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CoMP clustering and backhaul limitations in cooperative cellular mobile access networks

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Abstract

Coordinated Multi-Point (CoMP) transmission and reception is a promising solution for managing interference and increasing performance in future wireless cellular systems. Due to its strict requirements in terms of capacity, latency, and synchronization among cooperating Base Stations (BSs), its successful deployment depends on the capability of the mobile backhaul network infrastructure.

We deal with the feasibility of CoMP transmission/reception, in particular of Joint Transmission (JT). For this, we first evaluate which cluster sizes are reasonable from the wireless point-of-view to achieve the desired performance gains. Thereafter, we analyze how different backhaul topologies (e.g., mesh and tree structures) and backhaul network technologies (e.g., layer-2 switching and single-copy multicast capabilities) can support these desired clusters. We study for different traffic scenarios and backhaul connectivity levels, which part of the desired BS clusters are actually feasible according to the backhaul characteristics. We found out that a significant mismatch exists between the desired and feasible clusters. Neglecting this mismatch causes overheads in real JT implementations, which complicates or even prevents their deployment.

Based on our findings, we propose a clustering system architecture that not only includes wireless information, as done in the state of the art, but also combines wireless and backhaul network feasibility information in a smart way. This avoids unnecessary signaling and User Equipment (UE) data exchange among BSs which are not eligible to take part in the cooperative cluster. Evaluations show that our scheme reduces the signaling and UE data exchange overhead by up to 85% compared to conventional clustering approaches, which do not take into account the backhaul network’s status.

Introduction

Interference is a common and inherent issue to any wireless communication system. The demand for high throughput has nowadays stressed the adoption of frequency/spatial reuse techniques in such a way that interference management has become the key factor to determine the overall wireless system performance. In many existing wireless communication systems, interference is dealt with by coordinating User Equipments (UEs) in order to orthogonalize their transmissions in time or frequency, or by increasing transmission power and treating each other’s interference as noise. Recently, the paradigm has shifted to focus on how to intelligently exploit the knowledge and/or the structure of interference in a certain area through the use of Base Stations (BSs) cooperation techniques. In a Coordinated Multi-Point (CoMP) transmission/reception system, multiple BSs form a cluster and cooperate by exchanging signaling and/or UE data via the core and backhaul network. Such systems have proven to be a very effective solution for interference management [1], [2], [3].

Following the 3GPP Long Term Evolution—Advanced (LTE-A) terminology, CoMP techniques are grouped into two categories, depending on whether UE data, other than signaling, is shared among the cooperating BSs:

  • Joint Processing (JP): In the downlink, the UE receives either a Joint Transmission (JT), where multiple BSs simultaneously send on the same physical resources to create constructive interference at the UE, or the UE performs a Dynamic Cell Selection (DCS) to select the best BS for the current data transmission. In the uplink, the UE data transmission is received by multiple BSs and sent from each cooperating BS to a central point (e.g., the serving BS, which is the BS with the best wireless channel conditions to the UE, or a central processing unit located in the network) where the received versions of the UE data are jointly decoded. UE data in JP is shared among cooperating BSs.

  • Coordinated Scheduling (CS)/Coordinated Beamforming (CB): The UE receives data transmissions only from one cell but scheduling and beamforming decisions are coordinated among the cells in the CoMP cluster. UE data is not shared among cooperating BSs.

The different CoMP techniques are illustrated in Fig. 1. Note that JP is often combined with CB to further increase performance. Although this is not necessarily required, we show such a combined approach in the figure.

It has been shown that JT can improve the average cell throughput up to 10% when using Single-User MIMO (SU-MIMO) and up to 60% when using Multi-User MIMO (MU-MIMO). Cell-edge UEs’ performance improves for SU-MIMO up to 20% and for MU-MIMO up to 50% [4]. However, there is a trade-off between the performance gain and the processing complexity as well as the amount of Channel State Information (CSI) signaling and UE data to be exchanged between the cooperating BSs. The CSI is valid only for a few milliseconds, depending on the user mobility. This requires a continuous update of this information (on the order of 1 ms) both between the UE and its serving BS, and between the serving BS and its cooperation partners, which are also required to share the CSI of the UEs within their cell. This, in turn, results in very demanding requirements for the backhaul network, especially in terms of latency, capacity, and synchronization precision between the cooperating BSs. If these requirements cannot be fulfilled by the backhaul infrastructure, CoMP performance can significantly decrease or some of the described CoMP techniques even become infeasible. In this regard, if mobile operators intend to exploit the advantages of CoMP, they have to carefully take into account these additional requirements when deploying next generation mobile backhaul networks.

