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Your search keyword '"integrated access and backhaul"' showing total 4 results
Start Over Descriptor "integrated access and backhaul" Publisher institute of electrical and electronics engineers (ieee)
4 results on '"integrated access and backhaul"'

1. Full-Duplex Wireless for 6G : Progress Brings New Opportunities and Challenges

Author

Smida, Besma, Sabharwal, Ashutosh, Fodor, Gabor, Alexandropoulos, George C., Suraweera, Himal A., Chae, Chan-Byoung, Smida, Besma, Sabharwal, Ashutosh, Fodor, Gabor, Alexandropoulos, George C., Suraweera, Himal A., and Chae, Chan-Byoung

Abstract

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks., QC 20230911

Published
2023
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2. Access and Radio Resource Management for IAB Networks Using Deep Reinforcement Learning

Author

Malcolm M. Sande, Mduduzi C. Hlophe, and Bodhaswar T. Maharaj

Subjects
deep reinforcement learning, nearest neighbor, General Computer Science, Artificial neural network, Computer science, business.industry, Congestion control, General Engineering, resource allocation, millimeter wave, TK1-9971, Network congestion, integrated access and backhaul, Reinforcement learning, Resource allocation, General Materials Science, Resource management, Electrical engineering. Electronics. Nuclear engineering, Markov decision process, Radio resource management, business, Power control, Computer network
Abstract

Congestion in dense traffic networks is a prominent obstacle towards realizing the performance requirements of 5G new radio. Since traditional adaptive traffic signal control cannot resolve this type of congestion, realizing context in the network and adapting resource allocation based on real-time parameters is an attractive approach. This article proposes a radio resource management solution for congestion avoidance on the access side of an integrated access and backhaul (IAB) network using deep reinforcement learning (DRL). The objective of this article is to obtain an optimal policy under which the transmission throughput of all UEs is maximized under the dictates of environmental pressures such as traffic load and transmission power. Here, the resource management problem was converted into a constrained problem using Markov decision processes and dynamic power management, where a deep neural network was trained for optimal power allocation. By initializing a power control parameter, $\theta _{t}$ , with zero-mean normal distribution, the DRL algorithm adopts a learning policy that aims to achieve logical allocation of resources by placing more emphasis on congestion control and user satisfaction. The performance of the proposed DRL algorithm was evaluated using two learning schemes, i.e., individual learning and nearest neighbor cooperative learning, and this was compared with the performance of a baseline algorithm. The simulation results indicate that the proposed algorithms give better overall performance when compared to the baseline algorithm. From the simulation results, there is a subtle, but critically important insight that brings into focus the fundamental connection between learning rate and the two proposed algorithms. The nearest neighbor cooperative learning algorithm is suitable for IAB networks because its throughput has a good correlation with the congestion rate.

Published
2021

3. Integrated access and backhaul a New Type of Wireless Backhaul in 5G

Author

Henrik Ronkainen, Anders Ericsson, Christer Östberg, and Jonas Edstam

Subjects
Flexibility (engineering), Computer science, business.industry, Wireless backhaul, IAB, 010401 analytical chemistry, Testbed, 020206 networking & telecommunications, 02 engineering and technology, 01 natural sciences, lcsh:P87-96, 0104 chemical sciences, lcsh:Communication. Mass media, Protocol stack, Backhaul (telecommunications), integrated access and backhaul, 0202 electrical engineering, electronic engineering, information engineering, wireless backhaul, Electrical and Electronic Engineering, Telecommunications, business, 5G, mm wave communication
Abstract

In this paper, we study the concept and the potentials of integrated access and backhaul (IAB) as one of the main innovative aspects of 5G networks. We study IAB networks from different perspectives. Particularly, we summarize the recent Rel-16 discussions on IAB, and highlight the main IAB-specific agreements on different protocol layers. Moreover, we present simulations and proof-of-concept testbed results to evaluate the performance of IAB networks in both urban and suburban areas. As show, for both suburban and urban scenarios, IAB is an attractive complement to fiber, with high flexibility and low time-to-market.

Published
2020

4. Deep Reinforcement Learning-Based Spectrum Allocation in Integrated Access and Backhaul Networks

Author

Lei, Wanlu, Ye, Yu, Xiao, Ming, Lei, Wanlu, Ye, Yu, and Xiao, Ming

Abstract

We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deployment and dynamic environment. The available spectrum is divided into several orthogonal sub-channels, and the donor base station (DBS) and all IAB nodes have the same spectrum resource for allocation, where a DBS utilizes those sub-channels for access links of associated user equipment (UE) as well as for backhaul links of associated IAB nodes, and an IAB node can utilize all for its associated UEs. This is one of key features in which 5G differs from traditional settings where the backhaul networks are designed independently from the access networks. With the goal of maximizing the sum log-rate of all UE groups, we formulate the spectrum allocation problem into a mix-integer and non-linear programming. However, it is intractable to find an optimal solution especially when the IAB network is large and time-varying. To tackle this problem, we propose to use the latest DRL method by integrating an actor-critic spectrum allocation (ACSA) scheme and deep neural network (DNN) to achieve real-time spectrum allocation in different scenarios. The proposed methods are evaluated through numerical simulations and show promising results compared with some baseline allocation policies., QC 20201007

Published
2020
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