Matthias Wildemeersch

Energy Efficiency of Distributed Signal Processing in Wireless Networks: A Cross-Layer Analysis

In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: 1) hybrid, where the signal processing operations are shared between nodes and APs; 2) centralized, where signal processing is entirely implemented at the APs; and 3) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches.

Interference assessment of DVB-T within the GPS L1 and Galileo E1 band

Global navigation satellite systems (GNSS) are highly susceptible to interference, due to their low signal power. This paper studies the impact of DVB-T on the GPS L1 and Galileo E1 frequency band. An analytical tool has been developed to evaluate the probability of detection in the presence of interference, taking into account the possible effects of fading on the GNSS signal. Further, the tracking of GNSS signals has been simulated, estimating the degradation of the signal quality. Measurements on different receivers complete the study and illustrate the effects of DVB-T.

Traffic Adaptation and Energy Efficiency for Small Cell Networks With Dynamic TDD

The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered a promising scheme that is able to adjust the resource allocation to the instantaneous UL and DL traffic conditions, also known as traffic adaptation. In this paper, we study how traffic adaptation and energy harvesting can improve the energy efficiency (EE) in multi-antenna small cell networks operating dynamic TDD. Given the queue length distribution of small cell access points (SAPs) and mobile users (MUs), we derive the optimal UL/DL configuration to minimize the service time of a typical small cell, and show that the UL/DL configuration that minimizes the service time also results in an optimal network EE, but does not necessarily achieve the optimal EE for SAP or MU individually. To further enhance the network EE, we provide SAPs with energy harvesting capabilities, and model the status of harvested energy at each SAP using a Markov chain. We derive the availability of the rechargeable battery under several battery utilization strategies, and observe that energy harvesting can significantly improve the network EE in the low traffic load regime. In summary, the proposed analytical framework allows us to elucidate the relationship between traffic adaptation and network EE in future dense networks with dynamic TDD. With this work, we quantify the potential benefits of traffic adaptation and energy harvesting in terms of service time and EE.

Traffic Adaptation for Small Cell Networks with Dynamic TDD

Abstract-The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered as a promising scheme to flexibly adjust resource allocation based on UL and DL traffic demands, known as traffic adaptation. In this work, we study how traffic adaptation decreases cell service time and improves energy efficiency (EE) in small cell networks operating dynamic TDD. According to different UL and DL traffic parameters, we classify small cells into K ≥ 1 types, and accommodate the UL/DL configuration for each type of small cells with the objective to minimize the cell service time and maximize the network EE. In comparison with semi-static TDD scheme, dynamic TDD is shown to achieve larger service time gain as the traffic asymmetry between small cells increases. In summary, the proposed analytical framework allows us to elucidate the benefit of traffic adaptation to service time and EE in future dense networks with dynamic TDD.

Diffusion control in multi-agent networks

Diffusion processes are a fundamental way to describe the transfer of a continuous quantity in a generic network of interacting agents. In this work, we establish a probabilistic framework for diffusion in networks. In addition, we classify agent interactions according to two protocols where the total network quantity is conserved or variable. For both protocols, we use directed graphs to model asymmetric interactions between agents. Specifically, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree and out-degree Laplacians respectively. Our framework enables the addition and subtraction of the considered quantity to and from a set of agents. This allows the framework to accommodate external network control and targeted network design. We show how network diffusion can be externally manipulated by injecting time-varying input functions at individual nodes. Desirable network structures can also be constructed by modifying the dominant diffusion modes. To this purpose, we propose a Markov decision process that learns these network adjustments through a reinforcement learning algorithm, suitable for large networks. The proposed network control and design schemes enable flow modifications that promote the alteration of the dynamic and stationary behavior of the network in conservative and non-conservative networks.