Feasibility analysis and self-organizing algorithm for RAN cooperative caching

The radio access network (RAN) cache is recently proposed to improve quality of experience perceived by mobile users on Internet content access and reduce intense pressure on the backhaul network. However, because RAN caches (e.g., associated with base stations) are located below mobility management entities in mobile core network, the continuity of content service for the handover users with unfinished RANcache content transmission is destroyed. Thus motivated, we design the seamless RAN-cache handover framework based on mobility prediction algorithm (MPA), where the smart terminal with unfinished content service during mobility predicts the break and the target handover access node (such as the BS or the WiFi access point), and pre-triggers the source RAN cache to return the content service to CDN servers outside the core network (CN) or to notify the RAN cache associated with the target handover access node to prepare for serving the handover user. The framework incurs no change to cellular infrastructure including RAN and CN. Different from prior arts of assumption that users' mobility is pre-known, we design the parallel autoregressive (AR) based received signal strength (RSS) prediction approach to achieve mobility prediction, and mathematically define its false probability during different tolerant intervals. The simulation scenario deployment engine and the user movement engine are designed, considering practical issues. Through simulations, the the false probability of seamless RAN-cache handover pre-trigger through MPA is less than 1.36%, and it can be guaranteed less than 1% as the maximal RAN-cache handover processing time is increased by 8%.

The ever increasing video demands from mobile users have posed great challenges to cellular networks. To address this issue, video caching in radio access networks (RANs) has been recognized as one of the enabling technologies in future 5G mobile networks, which brings contents near the end-users, reducing the transmission cost of duplicate contents, meanwhile increasing the Quality-of-Experience (QoE) of users. Inspired by the emerging software-defined networking technology, recent proposals have employed centralized collaborative caching among cells to further increase the caching capacity of the RAN. In this paper, we explore a new dimension in video caching in software-defined RANs to expand its capacity. We enable the controller with the capability to adaptively select the bitrates of videos received by users, in order to maximize the number and quality of video requests that can be served, meanwhile minimizing the transmission cost. To achieve this, we further incorporate Scalable Video Coding (SVC), which enables caching and serving sliced video layers that can serve different bitrates. We formulate the problem of joint video caching and scheduling as a reward maximization (cost minimization) problem. Based on the formulation, we further propose a 2-stage rounding-based algorithm to address the problem efficiently. Simulation results show that using SVC with collaborative caching greatly improves the cache capacity and the QoE of users.

The rapid growth of Internet contents from wireless data networks, especially social media, results in unprecedented traffic volume, invoking a challenge to the load of cellular infra-structure. Moreover, instead of traditional quality of service (QoS), the demands of quality of experience (QoE) become a more practical norm, which could barely be improved under present resource allocation on radio spectrum. With this in mind, we more generally consider resource allocation by exploiting caching in radio access networks (RANs), and show that caching, as a sort of storage, could be viewed as a substitution of the communication spectrum. Thus, we propose a collaborative strategy to implement caching in infrastructure and in mobile devices simultaneously, which in general turns out to be device-to-device (D2D) communication. This new design paradigm enables great reduction of latency for requesting Internet contents and can be implemented via slight amendments to present cellular systems.

Video is expected to be the dominant application by traffic volume over mobile networks in the near future. Mobile network operators are deploying video optimization techniques to enhance the user experience and network utilization for video delivery. Video optimization is typically “out-of-network” and includes techniques such as transcoding, transrating, time shifting and pacing that are implemented outside of mobile radio access and core networks. However, such techniques cannot easily exploit information about real time cell congestion and radio conditions for the video terminals because it is difficult to obtain such information from the radio access network. We propose a novel “in-network” video optimization technique, named Adaptive Guaranteed Bit Rate (AGBR), for HTTP-based Adaptive Streaming (HAS) video. This optimization technique is implemented at the base station and can thus exploit knowledge of the radio and congestion conditions. With only limited knowledge of the video stream properties or content, AGBR works by adjusting the throughput delivered to the different HAS clients that in turn adjust the video quality they request. The optimization algorithm maximizes aggregate quality across multiple video flows served by the base station without starving data clients, thereby improving the overall quality of experience. We demonstrate through extensive analytical modeling and simulations that AGBR can adapt to changing network conditions to support more video sessions at an acceptable quality than alternative algorithms, while enforcing fairness among all users competing for resources within a sector.

This paper will discuss the distributed caching strategies in clustered heterogeneous cellular networks. Basically, there are two obstacles to accomplish the caching policy in the wireless access networks, i.e., the limited storage capacity of small cells and the too small coverage area of each base station to imply content aggregation effect. Traditionally, the existing caching strategies only concern the local requests within one base station coverage, which neglects the clustered characteristics of network infrastructure and user traffic demand. In this paper, we consider a two-tier clustered cellular networks, within which small cells can exchange content to fulfill each user request. Intuitively, this cooperation between base stations increases the service capacity of clustered cellular networks as testified in our numerical simulation.

