A Linear-Time Grouping Algorithm for F-RANs with Index Coding and Cache-Aided NOMA

Fog radio access networks (F-RANs) have been presented as promising architectures for the future wireless system to provide high spectral and energy efficiency. With the help of the new designed fog access points (F-APs), F-RANs can take the full advantage of local caching capabilities, which relieves the load of fronthaul and reduces transmission delay. However, the cache resource optimization is a challenging task due to the uncertainty and dynamics of user file requests. Considering the high utilization of cache space and file diversity by coded caching, a deep reinforcement learning (DRL) based algorithm is developed for coded caching enabled F-RANs. The core idea of the proposal is that the network controller intelligently allocates the limited cache spaces of F-APs to different coded files based on the historical requests of the user. While the successful transmission probability of user requests is maximized during the learning process. Through numerical simulations, the convergence of the DRL based caching scheme is demonstrated, and the superiority of the proposal is verified by comparing with other baselines.

Featuring edge caching and computing, fog radio access networks have been seen as promising architectures. However, the joint optimization of cache and radio resource can put heavy burden on the resource manager in the cloud. To overcome this issue, hierarchical radio and cache resource management is studied in this paper. The core idea is to fully utilize the resource management capabilities of fog access points (FAPs) to divide time-domain resource on a small timescale by participating a coalitional game while make the resource manager allocate cache resource on a large timescale to maximize a long-term utility. Based on defined FAP preference, a low-complexity and distributed coalition formation algorithm is first developed under per-FAP fronthaul capacity constraints. Then, facing the challenges incurred by no closed form, discrete variables and the curse of dimensionality, multi-agent reinforcement learning (MARL) based caching is proposed for the resource manager. In the proposal, multiple agents are created, one for each content-FAP pair, who jointly learn a caching strategy via the interaction with a history network environment. By simulation, the effectiveness of the MARL based caching is demonstrated.

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Considering that fog access points (F-APs) can offer cooperation gain by jointly transmitting the same file or content diversity gain by concurrently transmitting the coded subfiles, we propose to use both joint transmission (JT) strategy and parallel transmission (PT) strategy to serve users. Using tools from stochastic geometry, we first derive the expressions of successful transmission probability (STP) under different transmission strategies, and then derive the expressions of fractional offloaded traffic (FOT) and delay. We further formulate the corresponding edge caching optimization problem to maximize the STP, FOT or minimize the delay. Considering the NP-hard property of the above optimization problem, we propose an improved fruit fly optimization (FFO) based edge caching scheme, which utilizes the dynamic search step to avoid pre-mature convergence. Furthermore, we theoretically analyze the convergence and global optimality of our proposed scheme. Numerical results show that a significant performance gain is achieved by using our proposed edge caching scheme compared with the baseline schemes.

Cooperative content caching and power allocation strategy for fog radio access networks

In this chapter, we will discuss the cooperative signal processing technique in F-RANs, which is a key enabler to boost F-RAN capacity. First, cooperative non-orthogonal multiple access (NOMA) is presented aiming at further improving user data rate compared to traditional orthogonal multiple access and NOMA schemes. Second, as an effective tool to handle information asymmetry, contract theory is involved and the interference mitigation between the macrocell and remote radio heads (RRHs) is formulated as a contract design problem, in which RRHs can use cooperative signal processing to help the macrocell serve its user. By simulation, the effectiveness of cooperative NOMA and contract based cooperative signal processing deign is confirmed.

Deployed with computation resources, fog radio access network (F-RAN) provides a promising solution for computation offloading. To take full advantage of two-tier computing in the F-RAN, on one hand, it is inevitable to design, between edge and cloud, an efficient and flexible fronthaul transmission strategy, and fronthaul resource allocation should be jointly optimized with allocation of tasks and other resources. On the other hand, a robust computation provisioning strategy that can avoid failures caused by estimation errors of available computation resources is necessary. In this work, considering the fronthaul compression and the uncertain computation capacity, we design an energy-efficient computation offloading mechanism in the F-RAN. The formulated problem is challenging to solve due to coupled communication and computation resource constraints and binary variables for task placement. We show that the problem can be recast as a convex problem if binary variables are relaxed. On top of this result, we propose an efficient algorithm to find a stationary solution. Through simulation, we demonstrate that the proposed algorithm outperforms the baseline algorithm significantly and converges to the near-optimal point solution. Besides, we compare the F-RAN with single-tier computing systems and show the excellence of the F-RAN in energy conservation for mobile devices.

SummaryFog computing allows for local data processing at the edge of the network. This allows for reduced link delays between mobile users and access points. Here, fog nodes are collocated with radio remote heads (RRHs) to provide local processing capabilities, that is, creating fog radio access network (F‐RAN) that can support ultralow latency for cellular networks that operate on millimeter wave (mmWave) bands. Here, mobile stations (MS) demand various network functions (NFs) that correspond to different service requests. Hence, it is critical to study function popularity and allow content caching at the F‐RAN. Given the limited resources at the fog nodes, it is important to efficiently manage the resources to improve network operations and enhance the capacity at reduced delays and cost. Therefore, caching in mmWave F‐RAN requires node allocation that can accommodate the highest number of cached functions. Hence, this paper proposes a novel node placement scheme that leverages Nelder–Mead meta‐heuristic. Results show that the proposed scheme yields in reduced delay, cost, and power and energy consumption.

