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With the increasing number of Internet of Things (IoT) devices, a huge amount of latency-sensitive and computation-intensive IoT applications have been injected into the network. Deploying mobile edge computing (MEC) servers in cloud radio access network (C-RAN) is a promising candidate, which brings a number of critical IoT applications to the edge network, to reduce the heavy traffic load and the end-to-end latency. The MEC server’s deployment mechanism is highly related to the user allocation. Therefore, in this paper, we study hierarchical deployment of MEC servers and user allocation problem. We first formulate the problem as a mixed integer nonlinear programming (MINLP) model to minimize the deployment cost and average latency. In terms of the MINLP model, we then propose an enumeration algorithm and approximate algorithm based on the improved entropy weight and TOPSIS methods. Numerical results show that the proposed algorithms can reduce the total cost, and the approximate algorithm has lower total cost comparing the heaviest-location first and the latency-based algorithms.

Cloud-Radio Access Network (C-RAN) is a promising network architecture to reduce energy consumption and the increasing number of base station deployment costs in mobile networks. However, the necessity of enormous fronthaul bandwidth between a remote radio head and a baseband unit (BBU) calls for novel solutions. One of the solutions introduces the edge-cloud layer in addition to the centralized cloud (CC) to keep resources closer to the radio units (RUs). Then, split the BBU functions between the center cloud (CC) and edge clouds (ECs) to reduce the fronthaul bandwidth requirement and to relax the stringent end-to-end delay requirements. This paper expands this architecture by combining it with renewable energy sources in CC and ECs. We explain this novel system and formulate a mixed-integer linear programming (MILP) problem, which aims to reduce the operational expenditure of this system. Due to the NP-Hard property of this problem, we solve the smaller instances by using a MILP Solver and provide the results in this paper. Moreover, we propose a faster online heuristic to find solutions for high user densities. The results show that make splitting decisions by considering renewable energy provides more cost-effective solutions to mobile network operators (MNOs). Lastly, we provide an economic feasibility study for renewable energy sources in a CRAN architecture, which will encourage the MNOs to use these sources in this architecture.

Open Air Interface (OAI) alliance recently introduced a new disaggregated Open Radio Access Networks (O-RAN) framework for next generation telecommunications and networks. This disaggregated architecture is open, automated, software defined, virtual, and supports the latest advanced technologies like Artificial Intelligence (AI) Machine Learning (AI/ML). This novel intelligent architecture enables programmers to design and customize automated applications according to the business needs and to improve quality of service in fifth generation (5G) and Beyond 5G (B5G). Its disaggregated and multivendor nature gives the opportunity to new startups and small vendors to participate and provide cheap hardware software solutions to keep the market competitive. This paper presents the disaggregated and programmable O-RAN architecture focused on automation, AI/ML services, and applications with Flexible Radio access network Intelligent Controller (FRIC). We schematically demonstrate the reinforcement learning, external applications (xApps), and automation steps to implement this disaggregated O-RAN architecture. The idea of this research paper is to implement an AI/ML enabled automation system for software defined disaggregated O-RAN, which monitors, manages, and performs AI/ML-related services, including the model deployment, optimization, inference, and training.

In a world with increasing traffic demands, wireless technologies aim to meet them by means of new Radio Access Technologies that provide faster connectivity. Such is the case of 4G and 5G. However, in indoor scenarios, where the capabilities of these technologies are significantly affected by the distance to the base station and the materials used in the construction of buildings, Wi-Fi is still the technology of reference thanks to its low cost and easy deployment. In this context, it is usual to find multi-AP Wi-Fi networks whose deployment has been carefully planned. However, the user-AP association decision procedure is not defined by the IEEE 802.11 standard. As a result, vendors choose selfish approaches based on signal strength. This leads to uneven user distributions and nonoptimal resource utilization. To deal with this, densification has been used over the years, but this is expensive as it needs more infrastructure. Moreover, this results in more APs in the same collision domain. To avoid the need for densification, in this paper we introduce WiMCA, a joint SDN-based user association and channel assignment solution for Wi-Fi networks that considers signal strength, channel occupancy and AP load to make better association decisions. Experimental results have demonstrated that, in terms of aggregated goodput, WiMCA outperforms approaches based on signal strength by 55%, providing better user level fairness and accommodating more users and traffic before reaching the point at which densification is needed.

With the development of energy harvesting technologies and smart grid, the future trend of radio access networks will present a multi‐source power supply. In this article, joint renewable energy cooperation and resource allocation scheme of the fog radio access networks (F‐RANs) with hybrid power supplies (including both the conventional grid and renewable energy sources) is studied. In this article, our objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting. To solve this problem, the dynamic power allocation scheme in the network is studied by using Q‐learning and Deep Q Network respectively. Simulation results show that the proposed two algorithms have low complexity and can improve the average throughput of the whole network compared with other traditional algorithms. With the development of energy harvesting technologies and smart grid, the future trend of radio access networks will present a multi‐source power supply. In this article, joint renewable energy cooperation and resource allocation scheme of the fog radio access networks (F‐RANs) with hybrid power supplies (including both the conventional grid and renewable energy sources) is studied. In this article, our objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting. To solve this problem, the dynamic power allocation scheme in the network is studied by using Q‐learning and Deep Q Network respectively. Simulation results show that the proposed two algorithms have low complexity and can improve the average throughput of the whole network compared with other traditional algorithms.

