Richard Fritzsche

Towards a flexible functional split for cloud-RAN networks

Very dense deployments of small cells are one of the key enablers to tackle the ever-growing demand on mobile bandwidth. In such deployments, centralization of RAN functions on cloud resources is envisioned to overcome severe inter-cell interference and to keep costs acceptable. However, RAN back-haul constraints need to be considered when designing the functional split between RAN front-ends and centralized equipment. In this paper we analyse constraints and outline applications of flexible RAN centralization.

Robust sum rate maximization in the multi-cell MU-MIMO downlink

This paper studies linear precoding designed for the multi-cell multi-user multiple-input-multiple-output (MU-MIMO) downlink. The objective is to maximize the weighted sum rate (WSR) under imperfect channel state information (CSI) conditions and per base station (BS) transmit power constraints. The expectation of the WSR over the CSI error can be lower bounded by minimizing the expected weighted sum mean square error (MSE) assuming minimum MSE (MMSE) receive filters. The problem can be solved by an iterative algorithm which alternately calculates the MMSE receive filters given a fixed precoding matrix and vice versa. The algorithm converges to a local optimum. For the optimization of the precoding matrix under per BS power constraints, we present two robust solutions. The first one is based on the transmit Wiener filter solution under a sum power constraint combined with a consistent scaling to satisfy each per BS power constraint. In the second solution the problem is transformed into a second order cone program (SOCP) where per BS power constraints can be directly included. Simulation results show performance gains compared to robust and non-robust state of the art schemes.

CSI Distribution for Joint Processing in Cooperative Cellular Networks

Interference mitigation by applying Joint Processing (JP) in cooperative cellular networks boasts cell-edge performance compared to non-cooperative systems. However, downlink JP requires knowledge of Channel State Information (CSI) at all collaborating Base Stations (BSs), where interference received at the User Equipments (UEs) is markedly affected by the level of CSI quality influenced amongst others by the method of distributing CSI to the BSs. In this contribution, we compare CSI Distribution over the Air (CD-A) with CSI Distribution over the Backhaul (CD-B) considering a Frequency Devision Duplex (FDD) system. For CD-A, CSI is directly fed back from the UEs to the collaborating BSs via the uplink channel (assuming link adaptation is performed according to one master BS to which a UE has been assigned). Adopting CD-B, CSI is foremost transmitted only to the master BS and then forwarded to the other BSs using backhaul connections. This method typically introduces additional latency due to routing issues. In this contribution, we show that CD-A outperforms CD-B in the largest part of the cooperation area, even when pedestrian user velocities and small backhaul latencies are considered.

Iterative Soft-In Soft-Out Sphere Detection for MIMO Systems

Soft-Input Soft-Output (SISO) tree search algorithms pro- vide a promising approach for complexity reduced iterative MIMO detection. Realizations based on depth-first search enable near max-log optimal detection at reduced but still high complexity. In this paper we introduce how SISO sphere detectors based on enhancements of single tree search and tuple search algorithms can be efficiently used in iterative detection, outclassing previously proposed decoders or list based iterations. The complexity of the proposed algorithms can be significantly reduced by MMSE preprocessing in combination with a novel unbiased and separated candidate assimilation. Internal clipping of search paths enables further complexity reduction as well as alignment of the tree searches leading to efficient realizations.

Optimal Antenna Type Selection in a Real SU-MIMO Network Planning Scenario

Multiple-Input Multiple-Output (MIMO) technology is ready for deployment in the near future. A variety of MIMO antenna types including cross-polarized antennas, uniform linear arrays, and remote radio heads will be available. While theoretical MIMO performance gains have thoroughly been investigated, one of the major tasks of network operators, the selection process of an optimal MIMO antenna type for every sector in cellular network planning and optimization workflows, has rarely been treated so far. Consequently this paper presents a method on how advantages of different MIMO antenna types can be analyzed in coverage and capacity studies based on a MIMO gain benchmark. We show by simulations in a sample planning environment that the meaningful deployment of a variety of MIMO antenna types can increase the overall cellular network capacity and how mobile user positions influence the results.

