5G is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the Internet of Things. Focused research and standardization work have been addressing the corresponding challenges from the radio perspective while employing advanced features, such as network densi cation, massive multiple-input-multiple-output antennae, coordinated multi-point processing, intercell interference mitigation techniques, carrier aggregation, and new spectrum exploration. Nevertheless, a new bottleneck has emerged: the backhaul. The ultra-dense and heavy traf c cells should be connected to the core network through the backhaul, often with extreme requirements in terms of capacity, latency, availability, energy, and cost ef ciency. This pioneering survey explains the 5G backhaul paradigm, presents a critical analysis of legacy, cutting-edge solutions, and new trends in backhauling, and proposes a novel consolidated 5G backhaul framework. A new joint radio access and backhaul perspective is proposed for the evaluation of backhaul technologies which reinforces the belief that no single solution can solve the holistic 5G backhaul problem. This paper also reveals hidden advantages and shortcomings of backhaul solutions, which are not evident when backhaul technologies are inspected as an independent part of the 5G network. This survey is key in identifying essential catalysts that are believed to jointly pave the way to solving the beyond-2020 backhauling challenge. Lessons learned, unsolved challenges, and a new consolidated 5G backhaul vision are thus presented
5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, selfoptimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes.INDEX TERMS Backhaul, fronthaul, user-centric, user-cell association, SON, reinforcement learning, multiple attribute decision making.
Abstract-Cloud-RAN (C-RAN) is considered a prime enabler to 5G with promising resource pooling gains, tighter coordination among cells, and cost saving in remote radio heads and corresponding deployment and operation. However, C-RAN brings stringent requirements on the backhaul last mile, or the fronthaul, in terms of capacity, latency, and synchronisation, to the extent that direct fibre is believed to be the only plausible fronthaul solution. Knowing that more often than not, fibre to the home is not available and
Abstract-The backhaul network is a critical challenge towards the success of 5G and corresponding difficulties are many-fold, such as network coverage expansion, very high bandwidth, ultralow latency and energy consumption, at a minimum cost. No single backhaul solution can address all these requirements but, on the other hand, not all of the backhaul links require the same set of stringent requirements. To this end, we propose a novel scheme that capitalises on the diversity in both performance requirements and backhaul capabilities to maximise the systemcentric as well as user-centric performance indicators. The usercentric backhaul provisioning scheme uses multiple attribute decision making (MADM) for the user-cell-backhaul association criteria in a way that intelligently associates users with available cells based on corresponding dynamic radio and backhaul conditions while abiding by users requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator of the endto-end network performance such as capacity, latency, resilience, energy consumption, etc. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul capabilities that conform to the user requirements. Reinforcement learning is used by the radio cell to optimise the bias factors for each performance indicator in a way that maximises the system performance and users end-to-end quality of experience (QoE). Preliminary results based on a case study show considerable improvement in users QoE when compared to state-of-the-art user-cell association schemes.
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