OpenRAN Gym: AI/ML development, data collection, and testing for O-RAN on PAWR platforms

In recent years, the radio access network (RAN) domain has seen significant changes with increased virtualization and softwarization, driven by the Open RAN (O-RAN) movement. However, the fundamental building block of the cellular network, i.e., the base station, remains unchanged and ill-equipped to handle this architectural evolution. In particular, there exists a general lack of programmability and composability along with a protocol stack that grapples with the intricacies of the 3GPP and O-RAN specifications. Recognizing the need for an “O-RAN-native” approach to base station design, this paper introduces HexRAN– a novel base station architecture characterized by key features relating to RAN disaggregation and composability, 3GPP and O-RAN protocol integration and programmability, robust controller interactions, and customizable RAN slicing. Furthermore, the paper also includes a concrete systems-level prototype and comprehensive experimental evaluation of HexRAN on an over-the-air testbed. The results demonstrate that HexRAN uses only 8% more computing resources compared to the baseline, while managing twice the user plane traffic, delivering control plane processing latency of under 120μs, and achieving 100% processing reliability. This underscores the scalability and performance advantages of the proposed architecture.

As we are leaving in 5G era which promises us to give high throughput rate, better efficiency rate, and high bandwidth etc as its feature make headlines to give advanced and enhanced services in various sector whether it could be IOT, infrastructure, agriculture, and health care [1]. Giving well-grounded communication technology possesses a fundamental challenge for 5G system in both Core network and Radio Access Network (RAN) levels [2]. To get a better credibility at RAN level, base station (EnodeB or gNodeB in 4G and 5G respectively) should allocate proper amount of resource as per UE and select appropriate Modulation and scheme (MCS) to give better user experience. The amount of Physical Resource Blocks is directly proportional to channel condition at the User Equipment (UE) end [5]. The Base Station would generally know the real time channel strength of each UE, which then later provides accordingly resource to the UE, as UE would be sending periodically CQI to Base station. However, the periodic transmission of CQI would lead to signaling overhead which hampers the performance of RAN. Henceforth its foremost thing to average it or to optimize the CQI value to give a better user experience. Therefore, will be exploring four such machine learning algorithms (ANN, reinforcement learning, KNN and SVM) to optimize the CQI and would be analyzing which Algorithm will give better performance. Key Words: CQI, Machine Algorithm, RAN, Offloading, Handover, Base Station

In spite of promising outcomes in optimizing energy usage for Radio Access Network (RAN) Base Station (BS) hardware, deployment, and resource management, existing methods frequently lack flexibility for scenarios involving multiple frequencies and technologies of BSs. This investigation presents a comprehensive BS switching strategy based on a threshold, tailored for real-world multi-frequency and multi-technology BSs within the RAN. The proposed approach strategically deactivates BSs using a threshold parameter that determines the maximum allowable growth in transmission power for active BSs, ensuring both coverage for users affected by BS deactivation and energy saving. Simulations conducted on a realistic multi-technology 5G New Radio (NR) RAN in an urban environment validate the efficacy of the proposed strategy, achieving up to 73% of energy saving. The study assesses the influence of the frequency order of BS deactivation and examines user re-association strategies aimed at minimizing either path loss or transmission power.

Over the past decade, wireless communications has experienced tremendous growth, and this growth is likely to multiply in the near future. The proliferation of mobile users and an ever increasing demand for multimedia services has resulted in greater capacity requirements. Radio frequency spectrum is scarce and cannot meet this ever increasing demand and the required Quality of Service (QoS) will no longer be achieved if efficient Radio Resource Management (RRM) solutions are not found. Conventional Radio Access Networks (RAN) have standalone Base Stations (BS) with capacity preconfigured for peak loads. These RANs have high call blocking and dropping rates since BSs resources cannot be shared. Cloud based RANs (C-RAN) have been proposed as a cost and energy efficient way of meeting high capacity demand of future wireless access networks by consolidating BSs to the cloud. Instead of relying on rejection of new call requests due to limited BS resources, C-RAN takes benefit of the cloud elasticity, which allows dynamic provisioning of cloud BS resources. This paper presents a novel C-RAN Call Admission Control (C-RAN CAC) to ensure Grade of Service (GoS) by improving blocking probability and improvement of call waiting times. Call blocking probability, call average waiting time and system utilization are used to evaluate the performance of the proposed CAC algorithm.

