In this paper, we introduce and study the potentials and challenges of
integrated access and backhaul (IAB) as one of the promising techniques for
evolving 5G networks. We study IAB networks from different perspectives. We
summarize the recent Rel-16 as well as the upcoming Rel-17 3GPP discussions on
IAB, and highlight the main IAB-specific agreements on different protocol
layers. Also, concentrating on millimeter wave-based communications, we
evaluate the performance of IAB networks in both dense and suburban areas.
Using a finite stochastic geometry model, with random distributions of IAB
nodes as well as user equipments (UEs) in a finite region, we study the service
coverage rate defined as the probability of the event that the UEs' minimum
rate requirements are satisfied. We present comparisons between IAB and hybrid
IAB/fiber-backhauled networks where a part or all of the small base stations
are fiber-connected. Finally, we study the robustness of IAB networks to
weather and various deployment conditions and verify their effects, such as
blockage, tree foliage, rain as well as antenna height/gain on the coverage
rate of IAB setups, as the key differences between the fiber-connected and IAB
networks. As we show, IAB is an attractive approach to enable the network
densification required by 5G and beyond.

本文介绍并研究了作为一种有前途的 5G 网络技术之一的集成接入和回传（IAB）的潜力和挑战。我们从不同的角度研究了 IAB 网络，并总结了关于不同协议层面的 IAB 特定协议的主要协议。同时，我们评估了 IAB 网络在密集区域和郊区中的性能，并用随机分布的 IAB 节点和用户设备在一个有限区域内的有限随机几何模型研究了服务覆盖率。最后，我们还研究了 IAB 网络对天气和各种部署条件的鲁棒性和验证了其具有的关键区别，如堵塞、树冠、雨以及天线高度 / 增益等等，以实现 5G 及以上所需的网络密集化。

关于集成接入与返回链路网络的当前状况和潜力

On Integrated Access and Backhaul Networks: Current Status and  Potentials

Millimeter Wave (MmWave) communication is one of the key technology of the
fifth generation (5G) wireless systems to achieve the expected 1000x data rate.
With large bandwidth at mmWave band, the link capacity between users and base
stations (BS) can be much higher compared to sub-6GHz wireless systems.
Meanwhile, due to the high cost of infrastructure upgrade, it would be
difficult for operators to drastically enhance the capacity of backhaul links
between mmWave BSs and the core network. As a result, the data rate provided by
backhaul may not be sufficient to support all mmWave links, the backhaul
connection becomes the new bottleneck that limits the system performance. On
the other hand, as mmWave channels are subject to random blockage, the data
rates of mmWave users significantly vary over time. With limited backhaul
capacity and highly dynamic data rates of users, how to allocate backhaul
resource to each user remains a challenge for mmWave systems. In this article,
we present a deep reinforcement learning (DRL) approach to address this
challenge. By learning the blockage pattern, the system dynamics can be
captured and predicted, resulting in efficient utilization of backhaul
resource. We begin with a discussion on DRL and its application in wireless
systems. We then investigate the problem backhaul resource allocation and
present the DRL based solution. Finally, we discuss open problems for future
research and conclude this article.

本文研究基于深度强化学习技术的毫米波移动通信网络后向资源分配问题，准确预测信道状况，提高系统性能。

毫米波系统中有限回传链路容量的处理：一种深度强化学习方法

Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A  Deep Reinforcement Learning Approach

The design of distributed mechanisms for interference management is one of
the key challenges in emerging wireless small cell networks whose backhaul is
capacity limited and heterogeneous (wired, wireless and a mix thereof). In this
paper, a novel, backhaul-aware approach to interference management in wireless
small cell networks is proposed. The proposed approach enables macrocell user
equipments (MUEs) to optimize their uplink performance, by exploiting the
presence of neighboring small cell base stations. The problem is formulated as
a noncooperative game among the MUEs that seek to optimize their delay-rate
tradeoff, given the conditions of both the radio access network and the --
possibly heterogeneous -- backhaul. To solve this game, a novel, distributed
learning algorithm is proposed using which the MUEs autonomously choose their
optimal uplink transmission strategies, given a limited amount of available
information. The convergence of the proposed algorithm is shown and its
properties are studied. Simulation results show that, under various types of
backhauls, the proposed approach yields significant performance gains, in terms
of both average throughput and delay for the MUEs, when compared to existing
benchmark algorithms.

本论文提出了一种新的面向后传链路的干扰管理方法，通过解决非合作博弈问题来帮助宏基站用户设备优化上行表现，并利用分布式学习算法来在有限的信息量下选择最优上行传输策略，模拟结果显示在各种类型的后传链路下，该方法相比现有的基准算法显著提高了用户设备的平均吞吐量和延迟。