Fronthaul-Limited Uplink OFDMA in Ultra-Dense CRAN With Hybrid Decoding

Abstract

Abstract-In an ultra-dense cloud radio access network (UD-CRAN), a large number of remote radio heads (RRHs), typically employed as simple relay nodes, are distributed in a target area, which could even outnumber their served users. However, one major challenge is that the large amount of information required to be transferred between each RRH and the central processor (CP) for joint signal processing can easily exceed the capacity of the fronthaul links connecting them. This motivates our study in this paper on a new hybrid decoding scheme where in addition to quantizing and forwarding the received signals for joint decoding at the CP, i.e. forward-and-decode (FaD) as in the conventional CRAN, the RRHs can locally decodeand-forward (DaF) the user messages to save the fronthaul capacity. In particular, we consider the uplink transmission in an orthogonal frequency division multiple access (OFDMA)-based UD-CRAN, where the proposed hybrid decoding is performed on each OFDMA sub-channel (SC). We study a joint optimization of the processing mode selections (DaF or FaD), user-SC assignments and the users' transmit power allocations over all SCs to maximize their weighted-sum-rate subject to the RRHs' individual fronthaul capacity constraints and the users' individual power constraints. Although the problem is nonconvex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. Further, we propose a low-complexity greedy algorithm which is shown to achieve close to the optimal performance under practical setups. Simulation results show the promising throughput gains of the proposed designs with hybrid decoding, compared to the existing schemes that perform either DaF or FaD at all SCs.

Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission

“…However, if the fronthaul capacity C F is limited, the uplink signals reported to CS via fronthaul may contain quantization distortion noise of significant power. For this reason, for small C F , it is better to perform local edge decoding at APs avoiding the quantization distortion, even at the cost of the degradation by interference signals [12]. This observation invokes the combinatorial optimization of selecting one of the edge decoding and cloud decoding strategies for every ID, requiring an exhaustive search over…”

Feasible Point Pursuit and Successive Convex Approximation for Transmit Power Minimization in SWIPT-Multigroup Multicasting Systems

“…From the application perspective regarding the implementation of the proposed methods in an actual system, it is worth mentioning that the computations pertaining the proposed algorithms may be employed via forthcoming C-ran architectures. The method works on the principle of increased computational resources in the cloud (i.e., where the computation of these algorithms can take place) [26], and hence these values are tolerable in an actual system.…”

User-Centric Performance Optimization With Remote Radio Head Cooperation in C-RAN

“…The problem was formulated in a non-convex form and solved by a Lagrange dual method. The authors of [17] investigated the weighted sum rate problem by jointly optimizing RRH selection and power allocation. By applying a Lagrange dual method, the authors derived an optimal solution to a special case where the number of sub-carrier is infinite.…”