Space-Ground Integrated Networks (SGINs) hold the potential to enable seamless and high-quality global coverage in an economically viable manner. However, the limited bandwidth and relatively long delay of satellite-ground links pose significant challenges in meeting the increasing demands driven by surging traffic. To address this issue, we propose a three-tier cooperative caching architecture incorporating base stations (BSs), satellites, and a content server, which aims to reduce the average content retrieval delay through cooperative caching between BSs and satellites. The joint optimization problem among them is both challenging to solve and non-scalable due to its exponentially increasing computational complexity. Meanwhile, the geographic characteristics of requests are overlooked in existing work. As a consequence, we propose a novel block partitioning algorithm based on the popularity similarity across areas to facilitate cooperative caching, which reduces the computational complexity, and ensures the scalability and the effectiveness of cooperative caching. Based on the partitioned blocks, the optimization problem is decomposed into two subproblems: intra-block cooperative optimization among BSs and inter-block cooperative optimization among satellites, effectively catering to the characteristics of wide-area coverage in SGINs. For the subproblems with finite dimensions, Semidefinite Relaxation (SDR) based intra-block and inter-block cooperative caching approaches are proposed to obtain the optimal cooperative caching strategies. Extensive simulations demonstrate that, compared to directly solving the original problem, the proposed algorithm reduces the solving time from exponential to linear growth. Moreover, our algorithm outperforms existing schemes by reducing 13% average retrieval delay and improving 12% overall cache hit rate.

In the fifth-generation of mobile communications, network slicing is used to provide an optimal network for various services as a slice. In this paper, we propose a radio access network (RAN) slicing method that flexibly allocates RAN resources using deep reinforcement learning (DRL). In RANs, the number of slices controlled by a base station fluctuates in terms of user ingress and egress from the base station coverage area and service switching on the respective sets of user equipment. Therefore, when resource allocation depends on the number of slices, resources cannot be allocated when the number of slices changes. We consider a method that makes optimal-resource allocation independent of the number of slices. Resource allocation is optimized using DRL, which learns the best action for a state through trial and error. To achieve independence from the number of slices, we show a design for a model that manages resources on a one-slice-by-one-agent basis using Ape-X, which is a DRL method. In Ape-X, because agents can be employed in parallel, models that learn various environments can be generated through trial and error of multiple environments. In addition, we design a model that satisfies the slicing requirements without over-allocating resources. Based on this design, it is possible to optimally allocate resources independently of the number of slices by changing the number of agents. In the evaluation, we test multiple scenarios and show that the mean satisfaction of the slice requirements is approximately 97%.

To cope with higher demands, future radio access networks will consist of densely deployed base stations (BSs). As the BS ranges in such deployments will often overlap, areas exists that could be served individually by one of several BSs or cooperatively by all members of a group of BSs (using techniques like CoMP). One BS typically serves several such areas. Each BS has to decide, which area to serve when. This problem is compounded by the need to coordinate cooperative transmissions across several BSs. We model this as a queuing problem where each queue represents one particular area that is served by a specific combination of BSs; each BS has to decide which queue(s) to serve when. For three decision strategies, we derive a Markov model and solve it numerically to obtain steady-state solutions. These solutions let us quantify average latency and trade-offs between latency and number of BSs per area for both direct and cooperative scenarios. We show that cooperation reduces the number of BSs needed to cover an area but that a latency price has to be payed.

Wireless access networks like wireless LANs have been diffused widely. However, as mobile devices and applications increase and become diverse, more devices with various packet generation rates are expected to access such wireless networks. In this paper, in wireless access networks in which wireless stations (STAs) and a base station (BS) communicate directly in a multichannel environment, we propose novel channel selection control. This proposal adaptively switches the channel, on which each of the STAs is operating, to assign sufficient communication bandwidth to all of the STAs and the BS regardless of their packet generation rates.

We consider a mobile network consisting of both the wireless access network and the backhaul network. All base stations in the access network and gateways in the backhaul network are equipped with caches, so that routing costs for serving content requests can be reduced by caching the requested content items closer to the users. In this case, user association in the wireless access network must be aware of both the quality of wireless channels and the content caching strategy. In this paper, we propose a framework that jointly optimizes wireless user association and content caching in both access and backhaul networks. The resulting problem is NP-hard. We propose a polynomial-time algorithm based on convex approximation and pipage rounding that produces a solution within a constant factor of 1 − 1/e from the optimal. Simulation results show that the proposed joint algorithm outperforms schemes that combine cache-independent user association methods with traditional caching strategies (e.g. LRU) in terms of minimizing the aggregate routing cost and backhaul traffic while achieving a high data sum rate in the access network.