In this paper, we investigate the backhaul network infrastructure from the topological point of view, trying to understand which topologies, like mesh or tree structures, best fulfill CoMP requirements. Furthermore, we look at the used technology, analyzing the different trade-offs offered by available and upcoming backhaul network solutions, like support for layer-2 switching where no Internet Protocol (IP) processing delay occurs or backhaul infrastructures that support single-copy multicast. For these different backhaul infrastructure implementations, we evaluate, for different traffic scenarios and backhaul connectivity levels, which BS clusters are actually feasible compared to the ones desirable from the Radio Access Network (RAN) perspective, i.e., selected based on the wireless channel conditions. This means we check which fraction of the BSs that are desirable to cooperate from the wireless point of view can actually cooperate under the given backhaul network limitations. We call this ratio the wireless cluster feasibility.

To avoid undesirable waste of signaling exchange and eventually UE data sharing between BSs, we propose a CoMP clustering architecture which takes into account both the instantaneous backhaul status and long-term wireless channel conditions before doing expensive wireless clustering decisions based on CSI. Our scheme prevents to extend the wireless cluster to infeasible BSs (from the backhaul network perspective), thus saves a considerable amount of network resources.

Our study is supported by several simulations. We derive an upper bound for the wireless cluster feasibility, taking into account a certain degree of over-provisioning in the backhaul network. We find out that in many scenarios there exists a significant mismatch between the desired wireless cluster, as defined by the RAN, and the actually feasible cluster, as allowed by the backhaul network. Such results motivate the necessity of a solution like our CoMP clustering architecture.

The remainder of this work is structured as follows. In Section 2, we discuss in detail the requirements that have to be fulfilled by the backhaul network in a CoMP system and the problems that arise from that. We also take a look at different CSI feedback and sharing schemes and analyze the trade-offs, especially in terms of complexity and backhaul requirements. Thereafter, we evaluate the wireless cluster feasibility for different backhaul network configurations in Section 3. Based on these results, Section 4 proposes and evaluates a system architecture that exploits backhaul network information in the clustering process. Concluding remarks are given in Section 5.

Section snippets

Joint Transmission details and implementation issues

The most promising CoMP scheme in the downlink from the performance gain perspective is Multi-User MIMO (MU-MIMO) Joint Transmission (JT). JT in the downlink means that UE data is sent from multiple BSs at the same time using the same physical resources such that the signals interfere constructively at the UEs to maximize the received energy. This is achieved by individually precoding the UE data at the cooperating BSs based on measured CSI from the UEs.

To achieve this, MU-MIMO JT requires to

Desired CoMP clusters and their feasibility

To get an idea how the number of BSs that participate in a joint transmission to a UE influences the UE’s throughput performance, we first simulate JT assuming an ideal backhaul network in Section 3.1. From this evaluation, we derive reasonable BS cluster sizes that are desirable from the wireless point-of-view. Thereafter, we check the feasibility of these clusters sizes in a second simulation in Section 3.2. Here, several backhaul network deployment scenarios, including different topologies

CoMP clustering

The evaluations in Section 3 have shown that both the desired wireless cluster sizes, determined by the wireless channel conditions, and the feasible clusters, limited by the backhaul network, are quite different. In some situations, e.g., when the backhaul network provides good connectivity, the wireless conditions limit the size of the cluster eventually used for cooperation, as further increasing the cluster size does not lead to big additional gains. In other cases, e.g., when the backhaul

Conclusions

We have evaluated how big reasonable clusters for JT CoMP have to be from the wireless point-of-view. This simulation has shown that the gain in UE spectral efficiency per additional BS decreases from nearly 20% down to about 2% when clusters consist of more than 7 BSs. This renders cluster sizes between 2 and 7 BSs as reasonable assumption.

Based on these results, we have evaluated the feasibility of such desired clusters for different optical backhaul network scenarios. Simulations have shown

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