Caching popular contents at base stations (BSs) of a heterogeneous cellular network (HCN) avoids frequent information passage from content providers to the network edge, thereby reducing latency and alleviating traffic congestion in backhaul links. The potential of caching at the network edge for tackling 5G challenges has motivated the recent studies of optimal content placement in large-scale HCNs. However, due to the complexity of network performance analysis, the existing strategies were designed mostly based on approximation, heuristics and intuition. In general, the optimal strategies for content placement in HCNs remain largely unknown and deriving them forms the theme of this paper. To this end, we adopt the popular random HCN model where K tiers of BSs are modeled as independent Poisson point processes (PPPs) distributed in the plane with different densities. Further, the random caching scheme is considered where each of a given set of M files with corresponding popularity measures is placed at each BS of a particular tier with a corresponding probability, called placement probability. The probabilities are identical for all BSs in the same tier but vary over tiers, giving the name tier-level content placement. We consider the network performance metric, hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user. Leveraging existing results on HCN performance, we maximize the hit probability over content placement probabilities, which yields the optimal tier-level placement policies. For the case of uniform received signal-to-interference thresholds for successful transmissions for BSs in different tiers, the policy is in closed-form where the placement probability for a particular file is proportional to the square-root of the corresponding popularity measure with an offset depending on BS caching capacities. For the general case of non-uniform SIR thresholds, the optimization problem is non-convex and a sub-optimal placement policy is designed by approximation, which has a similar structure as in the case of uniform SIR thresholds and shown by simulation to be close-to-optimal.

No abstract

No abstract

No abstract

No abstract

No abstract

The widely studied network energy efficiency (EE)-optimal problems (NEPs) emphasize optimality of system EE without taking fairness into account. In this paper, we focus on the max-min EE-optimal problem (MEP) by means of power allocation in interference-limited wireless networks. The MEP offers fairness ensurance for users in terms of EE by maximizing the EE of the worst-case user. We show that the MEP is NPhard. Based on generalized fractional programming, we propose a general EE-based update algorithm (EEUA) to tackle the MEP. One key step in the EEUA involves a nonconvex optimization and NP-hard power allocation problem, and we solve it by devising an iterative power allocation algorithm (IPAA) using sequential convex programming. Simulation results exhibit the fast convergence, low complexity, and insensitivity to initial values of the proposed algorithms, and verify that the MEP guarantees EE fairness among users, as well as reveal the differences between the MEP and the NEP.

No abstract