Joint uplink and downlink delay‐aware resource allocation in C‐RAN

Abstract

This work considers two‐way communication between each pair of users with highly delay‐aware applications. We formulate a joint uplink and downlink resource allocation problem in a cloud radio access network. Assuming average end‐to‐end (E2E) delay of each user pair and practical limitation such as maximum transmit power, we maximize the total throughput of all pair of users in the cloud radio access network. In this setup, we consider that each user can be connected to at most one remote radio head and a limited capacity fronthaul link between each remote radio head and baseband unit. To present the resource allocation problem in a more tractable manner, we replace the E2E delay limitation with its equivalent throughput‐based formulation. Due to inherent NP‐hard and nonconvex nature of the proposed problem, we apply successive convex approximation to reach a two‐step iterative algorithm where, in each step, a specific set of optimization variable derived while other variables are fixed. The problem of each step is transformed into the standard geometric programming via the arithmetic‐geometric mean approximation. Simulation results reveal that our proposed joint uplink‐downlink resource allocation algorithm outperforms a case that uplink and downlink resources are allocated separately in terms of total throughput and outage probability of E2E delay, ie, a chance that E2E delay does not hold.

Pricing‐based power allocation in cloud radio access network with multiple access technology selection

“…For the case of m‐MIMO, let $$$ {M}_{\ell, k,{n}_g} $$$ denote the number of RRH antennas allocated to user $$$ {n}_g $$$ of the $$$ g $$$th MVNO on the $$$ k $$$th subcarrier. Also, assume channel state information (CSI) being estimated via uplink pilot transmission during period $$$ \tau $$$ at the beginning of each coherence time, $$$ T,T>>\tau $$$; then user throughput with pilot contamination is expressed as: 37,38 …”

Flexible Function Splitting and Resource Allocation in C-RAN for Delay Critical Applications

“…The considered E2E queueing model is illustrated in Fig.3. We note that this model is more practical in contrast to our previous work [27] in which we assume that each user data place in septate processing and transmission queues.…”

Resource allocation in sparse code multiple access‐based systems for cloud‐radio access network in 5G networks

“…The authors of Reference 22 consider the energy efficiency maximization in the uplink of a single‐carrier FDMA‐based multitier heterogeneous C‐RAN using a three‐step optimization algorithm. Authors in Reference 23 investigate joint uplink and downlink resource allocation problem under end‐to‐end average delay constraint in C‐RAN by a two‐step iterative algorithm. Furthermore in this paper to solve the proposed optimization problem, in each step, Successive Convex Approximation (SCA) is applied.…”

“…Furthermore in this paper to solve the proposed optimization problem, in each step, Successive Convex Approximation (SCA) is applied. Then each subproblem converts to standard geometric programming via the arithmetic geometric mean approximation 23 . Authors of Reference 24 proposed a resource allocation strategy based on machine learning for multitier Heterogeneous C‐RAN architecture in a 5G environment.…”