Michael Timmers

A Distributed Multichannel MAC Protocol for Multihop Cognitive Radio Networks

A cognitive radio (CR) network should be able to sense its environment and adapt communication to utilize the unused licensed spectrum without interfering with licensed users. In this paper, we look at CR-enabled networks with distributed control. As CR nodes need to hop from channel to channel to make the most use of the spectrum opportunities, we believe distributed multichannel medium access control (MAC) protocols to be key enablers for these networks. In addition to the spectrum scarcity, energy is rapidly becoming one of the major bottlenecks of wireless operations and has to be considered as a key design criterion. We present here an energy-efficient distributed multichannel MAC protocol for CR networks (MMAC-CR). Simulation results show that the proposed protocol significantly improves performance by borrowing the licensed spectrum and protects primary users (PUs) from interference, even in hidden terminal situations. Sensing costs are evaluated and shown to contribute only 5% to the total energy cost.

Distributed cognitive coexistence of 802.15.4 with 802.11

Thanks to recent advances in wireless technology, a broad range of standards are currently emerging. Interoperability and coexistence between these heterogeneous networks are becoming key issues, which require new adaptation strategies to avoid harmful interference. In this paper, we focus on the coexistence of 802.11 Wireless LAN and 802.15.4 sensor networks in the ISM band. Those networks have very different transmission characteristics that result in asymmetric interference patterns. We propose distributed adaptation strategies for 802.15.4 nodes, to minimize the impact of the 802.11 interference. This interference varies in time, frequency and space and the sensor nodes adapt by changing their frequency channel selection over time. Different distributed techniques are proposed, based on scanning (with increasing power cost) on the one hand, and based on increased cognition through learning on the other hand. These techniques are evaluated both for performance and energy cost. We show that it is possible to achieve distributed frequency allocation approaches that result only in an increase of 20% of the delay performance compared to ideal frequency allocation. Moreover, it is shown that a factor of two in energy consumption can be saved by adding learning to the system

Accumulative Interference Modeling for Cognitive Radios with Distributed Channel Access

A cognitive radio (CR) network should be able to sense its environment and adapt its communication to utilize unused licensed spectrum without interfering with incumbents. Properly modeling the expected interference from the CR network is therefore very important in the definition of coexistence rules to efficiently protect the incumbents. In this paper we model the accumulative interference generated from a large-scale CR network and investigate how this affects the sensing requirements of the CRs to meet an interference constraint. More specifically, our model considers the impact of discrete topology and the impact of the distributed channel access scheme. As an instantiation of our model, we consider a CR network based on the IEEE 802.11 standard. We show that the interference generated is large, since collisions cannot be avoided.

A Distributed Multichannel MAC Protocol for Cognitive Radio Networks with Primary User Recognition

A Cognitive Radio (CR) network should be able to sense its environment and adapt communication to utilize unused licensed spectrum without interfering with the licensed users. In this paper we will look at CR-enabled networks with distributed control. Since CR nodes will need to hop from channel to channel to make the most use of the spectrum opportunities, we believe distributed multichannel MAC protocols to be key enablers for these networks. Besides the spectrum scarcity, energy is rapidly becoming one of the major bottlenecks of wireless operations and has to be considered as a key design criterion. We present here an energy-efficient distributed multichannel MAC protocol for CR networks. Simulation results show that this protocol significantly improves performance by borrowing licensed spectrum and protects primary users from interference, even in hidden terminal situations. Sensing costs are evaluated and shown to contribute only 5% to the total energy cost.

Accumulative Interference Modeling for Distributed Cognitive Radio Networks

A Cognitive Radio (CR) network should be able to sense its environment to adapt its communication so that it can utilize unused licensed spectrum without interfering with incumbent users. Properly modeling the expected interference from the entire CR network is therefore very important to effectively protect these incumbent users. We model the accumulative interference generated from a large-scale CR network and investigate how the CR network density affects the sensing requirements of the CRs to meet an interference constraint. More specifically, our model considers the impact of discrete network topology, the impact of imperfect sensing and the impact of collisions when the CR uses a distributed channel access scheme. We then apply our model to a CR network based on the IEEE 802.11 standard. We show that the collisions occurring frequently in these networks only have a small on the sensing requirements to protect the incumbent network.

Exploring vs exploiting: Enhanced distributed cognitive coexistence of 802.15.4 with 802.11

Thanks to recent advances in wireless technology, a broad range of standards are currently emerging. Interoperability and coexistence between these heterogeneous networks are becoming key issues, which require new adaptation strategies to avoid harmful interference. In this paper, we focus on the coexistence of 802.11 WLANs and 802.15.4 sensor networks in the ISM band. These networks have very different transmission characteristics that result in asymmetric interference patterns. We propose distributed adaptation strategies for 802.15.4 nodes to minimize the impact of the 802.11 interference. This interference varies in time, frequency and space and the sensor nodes adapt by changing their frequency channel over time. The proposed strategies use adaptive simulated annealing and find a proper balance between exploration and exploitation.

Throughput Modeling of Large-Scale 802.11 Networks

The success of dynamic spectrum access through simple listen-before-talk etiquettes has made way for opening up the spectrum. However, many problems still remain in this kind of networks. Stations might not be able to sense as much transmissions and hence defer channel access less often than their neighbors. This can lead to unfairness or (worst-case) starvation of certain terminals. In this paper we model the throughput of a large-scale 802.11 network. Although the fairness issues in these networks are known, network modeling is still focusing on small-scale rigid networks. We want to open up this research toward large-scale randomly distributed topologies. A new model is developed to predict the long-term throughput of flows inside such a large-scale 802.11 network. Our model is validated through ns-2 simulations.

A Spatial Learning Algorithm for IEEE 802.11 Networks

The success of dynamic spectrum access through simple listen-before-talk etiquettes has paved the way for opening up the spectrum. However, many problems still remain in these networks. Due to the complex nature of IEEE 802.11 networks, for instance, optimizing these networks regarding power, rate and carrier sense threshold remains a very tough challenge. In this paper, we introduce Spatial Learning. This new optimization algorithm for IEEE 802.11 networks employs learning to find an optimal combination of power, rate and carrier sense threshold. It is assumed that nodes behave selfishly and are only interested in optimizing their own throughput. Extensive network simulations show that Spatial Learning performs better than the state-of-the-art solution, Spatial Backoff, on all axes of interest: network-wide throughput, fairness and power consumption.