The Practical Challenges of Interference Alignment

Interference alignment (IA) is a revolutionary wireless transmission strategy that reduces the impact of interference. The idea of interference alignment is to coordinate multiple transmitters so that their mutual interference aligns at the receivers, facilitating simple interference cancellation techniques. Since IA's inception, researchers have investigated its performance and proposed improvements, verifying IA's ability to achieve the maximum degrees of freedom (an approximation of sum capacity) in a variety of settings, developing algorithms for determining alignment solutions, and generalizing transmission strategies that relax the need for perfect alignment but yield better performance. This article provides an overview of the concept of interference alignment as well as an assessment of practical issues including performance in realistic propagation environments, the role of channel state information at the transmitter, and the practicality of interference alignment in large networks.Comment: submitted to IEEE Wireless Communications Magazin

Concurrency-Aware Peer-To-Peer Negotiation on Wireless Devices

This publication describes techniques of optimally establishing peer-to-peer connections between a group of wireless devices. In aspects, the techniques are performed by a Connection Manager implemented on one or more of the wireless devices. The Connection Manager utilizes a selection process to select a wireless device from the group to act as the group owner of the peer-to-peer network. Through the selection process, the Connection Manager selects the wireless device that is most suited to become the group owner. For example, the Connection Manager may select as group owner the wireless device with the highest probability of providing optimal data-exchange (i.e., throughput) among the wireless devices, based on factors including device capabilities and/or the concurrency state of the device. The wireless device selected as group owner then configures the peer-to-peer network

Interference alignment from theory to practice

textWireless systems in which multiple users simultaneously access the propagation medium suffer from co-channel interference. Untreated interference limits the total amount of data that can be communicated reliably across the wireless links. If interfering users allocate a portion of the system's resources for information exchange and coordination, the effect of interference can be mitigated. Interference alignment (IA) is an example of a cooperative signaling strategy that alleviates the problem of co-channel interference and promises large gains in spectral efficiency. To enable alignment in practical wireless systems, channel state information (CSI) must be shared both efficiently and accurately. In this dissertation, I develop low-overhead CSI feedback strategies that help networks realize the information-theoretic performance of IA and facilitate its adoption in practical systems. The developed strategies leverage the concepts of analog, digital, and differential feedback to provide IA networks with significantly more accurate and affordable CSI when compared to existing solutions. In my first contribution, I develop an analog feedback strategy to enable IA in multiple antenna systems; multiple antennas are one of IA's key enabling technologies and perhaps the most promising IA use case. In my second contribution, I leverage temporal correlation to improve CSI quantization in limited feedback single-antenna systems. The Grassmannian differential strategy developed provides several orders of magnitude in CSI compression and ensures almost-perfect IA performance in various fading scenarios. In my final contribution, I complete my practical treatment of IA by revisiting its performance when CSI acquisition overhead is explicitly accounted for. This last contribution settles the viability of IA, from a CSI acquisition perspective, and demonstrates the utility of the proposed feedback strategies in transitioning interference alignment from theory to practice.Electrical and Computer Engineerin

Interference alignment in real world environments

textInterference alignment (IA) has been shown to provide all users of an interference channel with half the capacity achievable in an interference free point-to-point link resulting in linear sum capacity scaling with the number of users in the high SNR regime. The linear scaling is achieved by precoding transmitted signals to align interference subspaces at the receivers, given channel knowledge of all transmit-receive pairs, effectively reducing the number of discernible interferers. The theory of IA was derived under assumptions about the richness of the propagation channel; practical channels do not guarantee such ideal characteristics. This paper presents the first experimental study of IA in measured multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) interference channels. We show that IA achieves the claimed scaling factors in a wide variety of measured channel settings for a 3 user, 2 antennas per node setup. In addition to verifying the claimed performance, we characterize the effect of several realistic system imperfections such as channel estimation error, feedback delay, and channel spatial correlation, on sum rate performance.Electrical and Computer Engineerin

Cereal foods world

In this paper, we present a novel technique for localizing an event of interest in an underwater environment monitored by an underwater sensor network. The network consists of randomly deployed identical sensor nodes. Instead of proactively localizing every single node in the network as all proposed techniques set out to do, we approach localization from a reactive angle. We reduce the localization problem to the problem of finding 4-Node Coverage, in which we form a subset of nodes such that every node in the original set is covered by four nodes belonging to this special subset - which we call the anchor nodes for simplicity. This subset of anchor nodes behaves like a backbone to the localization process. The anchor nodes are localized based on an underwater GPS preexisting system. Whenever a node detects an event, it is reactively localized using the anchor nodes, and the sink is supplied with the necessary information. By limiting the sensing range of the sensor nodes, once we have obtained the location of the node that has detected the event, we have a rough estimation of the location of the event. We show that in terms of energy consumption, this localization technique far surpasses others in terms of energy efficiency