Distributed robust sum rate maximization in cooperative cellular networks

Linear precoding for cooperative multi-cell transmission can provide substantial gains in user throughput, while channel state information (CSI) need to be available at the transmitter. Performance degradation due to imperfect CSI can be partially compensated by robust precoding techniques. For distributed precoding the pre-processing of the user data is performed locally at each base station (BS), while CSI of all participating users is needed. Hence, CSI need to be exchanged between BSs. However, in practice CSI sharing is affected by backhaul latency or limited backhaul capacity resulting in different CSI versions available at the BSs. In this paper, we present a novel robust sum rate maximizing precoding solution, which accounts for imperfect CSI sharing between BSs. Applying the proposed scheme, each BS optimizes its precoding matrix based on local knowledge and by assuming a certain precoding matrix is applied at the other BSs. The assumed precoding matrix results from degrading local knowledge to common but less accurate knowledge. We show that our solution can significantly boost the rate performance compared to existing precoding solutions.

Verifying ray tracing based CoMP-MIMO predictions with channel sounding measurements

Multiple-Input Multiple-Output (MIMO) technology is ready for deployment in commercial cellular networks in the very near future. Thus, the need of incorporating this technology into radio network planning and optimization rises dramatically for network operators. The main question to answer is how accurate MIMO channel models reflect the real MIMO channel. In this contribution we verify a detailed ray tracing channel simulator with channel sounding measurements in the 2.53 GHz range by comparing simulated and measured eigenvalue characteristics for various Single-User (SU) downlink scenarios in a Coordinated Multi-Point (CoMP) environment. From our comparison we can conclude that carefully performed Geometrical Optics based ray tracing simulations are an adequate prediction model to reflect main characteristics of SU-MIMO channels even in a CoMP scenario.

Robust precoding for network MIMO with hierarchical CSIT

In this work1 we consider a wireless network with K cooperating transmitters (TXs) serving jointly K receivers (RXs). Due to the practical limitations of the backhaul network, it is relevant to consider a setting where each TX receives its own imperfect estimate of the multi-user channel state, denoted as the distributed channel state information (CSI) setting. We focus in this work on a particular distributed CSI configuration called hierarchical CSI configuration in which the TXs can be ordered by increasing level of CSI. This scenario is particularly relevant for future networks with heterogeneous backhaul where the TXs connected with a weak backhaul link will receive only a coarse estimate while the TXs with a stronger backhaul will have a more accurate CSI. In that scenario, we formulate the optimal precoding as a team decision problem. Solving optimally this problem is extremely challenging such that we propose a heuristic approach allowing to obtain a simple, yet efficient and practical, precoding algorithm. The proposed precoding algorithm exploits the hierarchical structure of the CSI to make the transmission more robust to the imperfect CSI knowledge at the TXs.

Robust precoding with general power constraints considering unbounded channel uncertainty

In this contribution we deal with a cooperative cellular downlink scenario, where collaborating base stations jointly serve multiple users in a multiple-input multiple-output fashion. Linear spatial signal processing filters are applied at transmitter and receiver. The filters are designed in order to optimize four different mean square error related objective functions, considering general power constraints, i.e., transmit power constraints per arbitrary group of antennas. This optimization is based on channel state information, which is only imperfectly known in practical setups. In this contribution, we present a filter design for the stated optimization problems, taking statistical knowledge of unbounded channel uncertainty into account.

Joint RAN/backhaul optimization in centralized 5G RAN

This paper provides an overview of joint radio access network (RAN) and backhaul (BH) optimization methods in dense small cell networks, assuming a heterogeneous backhaul and centralization by Cloud RAN. The main focus is on the design of novel MAC (medium access control) and RRM (radio resource management) schemes for constrained, non-ideal backhaul which can influence the RAN performance. In this context, we provide some key technology approaches which incorporate the RAN/BH awareness at the cloud and exploit the benefits of Cloud-RAN by dynamically adapting to BH constraints.