Uplink Multicell Processing with Limited Backhaul via Per-Base-Station Successive Interference Cancellation

Abstract

Abstract-This paper studies an uplink multicell joint processing model in which the base-stations are connected to a centralized processing server via rate-limited digital backhaul links. Unlike previous studies where the centralized processor jointly decodes all the source messages from all base-stations, this paper proposes a simple scheme which performs WynerZiv compress-and-forward relaying on a per-base-station basis followed by successive interference cancellation (SIC) at the central processor. The proposed scheme significantly reduces the implementation complexity of joint decoding while resulting in an easily computable achievable rate region. Although suboptimal in general, this paper proves that the proposed per-base-station processing SIC scheme can achieve the sum capacity of a special cellular Wyner model to within a constant gap. Under the proposed SIC framework, this paper also studies the capacity scaling with limited backhaul. It is established that in order to achieve to within constant gap to the maximum SIC rate with infinite backhaul, the limited-backhaul system must have backhaul link capacities that scale logarithmically with the signalto-interference-and-noise ratios (SINR) at the base-stations. This paper further studies the optimal backhaul rate allocation problem for an uplink multicell joint processing model with a total backhaul capacity constraint. The analysis reveals that the optimal rate allocation that maximizes the overall sum rate should have backhaul link rates that also scale logarithmically with the SINR at each base-station. Finally, the proposed perbase-station SIC scheme is evaluated in a practical multicell orthogonal frequency division multiple access (OFDMA) network to quantify the performance gain brought by the centralized processor.Index Terms-Coordinated multi-point (CoMP), interference channel, limited backhaul network multiple-input multipleoutput (MIMO), relay channel, successive interference cancellation, Wyner-Ziv coding

Adaptive remote radio head control for cloud radio access networks

“…Without RRH control, the number of selected RRHs inherently increases proportionally to L. Under both the nearest and SNR near range RRH selection and L near nearest RRH selection, the number of selected RRHs also increases with growth of L because increase of L makes it more probable that different users select different sets of RRHs. On the other hand, the proposed RRH control selects a subset of RRHs to maximize network capacity in consideration of RS overhead according to (11). Therefore, the proposed RRH control with both the greedy search-based and LP relaxation-based algorithms (Algorithms 1 and 2 developed in Section 3.3) selects far smaller number of RRHs than the compared RRH control approaches, which leads to considerable performance improvement as will be discussed in the following.…”

“…Hence, we develop practical algorithms that efficiently find approximate solutions of the RRH subset selection problem in (11). The first algorithm is a greedy searchbased algorithm described in Algorithm 1.…”

“…Therefore, when fronthaul links have finite capacity, RRH cooperation is inevitably limited. In [8][9][10][11][12][13], RRHs are modeled as relays between the users and CP, and compression/decompression strategies are developed considering the finite capacity of fronthaul links. In [14,15], RRH clustering schemes are developed considering the capacity limit of fronthaul.…”

A Stochastic Approach for Resource Allocation with Backhaul and Energy Harvesting Constraints

“…For example, in [2], the authors developed a strategy to design the precoder and the power allocation in a DL scenario considering limited backhaul capacity. In [3], the authors proposed a simple scheme that performs Wyner-Ziv compress-and-forward relaying on a per-BS basis in an uplink multicell scenario where the BS's are connected to a centralized processor via rate-limited backhaul links. In [4] a strategy is developed to efficiently manage the backhaul capacity among a group of picocells.…”

“…where ξ, (ξ > 1), is an overhead considered for the data transmissions to be sent through the backhaul,Ř BH (|K V |) is the backhaul capacity used by the voice users 3 , |K V | being the number of voice users, R BH is the overall backhaul capacity, Γ is the target SINR for the voice users, the function φ(·) is related to g(·) in (4) as φ (B(t)) = g(B(t))−T s ·(P CPICH + P c ), and N max is the number of available codes for the data users. Although all the variables in the optimization problem (9) depend on the scheduling period t, we only keep such explicit dependence w.r.t.…”