In this letter, we propose a novel precoding scheme for base station (BS) cooperation in downlink cellular networks that allow overlapped clusters. The proposed precoding scheme is designed to mitigate the overlapping-BS interference by maximizing the so-called clustered virtual signal-to-interference-plus-noise ratio (CVSINR). Simulations show that with the proposed scheme, overlapped clustering provides substantial throughput gain over the traditional non-overlapped clustering methods, and user fairness is also improved.

Base station (BS) cooperation is a promising technique to suppress co-channel interference for cellular networks. However, practical limitations constrain the scale of cooperation, thus the network is divided into small disjoint BS cooperation groups, namely clusters. A decentralized scheme for BS cluster formation is proposed based on efficient BS negotiations, of which the feedback overhead per user is nearly irrelevant to the network size, and the number of iteration rounds scales very slowly with the network size. Simulations show that our decentralized scheme provides significant sum-rate gain over static clustering and performs almost the same as the existing centralized approach. The proposed scheme is well suited for large-scale cellular networks due to its low overhead and complexity.

The energy consumption of the information and communication technology (ICT) industry, which has become a serious problem, is mostly due to the network infrastructure rather than the mobile terminals. In this paper, we focus on reducing the energy consumption of base stations (BSs) by adjusting their working modes (active or sleep). Specifically, the objective is to minimize the energy consumption while satisfying quality of service (QoS, e.g., blocking probability) requirement and, at the same time, avoiding frequent mode switching to reduce signaling and delay overhead. The problem is modeled as a dynamic programming (DP) problem, which is NP-hard in general. Based on cooperation among neighboring BSs, a low-complexity algorithm is proposed to reduce the size of state space as well as that of action space. Simulations demonstrate that, with the proposed algorithm, the active BS pattern well meets the time variation and the non-uniform spatial distribution of system traffic. Moreover, the tradeoff between the energy saving from BS sleeping and the cost of switching is well balanced by the proposed scheme.