1. Collaborative Wireless Networks Computer Laboratory Digital Technology Group Wireless Communications Today Wireless communications today has evolved into two complimentary sectors: a mobile cellular network and a static home network. None of them deliver one service the customers want: ultra high speed static data transfer. People are on the move only 10% of their time, and with growing high quality media on demand, bandwidth is in short supply. This project intends to create a fixed wireless high data rate collaborative network and tackle the cooperative issues to optimize instantaneous throughput. Data Speed User Mobility 3G 2G 4G/ WiMax ADSL Collaborative Networks The idea of antennas collaborating originated from MIMO (multiple input, multiple output) systems. Multiple antennas under independent fading paths cooperated to increase multiplexing gain. However, due to the limited space on a mobile device, multiple antennas often can’t be realised. A distributed version, where multiple users each with an antenna cooperate to increase data throughput collectively is known as a collaborative network. Weisi Guo - 1 st Year PhD Student Coded Cooperation There are many cooperation strategies, all of them can implement coding to decrease errors. Cooperative codes can offer extra diversity in slow fading channels, but not in fast fading channels. Power Control Power allocation amongst a collaborative network is important for practical energy efficiency and channel capacity. Centralised power control, and distributed power control schemes are considered for varying knowledge of channel state information (CSI). How to estimate CSI effectively and how this impacts capacity is investigated. It was found that CSI is critical to uplink transmitter collaboration, whereas optimal power control is critical to downlink receiver cooperation. Further investigation optimized results using different modulation methods that account for capacity, as opposed to standard Gaussian input assumptions. This leads to a mercury filling scheme, where power is not always most allocated to the best uplink channel node. Mercury Filling (BPSK): Water Filling Power Allocation (Gaussian): Conclusions and Future Work The project started April 2007 as an EPSRC collaboration project between Cambridge University and Newcastle University. I plan to derive a distributed mercury power control mechanism that maximizes channel capacity for practical modulation methods. The project will also look at various convolution and turbo coding schemes in a variety of channel conditions. On the aspect of relay strategies, game theory and biological collaborative communications will be looked into. http://www.cl.cam.ac.uk/research/DTG Purple bar denotes the amount of noise a node’s channel has. The better the channel (less noise), the more power it should be allocated and the more it should cooperate with other weaker channels. This is for Gaussian inputs in a MIMO system. In a constellation modulated input system: an offset (mercury filling) is performed before power allocation (water filling). This takes into account the capacity limit of a channel. Performance is similar at low SNR, but can give opposite power allocations at high SNR as a channel reaches capacity. Slow Fading Cooperative Coding (Diversity order of 2): Fast Fading Cooperative Coding (Diversity of 1):