Open RAN—Radio Access Network Evolution, Benefits and Market Trends

Open RAN (radio access network) movement is perceived as a game changer, having robust potential to introduce shifts in mobile radio access networks towards tailor-made solutions based on the architecture decomposition. It is widely assumed that those changes will affect the approach to network deployments and supply chains of network elements and their further integration and maintenance. First deployments of O-RAN-based networks have already delivered broadband services to end users. In parallel, many proof-of-concept feature evaluations and theoretical studies are being conducted by academia and the industry. In this review, the authors describe the RAN evolution towards open models and make an attempt to indicate potential open RAN benefits and market trends.

Signalling load reduction in 5G network based on cloud radio access network architecture

The rapid growth of both mobile users and application numbers has caused a huge load on the core network (CN). This is attributed to the large numbers of control messages circulating between CN entities for each communication or service request, however, making it imperative to develop innovative designs to handle this load. Consequently, a variety of proposed architectures, including a software defined network (SDN) paradigm focused on the separation of control and data plans, have been implemented to make networks more flexible. Cloud radio access network (C-RAN) architecture has been suggested for this purpose, which is based on separating base band units (BBU) from several base stations and assembling these in one place. In this work, a novel approach to realize this process is based on SDN and C-RAN, which also distributes the control elements of the CN and locates them alongside the BBU to obtain the lowest possible load. The performance of this proposed architecture was evaluated against traditional architecture using MATLAB simulation, and. the results of this assessment indicated a major reduction in signalling load as compared to that seen in the traditional architecture. Overall, the number of signalling messages exchanged between control entities was decreased by 53.19 percent as compared to that seen in the existing architecture.

Deep Reinforcement Learning for 5G Radio Access Network Slicing with Spectrum Coexistence

The paper presents a reinforcement learning solution to dynamic admission control and resource allocation for 5G radio access network (RAN) slicing requests, when the spectrum is potentially shared between 5G and an incumbent user such as in the Citizens Broadband Radio Service scenarios. Available communication resources (frequency-time resource blocks and transmit powers) and computational resources (processor power) not used by the incumbent user can be allocated to stochastic arrivals of network slicing requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements. As online algorithms, the greedy and myopic solutions that do not consider heterogeneity of future requests and their arrival process become ineffective for network slicing. Therefore, reinforcement learning solutions (Q-learning and Deep Q-learning) are presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon, subject to communication and computational constraints. Results show that reinforcement learning provides improvements in the 5G network utility relative to myopic, greedy, random, and first come first served solutions. In particular, deep Q-learning reduces the complexity and allows practical implementation as the state-action space grows, and effectively admits/rejects requests when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks. Furthermore, the robustness of deep reinforcement learning is demonstrated in the presence of the misdetection/false alarm errors in detecting the incumbent user's activity.

Deep Reinforcement Learning for 5G Radio Access Network Slicing with Spectrum Coexistence

The paper presents a reinforcement learning solution to dynamic admission control and resource allocation for 5G radio access network (RAN) slicing requests, when the spectrum is potentially shared between 5G and an incumbent user such as in the Citizens Broadband Radio Service scenarios. Available communication resources (frequency-time resource blocks and transmit powers) and computational resources (processor power) not used by the incumbent user can be allocated to stochastic arrivals of network slicing requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements. As online algorithms, the greedy and myopic solutions that do not consider heterogeneity of future requests and their arrival process become ineffective for network slicing. Therefore, reinforcement learning solutions (Q-learning and Deep Q-learning) are presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon, subject to communication and computational constraints. Results show that reinforcement learning provides improvements in the 5G network utility relative to myopic, greedy, random, and first come first served solutions. In particular, deep Q-learning reduces the complexity and allows practical implementation as the state-action space grows, and effectively admits/rejects requests when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks. Furthermore, the robustness of deep reinforcement learning is demonstrated in the presence of the misdetection/false alarm errors in detecting the incumbent user's activity.