Design of Radio Over Plastic Optical Fibre for Broadband Indoor Access Network

Indoor wireless traffic increases exponentially. Radio over fibre technology has been used to provide broadband services in the access network. Plastic Optical Fibre (POF) has been considered as a promising waveguide to be used as backhaul link for indoor access network. The RoF system uses an improved POF cable as a backhaul. A photonic antenna is developed by integrating a microstrip patch antenna with an opto-electronic converter. A standard inset fed microstrip patch antenna used for the wireless transmission and reception is designed using CST software tool. A RoF communication system based on the developed POF is designed and simulated using Optisystem software tool. The Bit Error Rate (BER) performance of the system in terms of Quality factor is evaluated. A maximum achievable POF cable length of 117 m for an On-Off Keying (OOK) transmission data rate of 1 Gbps with an acceptable Quality factor of 7.0 is obtained. When the developed RoF system was used to transmit 10 Gbps and 50 Gbps data, the achievable cable lengths reduced to 80 m and 63 m, respectively. Keywords— Plastic Optical Fibre, RoF, photonic antenna, and modal dispersion

Wireless Terrestrial Backhaul for 6G Remote Access: Challenges and Low Power Solutions

Despite developments in communication systems over the last few decades, a digital divide exists in the unconnected part of the world. The latter is characterized by large distances to internet access points, underdeveloped infrastructure, sparse populations, and low incomes. This concern of digital divide is raised in the sixth generation’s (6G) initial vision as an extremely important topic. However, it is important to understand affiliated challenges and potential solutions to achieve this vision. Motivated by the recent backhaul link forecasts that expect a dominance of the microwave technology within the backhauling market, this paper studies the potential of a low-power terrestrial microwave backhaul from the sufficient-data-rate and solar powering perspective. Competing technologies (e.g., fiber) may not be energy efficient and commercially viable for global connectivity. Since rural and remote areas may not have grid power, we look at the viability of alternative sustainable sources, in particular solar power, to power the wireless backhaul in 6G. In addition, we also explore services for the operators and users to use the system efficiently. Since the access points are connected to backhaul, we also compare the two prominent solutions based on low-power small-radius cells and a mega-cell that covers a large area and show insights on the power autonomy of the systems. In the end, we propose directions for research and deployment for an inclusive connectivity as a part of future 6G networks.

Latency-aware secure transmission in MEC-assisted HSR communication systems

Mobile edge computing can provide short-range cloud computing capability for the mobile users, which is considered to be a promising technology in 5G communication. The mobile users offload some computing tasks to the edge server through the wireless backhaul link, which can reduce the energy consumption and the time latency. Meanwhile, due to the open characteristics of the wireless channel, the offloading tasks through the backhaul link may face the risk of eavesdropping. Therefore, the secure transmission based on physical layer security for the offloading tasks to the edge server is considered. The optimization problem of minimizing the energy consumption for the vehicular stations (VSs) in mobile edge computing-assisted high-speed railway communication system is studied in this paper. The energy consumption of the mobile users is generated by executing the local computing task and by transmitting the partial offloading task to the edge server. In this paper, a novel joint iterative optimization algorithm is proposed. By jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency. Numerical simulation results verify the effectiveness of the proposed algorithm.

IMPSO and Linear Programming-Based Energy-Efficient Cell Association Algorithm for Backhaul-Constrained Ultra-Dense Small-Cell Networks

This paper studies the energy efficiency optimization problem for coordinated multipoint (CoMP)-enabled and backhaul-constrained ultra-dense small-cell networks (UDNs). Energy efficiency is an eternal topic for future wireless communication networks; however, taking actual bottleneck of the backhaul link and the coordinated network architecture into consideration, it is difficult to find an effective way to improve the energy efficiency of the network. Aiming at this problem, we propose to combine cell association, subchannel allocation, backhaul resource allocation, and sleep/on of the cells together to develop an optimization algorithm for energy efficiency in UDN and then solve the formulated energy efficiency optimization problem by means of improved modified particle swarm optimization (IMPSO) and linear programming in mathematics. Simulation results indicate that nearly 13 % energy cost saving and 21 % energy efficiency improvement can be obtained by combining IMPSO with linear programming, and the backhaul link data rate can be improved by 30 % as the number of small cells increases. From the results, it can be found that by combining IMPSO with linear programming, the proposed algorithm can improve the network energy efficiency effectively at the expense of limited complexity.