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.

Fog radio access networks (F-RANs) put a substantial amount of computing, caching and control at the network edge, which has been demonstrated to decrease the heavy burden on fronthaul links and avoid high-complexity signal processing in the centralized baseband unit pool. Although F-RANs has the potential to improve the performance, improper remote radio heads (RRHs) clustering and RRH-server matching may result in severe performance degradation. In this paper, we jointly optimize RRH clustering and RRH-server matching to maximize the system throughput for F-RANs in a distributed manner. Specifically, we first model the throughput maximization problem into the combinatorial auction problem (CAP) with considering the non-linear computational resource constraint. Then, due to the NP-hard property of this problem, we propose a greedy algorithm to obtain a feasible solution of CAP by exploiting the properties in F-RANs, based on the methods in the weighted independent set problem (WISP). Next, we gradually improve the performance by adopting local $\alpha$-good improvement algorithm of WISP. Furthermore, we theoretically derive the performance bound of the proposed algorithm. Finally, the simulation results demonstrate the convergence property of the proposed algorithm, and corroborate the superiority of the proposed algorithm.

Fog radio access network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users. To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs. Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separate content placement and delivery phases. In contrast, this paper focuses on the scenario in which the set of popular content is time-varying, hence necessitating the online replenishment of the ENs’ caches along with the delivery of the requested files. The analysis is centered on the characterization of the long-term normalized delivery time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime. Online edge caching and delivery schemes are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit on the long-term NDT of F-RAN system. Analytical results are further verified by numerical simulation, yielding important design insights.

To facilitate various performance verification in practical fog radio access networks (F-RANs), this chapter will have a discussion on the prototype and test bed design of F-RANs. First, the design basics of F-RAN prototypes are elaborated, which are about fog computing enablers and the network controller. Second, some commonly used development tools will be presented to provide convenience for implementing F-RAN prototypes. Next, an example F-RAN prototype will be demonstrated, on which experimental evaluation is conducted to confirm the superior performance gains brought by F-RANs.

The fog radio access network (F-RAN) is regarded as a promising architecture allowing distributed signal processing and edge intelligent, offering a synergistic paradigm of communication and computing. However, emerging services transcend this framework, necessitating additional support for advanced generalized computing and environmental sensing. In response, this paper presents an upgraded F-RAN enabled by integrated sensing, communication and computing (ISCC). Specifically, integrated sensing and communication is introduced for extended radio capabilities, with computing-first design concurrently employed as an advanced computational solution. The updated F-RAN architecture harmonizes these two emerging technical trends, underscoring their synergistic benefits. Then, key challenges in realizing this vision are identified, with corresponding solutions discussed as key technologies. Some simulation results are provided to demonstrate that incorporating ISCC design into F-RANs enhances the comprehensive performance encompassing communication, sensing, and computing. Finally, open issues are outlined to stimulate innovation in related fields.

With the rapid development of healthcare, mobile cloud computing can improve medical efficiency by capturing and analyzing patient data. Fog computing, as an emerging paradigm to complement cloud computing, has significant advantages in local wireless signal processing, resource management and distributed storage capabilities to potentially meet future healthcare demands. However, performance is limited by the capacity of fronthaul links. In this paper, we propose a novel fog radio access network (F-RAN) model, where cooperation caching strategy and content transmission are jointly optimized. We formulate a mixed integer nonlinear programming problem in order to achieve an ultra-low delay for the proposed F-RAN. We also propose a novel matching algorithm based on the student project allocation (SPA) algorithm instead of the traditional optimization algorithm to solve the formulated problem. Numerical results reveal that the proposed joint optimization design can significantly improve the performance of the considered F-RAN.

This paper aims to characterize the synergy of distributed caching and wireless fronthaul in a fog radio access network (Fog-RAN) where all edge nodes (ENs) and user equipments (UEs) have a local cache and store contents independently at random. The network operates in two phases, a file-splitting based decentralized cache placement phase and a fronthaul-aided content delivery phase. We adopt normalized delivery time (NDT) to characterize the asymptotic latency performance with respect to cache size and fronthaul capacity. Both an achievable upper bound and a theoretical lower bound of NDT are obtained, and their multiplicative gap is within 12. In the proposed delivery scheme, we utilize the fronthaul link, by exploiting coded multicasting, to fetch both non-cached and cached contents to boost EN cooperation in the access link. In particular, to fetch contents already cached at ENs, an additional layer of coded multicasting is added on the coded messages desired by UEs in the fronthaul link. Our analysis shows that the proposed delivery scheme can balance the delivery latency between the fronthaul link and access link, and is approximately optimum under decentralized caching.

This work studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, cloud processing is carried out by a baseband processing unit (BBU) that is connected to enhanced remote radio heads (eRRHs) by means of fronthaul links. Edge processing is instead enabled by local caching of popular content at the eRRHs. Focusing on the design of the delivery phase for an arbitrary pre-fetching strategy, a novel superposition coding approach is proposed that is based on the hybrid use of the fronthaul links in both hard-transfer and soft-transfer modes. With the former, non-cached files are communicated over the fronthaul links to a subset of eRRHs, while, with the latter, the fronthaul links are used to convey quantized baseband signals as in a cloud RAN (C-RAN). The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to validate the performance of the proposed hybrid delivery scheme for different baseline pre-fetching strategies.