Hans Kroener

Dynamic point selection for LTE-advanced: Algorithms and performance

Dynamic Point selection (DPS) is a key Downlink (DL) Coordinated Multipoint (CoMP) technique that switches the serving data Transmission Point (TP) of a User Equipment (UE) dynamically among the UEs cooperating set of TPs without requiring a cell handover. It provides performance improvement due to TP selection-diversity gains and dynamic UE load balancing benefit. In this paper, we propose two simple DPS schemes that take into account the UEs current channel conditions and the cell loading conditions to make the UEs TP switching decisions. We show that these schemes improve the system performance under different practical and realistic settings, such as, cell handover margin, TP switching periods, bursty traffic conditions, and cooperation cell cluster sizes.

Centralized and Decentralized Coordinated Scheduling with Muting

We compare the algorithms for centralized and decentralized multi-cell joint optimization of coordinated scheduling with muting. For conventional non-overlapping clusters of cooperating cells, we show that for the same latency of communication, an appropriate decentralized algorithm can equivalently achieve the same jointly optimal solution achieved by a centralized approach. In this decentralized algorithm, cells do not exchange CSIs of all users with other cells, but instead exchange per-cell benefit metrics. In non-overlapping clusters, border cells in a cluster do not coordinate with strong interferers outside the cluster. We introduce a new cluster design called Liquid Cluster which allows a cell to flexibly cooperate with all its relevant interferers. The decentralized algorithm easily extends to Liquid Clusters, performing better than conventional non-overlapping 9-cell clusters.

Fast muting adaptation for LTE-A HetNets with Remote Radio Heads

Enhanced Intercell Interference Coordination (eICIC) techniques boost the performance of co-channel deployments of macro and small cells. However, features like the Almost Blank Subframes (ABS) are intended to be semistatically configured, due to the limited amount of information and the intrinsic delay over the X2 inter-cell interface. In this paper we investigate dynamic solutions for the allocation of the muted resources with small cells in the form of Remote Radio Heads (RRH), connected to the macro cell through a low latency interface. A simple eICIC framework for intercell Radio Resource Management in macro-RRH cases is developed. Based on minimal information collection, we propose an algorithm that adjusts the ABS ratio on a fast basis, aiming at balancing the instantaneous load betweeen the macro and the RRH layer. Performance results with bursty traffic and low and high load conditions show that the dynamic algorithm provides gains of 40-50% both in 5%-ile and 50%-ile user throughput compared to the semi-static case.

Performance Analysis of Interference Penalty Algorithm for LTE Uplink in Heterogeneous Networks

Heterogeneous networks (Hetnets) introduce an imbalance between downlink and uplink because the downlink transmit powers of the macro and pico eNodeBs can differ by as much as 16 dB. However, the user equipment (UE)s maximum uplink transmit power is the same regardless of whether the UE is connected to the macro or the pico eNodeB. The uplink throughput performance depends both on the SINR that the UEs see and the number of PRBs that each UE can get allocated. Due to the difference in the transmit powers of the eNodeBs, the number of UEs that attach to the pico eNodeB is typically smaller than the number that attach to the macro eNodeB. In this work, we consider these impacts and optimize the power control settings of the macro and pico UEs so that the cell-edge and average UE throughput are improved. We study the performance under two different power control schemes: Fractional Power Control (FPC) and Interference Penalty Algorithm (IPA). Using simulations, we study the impact of various parameter settings on the performance of IPA. Based on our studies, we can conclude that the macro UEs transmit power needs to be larger than that of the pico UE to overcome the larger number of UEs attaching to the macro eNodeB. We also study the impact of imperfect knowledge of neighbor cell path losses on the uplink performance of IPA.

Architecture Principles for Cloud RAN

Cloud Radio Access Networks (Cloud RAN) is an emerging architectural paradigm that attempts to exploit operational efficiencies through centralization of baseband functions, pooling efficiencies for RAN baseband processing, and air-interface performance gains by fast-time-scale multi-cell coordination. In this paper, we aim to derive insights underlying key technologies and tradeoffs that drive Cloud RAN. We highlight principles that explain the impact of Cloud RAN architectures and functional splits on multi-cell coordination gains. We show that the ability to extract pooling gains can differ significantly between RAN functions, depending on whether they are per-user or per-cell functions. We propose a method for elastic scaling of RAN functions that takes into account both real-time and non-real-time needs. We conclude with a proposal for a methodology for operators to evaluate the overall tradeoffs they will face in deciding whether to adopt Cloud RAN.

Channel Orthogonality and Utility-Based UE Pairing Schemes for LTE Uplink MU-MIMO

Multi-User MIMO (MU-MIMO) is a useful technique to enhance uplink capacity in LTE because the base stations employ multiple receive antennas whereas the user equipments transmit with only a single antenna. In this paper we investigate several UE pairing schemes that maximize the total system utility while adhering to the LTE Release-8 constraint of allocating identical contiguous resource blocks (RBs) to the paired UEs. We design pairing decisions using either the wideband or sub- band channel cross-correlation values of the UEs. These mechanisms offer varying trade-offs between performance and complexity.We show that with appropriate modifications, these schemes work well even for finite queue traffic.