Efficient Gradient Updating Strategies with Adaptive Power Allocation for Federated Learning over Wireless Backhaul

In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shared for all the distributed clients and it is trained through the federated learning over wireless backhaul connected to the main server. However, during the training phase, local gradients need to be transferred from multiple clients to the server over wireless backhaul link and can be distorted due to wireless channel fading. To overcome it, an efficient gradient updating method is proposed, in which the gradients are combined such that the effective SNR is maximized at the server. In addition, when the backhaul links for all clients have small channel gain simultaneously, the server may have severely distorted gradient vectors. Accordingly, we also propose a binary gradient updating strategy based on thresholding in which the round associated with all channels having small channel gains is excluded from federated learning. Because each client has limited transmission power, it is effective to allocate more power on the channel slots carrying specific important information, rather than allocating power equally to all channel resources (equivalently, slots). Accordingly, we also propose an adaptive power allocation method, in which each client allocates its transmit power proportionally to the magnitude of the gradient information. This is because, when training a deep learning model, the gradient elements with large values imply the large change of weight to decrease the loss function.

Matching-based cache placement decision for 5G network caching

In this thesis, we present a cache placement scheme to deal with backhaul link constraint in Small Cell Network for 5G wireless network. We formulated the cache placement problem as a graph matching problem and presented an optimal file-helper matching algorithm. We defined stability criterion for the matching and found that our matching solution is stable in the sense that every helper finds at least one file to cache given that no file exceed minimum cache size. We achieved a unique placement of a file within a cluster of helpers to increase the number of files cached within a cluster. Further, our experimental evaluation demonstrates that our algorithm increases local and neighbor hit ratios as compared to a random placement, which in turn significantly decreases the traffic that goes over the backhaul bottleneck link.

Matching-based cache placement decision for 5G network caching

In this thesis, we present a cache placement scheme to deal with backhaul link constraint in Small Cell Network for 5G wireless network. We formulated the cache placement problem as a graph matching problem and presented an optimal file-helper matching algorithm. We defined stability criterion for the matching and found that our matching solution is stable in the sense that every helper finds at least one file to cache given that no file exceed minimum cache size. We achieved a unique placement of a file within a cluster of helpers to increase the number of files cached within a cluster. Further, our experimental evaluation demonstrates that our algorithm increases local and neighbor hit ratios as compared to a random placement, which in turn significantly decreases the traffic that goes over the backhaul bottleneck link.

MSE minimized joint transmission in coordinated multipoint systems with sparse feedback and constrained backhaul requirements

In a joint transmission coordinated multipoint (JT-CoMP) system, a shared spectrum is utilized by all neighbor cells. In the downlink, a group of base stations (BSs) coordinately transmit the users’ data to avoid serious interference at the users in the boundary of the cells, thus substantially improving area fairness. However, this comes at the cost of high feedback and backhaul load; In a frequency division duplex system, all users at the cell boundaries have to collect and send feedback of the downlink channel state information (CSI). In centralized JT-CoMP, although with capabilities for perfect coordination, a central coordination node have to send the computed precoding weights and corresponding data to all cells which can overwhelm the backhaul resources. In this paper, we design a JT-CoMP scheme, by which the sum of the mean square error (MSE) at the boundary users is minimized, while feedback and backhaul loads are constrained and the load is balanced between BSs. Our design is based on the singular value decomposition of CSI matrix and optimization of a binary link selection matrix to provide sparse feedback—constrained backhaul link. For comparison, we adopt the previously presented schemes for feedback and backhaul reduction in the physical layer. Extensive numerical evaluations show that the proposed scheme can reduce the MSE with at least $$25\%$$ 25 % , compared to the adopted and existing schemes.