This chapter presents a content-centric framework for transmission optimization in cloud radio access networks (RANs) by leveraging wireless edge caching and physical-layer multicasting. We consider a cache-enabled cloud RAN, where each base station (BS) is equipped with a local cache and connected to a central processor (CP) via a backhaul link. The BSs acquire the requested contents either from their local caches or from the core network via the backhaul links. We first study the caching effects on multicast-enabled access downlink, where users requesting the same content are grouped together and served by the same BS or BS cluster using multicasting. We study the cache-aware joint design of the content-centric BS clustering and multicast beam-forming to minimize the system total power cost and backhaul cost subject to the quality-of-service (QoS) constraints for each multicast group.

This chapter investigates the impact of caching in the interference networks. First, we briefly review the basics of some classic interference networks and the corresponding interference management techniques. Then we review an interference network with caches equipped at all transmitters and receivers, termed as cache-aided interference network. The information-theoretic metric normalized delivery time (NDT) is introduced to characterize the system performance. The NDT in the cache-aided interference network is discussed for both single-antenna and multiple-antenna cases. It is shown that with different cache sizes, the network topology can be opportunistically changed to different classic interference networks, which leverages local caching gain, coded multicasting gain, and transmitter cooperation gain (via interference alignment and interference neutralization). Finally, the NDT results are extended to the partially connected interference network.

In this chapter, a novel framework is proposed to address critical mobility management challenges, including frequent handovers (HOs), handover failure (HOF), and excessive energy consumption for seamless HO in emerging dense wireless cellular networks. In particular, we develop a model that exploits broadband mmW connectivity whenever available to cache content that MUEs are interested in. Thus it will enable the MUEs to use the cached content and avoid unnecessary HO to small cell base stations (SCBSs) with relatively small cell sizes. First, we develop a geometric model to derive tractable, closed-form expressions for key performance metrics, such as the probability of caching, cumulative distribution function of caching duration, and the average data rate for content caching over an mmW link. In addition, we provide insight on the performance gains that caching in mmW–mW networks can yield in terms of reducing the number of HOs and the average HOF.

We consider joint caching, routing, and channel assignment for video delivery over coordinated small-cell cellular systems of the future internet. We formulate the problem of maximizing the throughput of the system as a linear program in which the number of variables is very large. To address channel interference, our formulation incorporates the conflict graph that arises when wireless links interfere with each other due to simultaneous transmission. We utilize the column generation method to solve the problem by breaking it into a restricted master subproblem that involves a select subset of variables and a collection of pricing subproblems that select the new variable to be introduced into the restricted master problem, if that leads to a better objective function value.

Edge-caching has received much attention as an efficient technique to reduce delivery latency and network congestion during peak-traffic times by bringing data closer to end users. Existing works usually design caching algorithms separately from physical layer design. In this chapter, we analyze edge-caching wireless networks by taking into account the caching capability when designing the signal transmission. Particularly, we investigate multi-layer caching, where both base station (BS) and users are capable of storing content data in their local cache and analyze the performance of edge-caching wireless networks under two notable uncoded and coded caching strategies. Wefirst calculate backhaul and access throughputs of the two caching strategies for arbitrary values of cache size. The required backhaul and access throughputs are derived as a function of the BS and user cache sizes. Then closed-form expressions for the system energy efficiency (EE) corresponding to the two caching methods are derived. Based on the derived formulas, the system EE is maximized via a precoding vectors design and optimization while satisfying a predefined user request rate. Two optimization problems are proposed to minimize the content delivery time for the two caching strategies.

Video data have been showed to dominate a significant portion of mobile data traffic and have a strong influence on a backhaul congestion issue in cellular networks. To tackle the problem, proactive caching is considered as a prominent candidate in terms of cost efficiency. In this chapter, we study a novel popularity-predicting-based caching procedure that takes raw video data as input to determine an optimal cache placement policy, which deals with both published and unpublished videos. For dealing with unpublished videos whose statistical information is unknown, features from the video content are extracted and condensed into a high-dimensional vector. This type of vector is then mapped to a lower-dimensional space. This process not only alleviates the computational burden but also creates a new vector that is more meaningful and comprehensive. At this stage, different types of prediction models can be trained to anticipate the popularity, for which information from published videos is used as training data.

In this chapter, we discuss the application of edge caching to enhance the physical layer security of cellular networks with limited backhaul capacity. By proactively sharing the same content across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative multiple-input multiple-output (MIMO) transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. We formulate a two-stage nonconvex optimization problem for minimizing the total transmit power while providing quality of service (QoS) and guaranteeing communication secrecy during content delivery, where the caching and the cooperative MIMO transmission policy are optimized in an offline caching stage and an online delivery stage, respectively. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design.

Driven by the inherent spatiotemporal correlation in wireless data demand, cellular network design is becoming increasingly content-centric. An integral component of this new paradigm is the network's ability to cache popular content at its edge, which includes base stations, access points, and handheld devices. This additionally reduces latency, which is one of the key challenges facing the next generation of cellular networks. As discussed in the earlier chapters, the huge size of a typical library of popular files and the relatively smaller storage capacities of edge devices, especially small cell base stations (SCBSs) and handheld devices, make it necessary to carefully determine the set of files (cache) that should be placed on each device. Compared to a wireless network for which caching mechanisms are fairly well understood, a distinctive feature of content-centric wireless networks is the mobility of the end users, which needs to be included in the system design. Inspired by this, we investigate the impact of mobility on edge caching in this chapter.

In this chapter, we present the concept of stochastic caching in large wireless networks with randomly distributed nodes. Specifically, we consider a random network where user devices can directly communicate and exchange information through device-to-device (D2D) communication. The distribution of D2D-enabled devices follows a Poisson point process (PPP), and each user stores proactively the popular files based on some probabilistic caching policy. The optimal caching probabilities depend on the specific objective functions to be optimized. We investigate three different caching schemes – namely maximizing the cache-hit probability, maximizing the density of successfully served requests by local caches, and minimizing the delay to receive the requested content. By comparing the performance achieved with these schemes, we show that the success probability of physical layer (PHY) transmission plays a critical role in the throughput and delay performance of large wireless networks with stochastic caching methods.

With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultra dense networks (UDNs), overlapped user-centric (UC) clustering is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each UE. However, the overlapped UC clustering has to be jointly designed with resource allocation in UDNs. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) are carefully considered in UDNs. To tackle these challenges, we formulate a joint overlapped UC clustering and resource allocation problem with the goal of maximizing the system’s spectral efficiency (SE). With the aid of the graph-theoretical framework, the problem is decoupled into two independent subproblems, and a distributed overlapped UC clustering solution as well as a graph-based resource allocation scheme were proposed. Our numerical results quantify the superior performance of the proposed framework in terms of both its per area aggregated user rate (PAAR) and user rate.

Ultra-dense cloud radio access network (UDCRAN) architecture, which integrates the capability of cloud computing and edge computing with the massively deployed radio access points, is a promising solution for the fifth-generation and beyond mobile communications. In order to accommodate the anticipated explosive growth of data traffic, fronthauling technology becomes a challenge technical issue in the fifth-generation and beyond UDCRANs. Moreover, the schemes related to interference management and resource management need to be reconsidered. In this chapter, we will provide a comprehensive review of the current research progress on fronthauling technology. Moreover, we will compare the advantages of various candidate fronthaul schemes.