Cooperation of co‐located mobile network operators can provide potential benefits for the capacity expansion without further densification of radio nodes. However, such benefits need to be scrutinised for coexisting cloud radio access network (C‐RAN) operators because the inter‐operator cooperation may compromise the superb interference coordination capability that each C‐RAN has. Furthermore, altering C‐RAN infrastructure for the cooperation incurs a high investment cost. In this paper, quantitative gain of inter‐operator coordination strategies is evaluated to provide the C‐RAN operators with a guideline on their cooperation decisions. The coordination strategies encompass dynamic user association and the spectrum sharing which aim at maximising the total user throughput. A heuristic algorithm is proposed that reduces the computational burden of the coordination significantly. Numerical results suggest that the inter‐operator cooperation is beneficial particularly when the network size of each operator tends to be highly asymmetric. It is also verified that the users who belong to smaller network attain more coordination gains.

Densification of mobile network infrastructure and integration of multiple radio access technologies are important approaches to support the increasing demand for mobile data traffic and to reduce energy consumption in future 5G networks. In this paper, the benefits of multi-radio transmission diversity (MRTD) are investigated by modelling the radio access link throughputs as uniform- and Rayleigh-distributed random variables and evaluating different user schedulers and resource allocation strategies. We examine different strategies for the allocation of radio accesses to individual users ranging from independent utilisation of the radio accesses to MRTD-enabled schemes. The schemes are compared by considering the statistics of the system throughput and energy consumption of the mobile devices. It is shown that MRTD can increase the throughput significantly through two types of diversity gain: Firstly by having multiple radio accesses to choose from for each user and secondly by having more available users to choose from for each radio access. The increased throughput also helps to reduce the energy consumption per bit, but this comes at a cost of increased energy consumption for channel measurement and reporting.

This research article has been conducted for the efficient use of frequency spectrum in multi-RAT cellular network using dynamic spectrum refarming (DSR). For this purpose, two overlapping networks, GERAN and EUTRAN, and their voice and data traffic statistics are taken into consideration. Our investigation of hourly traffic changes in the voice service in the GERAN and the data service in the EUTRAN (which are the dominant services of each of these networks), indicates that these changes are approximately orthogonal. This issue is the main motivation for use of DSR methods, including DSR without inband/guardband (I/G) overlay and DSR with I/G overlay, for efficient assignment of frequency resources to GERAN and EUTRAN. In this paper, the main focus is on the process of performing DSR with I/G overlay in a real network and its challenges. For this purpose, we used a nonlinear autoregressive neural network to predict the traffic changes of voice service in the GERAN. By this method, the prediction error is less than 6.5% in the peak hours. Also, a method for efficient use of GERAN and EUTRAN carrier numbers (ARFCN and EARFCN, respectively) has been proposed to reduce the mutual interference in DSR with I/G overlay. The results show the significant impact of the DSR with I/G overlay on increasing the EUTRAN average bit rate compared to the classic spectrum refarming method (168% in downlink and 146% in uplink).

As a promising paradigm for the 5G wireless communication system, a new evolution of the cloud radio access networks has been proposed, named as fog radio access networks (F-RANs). It is an advanced socially aware mobile networking architecture to provide a high spectral and energy efficiency while reducing backhaul burden. In particular, F-RANs take full advantages of social information and edge computing to efficiently alleviate the end-to-end latency. Based on the benefit of edge and cloud processing, key issues of F-RAN technique are radio resource allocation, caching, and service admission control. In this paper, we develop a novel F-RAN system control scheme based on the embedded game model. In the proposed scheme, spectrum allocation, cache placement, and service admission algorithms are jointly designed to maximize system efficiency. By developing a new embedded game methodology, our approach can capture the dynamics of F-RAN system and effectively compromises the centralized optimality with decentralized distribution intelligence for the faster and less complex decision making process. Through simulations, we compare the performance of our scheme to the existing studies and show how we can achieve a better performance under dynamic F-RAN system environments.

Radiotherapy (RT) has a fundamental role in the treatment of gynecologic malignancies, including cervical and uterine cancers. Hypofractionated RT has gained popularity in many cancer sites, boosted by technological advances in treatment delivery and image verification. Hypofractionated RT uptake was intensified during the COVID-19 pandemic and has the potential to improve universal access to radiotherapy worldwide, especially in low-resource settings. This review summarizes the rationale, the current challenges and investigation efforts, together with the recent developments associated with hypofractionated RT in gynecologic malignancies. A comprehensive search was undertaken using multiple databases and ongoing trial registries. In the definitive radiotherapy setting for cervical cancers, there are several ongoing clinical trials from Canada, Mexico, Iran, the Philippines and Thailand investigating the role of a moderate hypofractionated external beam RT regimen in the low-risk locally advanced population. Likewise, there are ongoing ultra and moderate hypofractionated RT trials in the uterine cancer setting. One Canadian prospective trial of stereotactic hypofractionated adjuvant RT for uterine cancer patients suggested a good tolerance to this treatment strategy in the acute setting, with a follow-up trial currently randomizing patients between conventional fractionation and the hypofractionated dose regimen delivered in the former trial. Although not yet ready for prime-time use, hypofractionated RT could be a potential solution to several challenges that limit access to and the utilization of radiotherapy for gynecologic cancer patients worldwide.