Thomas Werthmann

Context-aware resource allocation for cellular wireless networks

Current cellular networks are often overloaded by Smartphone traffic, while the users’ Quality of Service (QoS) demands are not met. To cope with this problem, we demonstrate a new radio resource management approach. With Context-Aware Resource Allocation, the base station’s scheduler (i) observes Context Information (CI) from the user’s environment and (ii) utilizes this knowledge for an efficient throughput-delay tradeoff. After introducing our framework for accessing CI from the handheld’s applications and operating system, we use time-utility functions to develop a practical scheduling algorithm. Studying this heuristic under various traffic assumptions shows that our context-aware scheduler can support three times the load of proportional fair scheduling, at equal capacity and utility. Thus, even a small degree of CI increases the wireless links’ efficiency without sacrificing the users’ QoS.

Multiplexing gains achieved in pools of baseband computation units in 4G cellular networks

The tremendous increase of mobile user traffic load within the last few years forces us to efficiently use the wireless network and processing resources. Cloud computing and virtualization techniques offer an exciting opportunity to considerably reduce operation costs and provide flexible and dynamic systems. In this paper we present a simulation study for a cloud base station, which concentrates baseband processing functions of multiple radio sites. There we focus on the multiplexing-gains induced from user load and traffic heterogeneity. Our simulation results show that the data traffic influences the variance of the compute resource utilization, which in consequence leads to significant multiplexing gains if multiple sectors are aggregated into one single cloud base station. In addition, the spatial user distribution has a high impact on the compute resource load. These findings should be taken into account for the assessment of multiplexing gains in real networks.

Approaches to adaptively reduce processing effort for LTE Cloud-RAN systems

Cloud-RAN is a novel architecture for LTE networks, where antennas with only limited processing capabilities are deployed in the field and all baseband and higher layer processing of the base stations is pooled in a central office. It has been shown that the centralization leads to multiplexing gains for signal processing hardware. When a system is able to efficiently cope with overload of the compute resources, significant savings are possible. This allows to install less resources (saving Capital Expenditure, CAPEX) and to switch off more resources during low-load periods (saving Operational Expenditure, OPEX). In this publication, the resource allocation of a system of LTE base stations is formulated as an optimization problem. The formulation includes the reduction of interference by not using radio resources for transmission and the flexibility of using different MIMO modes. The results of optimization runs are evaluated. They show that about 50% of the compute resources can be saved without impacting the system performance. In overload situations, only 20% of the resources are sufficient to deliver 87% of the system performance. The most efficient approach to reduce processing effort is to adapt the MIMO mode and to reduce the number of virtual transmit antennas.

Interference mitigation by distributed beam forming optimization

Inter-cell interference is a major issue in OFDMA networks. One approach to reduce the amount of interference is to use beam forming antennas. Further interference mitigation is achieved if neighbor base stations coordinate the directions of their beams. This paper presents a novel distributed interference coordination algorithm using main-lobe steering beam formers. Our algorithm does not require any explicit signaling between base stations. Also, it does not require any additional channel measurements to be signaled to the base stations besides those already performed for basic operation. Our evaluations show that significant performance gains can be achieved even with non-greedy traffic. Index Terms – Interference coordination, beam forming antennas, COMP

Task assignment strategies for pools of baseband computation units in 4G cellular networks

A promising architecture for future cellular mobile networks is to place remote radio heads on the cell towers and connect those via fibers with a centralized pool of baseband units. Among other things, the centralization of baseband computation facilitates multiplexing gains and can thereby save compute resources. To realize these gains, an efficient and load-balancing assignment of compute jobs to computation units is required. In contrast to approaches in literature, our architecture virtualizes the processing for each UE separately. It thereby provides a finer granularity and allows to newly decide the assignment of a compute job to a processing unit whenever a UE starts transmitting data. In this publication, we present multiple heuristics to decide this assignment. We compare the efficiency of the assignment and the perceived service quality realized by the heuristics with an ideal assignment and the classical static cell-based assignment. Our evaluation shows that by using a good assignment heuristic, about 50% of the hardware resources can be saved.