thus investigate the energy-efficient multi-access mobile edge computing with secrecy provisioning. Specifically, we first investigate the scenario of one wireless device's (WD's) multi-access offloading subject to a malicious node's eavesdropping. By characterizing the WD's secrecy based throughput in its offloading transmission, we formulate a joint optimization of the WD's multi-access computation offloading, secrecy provisioning, and offloading-transmission duration, with the objective of minimizing the WD's total energy consumption, while providing a guaranteed secrecy-outage during offloading and a guaranteed overall-latency in completing the WD's workload. Despite the non-convexity of this joint optimization problem, we exploit its layered structure and propose an efficient algorithm for solving it. Based on the study on the single-WD scenario, we further investigate the scenario of multiple WDs, in which a group of WDs sequentially execute the multi-access computation offloading, while subject to a malicious node's eavesdropping. Taking the coupling effect among different WDs into account, we propose a swapping-heuristic based algorithm (that uses our proposed single-WD algorithm as a subroutine) for finding the ordering of the WDs to execute the multi-access computation offloading, with the objective of minimizing all WDs’ total energy consumption. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms. The results demonstrate that our algorithms can outperform some conventional fixed offloading scheduling scheme and randomized offloading ordering scheme.

RETRACTED ARTICLE: IOV-a novel mechanism for initiating secure vehicle data transmission using PSO with two phase authentication

It has been significantly evolved and it also includes its own vehicular communication. The main communications are said to be V2V, V2R, V21 named as Vehicular Ad-hoc Networks (VANET). VANET is said to be an important part of Intelligent Transport System. In VANET is nothing but it is said to be a special class of Mobile Ad-hoc Network (MANET). It consists of both technical and business-related limitations. VANET still does not consider as a very large scale deployment in the world. At present IoV and its significant extension of the VANET Capabilities are discussed with the vehicles present here by using a type of global network was enabled with the help of Wireless Access Technologies (WAT). In additionally, it involves the internet and also it includes heterogeneous access networks. Here along with IoV, the Internet of Things (IoT) is to be considered with RWP to monitor vehicle distance. Additionally, the vehicles present here need to be driven safely especially in the urban traffic management by using the automobile production. This work highlights the impact of IoT for improving the efficacy of ITS towards IoV.

Hybrid Radio Resource Management for Time-Varying 5G Heterogeneous Wireless Access Network

In this paper, we explore radio resource management for a time-varying 5G heterogeneous wireless access network that includes multi-RATs such as 5G new radio (NR) and long-term evolution (LTE). To cope with the practical challenges of a centralized approach such as signalling overhead and computational complexity, we decomposed the process of radio resource management into three parts, 1) RAT selection, 2) optimal radio resource allocation, and 3) congestion control. RAT selection is performed by each user device with network assistance, whereas the problem of radio resource allocation and congestion control is formulated as a stochastic optimization problem. Maintaining network stability, the average throughput utility is maximized subject to admission control and resource allocation. By using Lyapunov optimization, this utility maximization problem is decomposed into two subproblems. Radio resource allocation policy implemented at the central controller node allocates resources at each time slot using the Lagrange dual method, whereas the process of congestion control is carried out at user end based on throughput adaptation according to its current channel conditions. The theoretical and simulation results evaluate the performance of our proposed approach under the assumption of network stability. Simulation results related to individual users throughput and queue length, and performance comparison of equal power and adaptive power allocation techniques, are presented to depict the effectiveness of our proposed scheme. Furthermore, our proposed RAT selection scheme performs better than the traditional centralized and distributive mechanisms.

Heterogeneous Optical Access Networks: Enabling Low-Latency 5G Services With a Silicon Photonic Smart Edge

In the 5G era, optical fronthaul is a major challenge in meeting growing demand. Edge computation and coordinated multipoint for 5G have stringent requirements for high throughput and low latency, either in single-wavelength or wavelength-division-multiplexing fronthaul. We propose a new silicon photonic solution to deliver 5G services on existing optical access networks with colorless optical network units, such as passive optical networks. The newly added 5G services form a heterogeneous optical access network. Using the existing fiber infrastructure, broadband services coexist with new 5G signals that can densify 5G coverage. The proposed scheme is both wavelength-selective (in the distribution network) and colorless (at the end user site). We use silicon microring modulators to create subcarriers slaved from the broadband service distributed carrier; additional microring modulators generate 5G signals exploiting those subcarriers. We experimentally validated the successful coexistence of 5G signals (various formats) with a broadband signal (various formats).

Adaptive subflow allocation for multipath data transmission in mobile edge networks

Multipath data transmission techniques have been proposed to aggregate the resource of multiple heterogeneous access networks to enhance the robustness and throughput of multi-homed mobile devices. The Multipath TCP (MPTCP) protocol has been standardized by the IETF as an extension of conventional TCP, which enables establishing several subflows over multiple interfaces for simultaneous data transmission. However, current implementation of MPTCP only supports static subflow management strategies, which cannot adapt to the changing to network environment. In this paper, we focus on the issues of dynamic subflow management for MPTCP. We present theoretical analysis to the overall throughput of establishing multiple subflows with MPTCP, based on which we propose an adaptive subflow allocation algorithm to determine the proper number of subflows for each network interface to optimize the performance gain. We implement the proposed algorithm, integrate it into the MPTCP Linux Kernel implementation, and evaluate its performance on a wireless network testbed. Extensive experiments show that the proposed allocation algorithm significantly outperforms the existing subflow management strategies with 57% improvement.