This paper considers a cache-aided device-to-device (D2D) system where the users are equipped with cache memories of different size. During low traffic hours, a server places content in the users' cache memories, knowing that the files requested by the users during peak traffic hours will have to be delivered by D2D transmissions only. The worst-case D2D delivery load is minimized by jointly designing the uncoded cache placement and linear coded D2D delivery. Next, a novel lower bound on the D2D delivery load with uncoded placement is proposed and used in explicitly characterizing the minimum D2D delivery load (MD2DDL) with uncoded placement for several cases of interest. In particular, having characterized the MD2DDL for equal cache sizes, it is shown that the same delivery load can be achieved in the network with users of unequal cache sizes, provided that the smallest cache size is greater than a certain threshold. The MD2DDL is also characterized in the small cache size regime, the large cache size regime, and the three-user case. Comparisons of the server-based delivery load with the D2D delivery load are provided. Finally, connections and mathematical parallels between cache-aided D2D systems and coded distributed computing (CDC) systems are discussed.

In this work, we consider a cache-aided network where the users share the end-caches. In particular, a user has access to only one of the caches and the number of caches is less than the number of users. We propose a coded placement scheme that exploits the asymmetry in the number of users associated with each cache. Some of the signals sent to the overloaded caches facilitate the decoding of the coded subfiles stored at the underloaded caches. We present an explicit caching scheme and fully characterize the coded placement gain for two-cache systems. Then, we generalize our scheme to larger networks, where the optimal parameters are characterized by solving a linear program. We observe that, with the proposed scheme, as the asymmetry in the users' connectivity increases, the gain from coded placement is more evident.

In cache-aided networks, the server populates the cache memories at the users during low-traffic periods in order to reduce the delivery load during peak-traffic hours. In turn, there exists a fundamental tradeoff between the delivery load on the server and the cache sizes at the users. In this paper, we study this tradeoff in a multicast network, where the server is connected to users with unequal cache sizes and the number of users is less than or equal to the number of library files. We propose centralized uncoded placement and linear delivery schemes which are optimized by solving a linear program. Additionally, we derive a lower bound on the delivery memory tradeoff with uncoded placement that accounts for the heterogeneity in cache sizes. We explicitly characterize this tradeoff for the case of three end-users, as well as an arbitrary number of end-users when the total memory size at the users is small, and when it is large. Next, we consider a system where the server is connected to the users via rate-limited links of different capacities and the server assigns the users' cache sizes subject to a total cache budget. We characterize the optimal cache sizes that minimize the delivery completion time with uncoded placement and linear delivery. In particular, the optimal memory allocation balances between assigning larger cache sizes to users with low capacity links and uniform memory allocation.

This paper considers a device-to-device (D2D) coded caching system where the users have differing cache sizes. During low traffic hours, the server places subsets of the files at the users' cache memories, in a manner that enables serving the users' requests via D2D transmissions during peak traffic hours. The objective is to jointly design the users' cache contents and the D2D transmissions in order to minimize the D2D delivery load. In particular, we seek to identify the optimal uncoded placement and linear delivery schemes. We propose a novel lower bound on the D2D delivery load under uncoded placement, which enables us to explicitly characterize the minimum D2D delivery load under uncoded placement for several cases of interest.

This paper considers centralized coded caching, where the server not only designs the users' cache contents, but also assigns their cache sizes under a total cache memory budget. The server is connected to each user via a link of given finite capacity. For given link capacities and total memory budget, we minimize the worst-case delivery completion time by jointly optimizing the cache sizes, the cache placement and delivery schemes. The optimal memory allocation and caching scheme are characterized explicitly for the case where the total memory budget is smaller than that of the server library. Numerical results confirm the savings in delivery time obtained by optimizing the memory allocation.

Coded caching can improve fundamental limits of communication, utilizing storage memory at individual users. This paper considers a centralized coded caching system, introducing heterogeneous cache sizes at the users, i.e., the users' cache memories are of different size. The goal is to design cache placement and delivery policies that minimize the worst-case delivery load on the server. To that end, the paper proposes an optimization framework for cache placement and delivery schemes which explicitly accounts for the heterogeneity of the cache sizes. We also characterize explicitly the optimal caching scheme, for the case where the sum of the users' cache sizes is smaller than or equal to the library size.

Energy harvesting (EH) is a promising technology for realizing energy-efficient wireless networks. In this paper, we utilize the ambient RF energy, particularly interference from neighboring transmissions, to replenish the batteries of the EH enabled nodes. However, RF energy harvesting imposes new challenges into the analysis of wireless networks. Our objective in this paper is to investigate the performance of a slotted Aloha random access wireless network consisting of two types of nodes, namely Type I, which has unlimited energy supply and Type II, which is solely powered by an RF energy harvesting circuit. The transmissions of a Type I node are recycled by a Type II node to replenish its battery. We characterize an inner bound on the stable throughput region under half-duplex and full-duplex energy harvesting paradigms as well as for the finite capacity battery case. Additionally, we analyze the case where RF energy harvesting serves as a backup for an unlimited energy source. We present numerical results that validate our analytical results, and demonstrate their utility for the analysis of the exact system.

We consider a distributed storage system where data storage nodes are equipped with energy harvesting transmitters. In particular, F files are stored over n storage nodes using regenerating codes. The main operations of the distributed storage system are serving the file requests of data collectors and repairing the content of storage nodes that fail or leave the system. Each operation has an associated energy expenditure. Under the intermittent energy arrival profile, we study the problem of maximizing the number of retrieved files given a deadline. Additionally, we consider the problem of minimizing the repair time of a failed node. Both optimization problems turn out to be equivalent to binary programs, for which we provide a tractable solution in two steps. First, we determine necessary and sufficient conditions on the harvested energy that ascertain the feasibility of retrieving (repairing) M files in T time slots. Using these conditions, we develop two algorithms that reduce the formulated optimization problems to a single feasibility problem. Then, we solve the feasibility problem using forward and backward algorithms. Additionally, we study the online setup where only causal knowledge of energy arrivals is available at the network nodes. We present numerical results on the short and long term performance of the system operations under the proposed algorithms.

The recent witnessed evolution of cellular networks from a carefully planned deployment to more irregular, heterogeneous deployments of Macro, Pico and Femto-BSs motivates new analysis and design approaches. In this paper, we analyze the coverage probability in cellular networks assuming repulsive point processes for the base station deployment. In particular, we characterize, analytically using stochastic geometry, the downlink probability of coverage under a Matern hardcore point process to ensure minimum distance between the randomly located base stations. Assuming a mobile user connects to the nearest base station and Rayleigh fading, we derive two lower bounds expressions on the downlink probability of coverage that is within 4% from the simulated scenario. To validate our model, we compare the probability of coverage of the Matern hardcore topology against an actual base station deployment obtained from a public database. The comparison shows that the actual base station deployment can be fitted by setting the appropriate Matern point process density.