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Spatially Controlled Wireless Comms

Distributed, networked communication systems are typically designed without considering how node positioning might affect Quality-of-Service (QoS). Network nodes are either assumed to be stationary or, if moving, their trajectories are independent of the respective communication task. In reality, though, channel information observed by each node is both spatially and temporally dependent. It is thus reasonable to ask if and how system performance can be improved by strategically controlling node positions, exploiting the spatiotemporal dependencies of the wireless channel.

We have developed a rigorous framework for exploiting node mobility in cooperative networks, in a fully stochastic setting. The basic paradigm considered is a single endpoint relay beamforming network, where relaying nodes are spatially controllable.

Presuming a realistic spatiotemporal stochastic model of the wireless channel, we have introduced a sequentially executed 2-stage stochastic programming framework, which optimizes expected network QoS jointly over network resources and relay positions, across time. This formulation led to an efficient procedure for joint communication and relay mobility control, and enabled the development of robust and easily implementable, distributed relay motion policies. An operationally important feature of this approach is that, although the system is optimized myopically, the average network QoS is nondecreasing through time (in practice, increasing), as long as the temporal dependence of the channel is sufficiently strong. In practice, our experiments have shown an improvement of about 80% on the average network QoS at steady state, compared to purely randomized relay motion (or no motion at all). This shows that strategic relay mobility can result in substantial performance gains, as far as enhancement of QoS is concerned.

This work has also been extended to the significantly more complicated setting of mmWave relay networks, in an urban communication scenario. Proper exploitation of the mmWave spectrum for commercial wireless communications is expected to offer data rates in the order of Gigabits-per-second, thus able to support future 5G applications (and beyond) such as Vehicle-to-Vehicle or Vehicle-to-Infrastructure communication.

We have introduced a new relay beamforming approach for mmWave communications in an urban scenario, which exploits the shadowing-induced correlation structure of the channel to reduce both network latency and CSI estimation overhead. Our system model consists of static relays deployed in clusters across streets. Under that setting, we have proposed a resource efficient scheme for joint optimal relay selection and distributed cooperative beamforming that maximizes expected Signal-to-Interference plus Noise Ratio (SINR) at the destination, under power constraints.

The key novelty of the proposed scheme is that relay selection is implemented in a predictive and distributed manner, by exploiting channel correlations and by using past and present measurements of magnitude CSI. As a result, at each time slot, optimal beamforming and optimal predictive relay selection are implemented completely in parallel. This parallelization eliminates delays induced by sequential execution of relay selection and beamforming, and substantially reduces CSI estimation overhead. Our simulations confirm that the proposed relay selection scheme outperforms any randomized selection policy, while, at the same time, achieves comparable performance to an ideal selection scheme that relies on perfect CSI estimates for all candidate relays.


  1. NSF Grant CNS-1239188 (pi: Dr. Athina Petropulu)
  2. NSF Grant CCF-1526908 (pi: Dr. Athina Petropulu, co-pi: Dr. Wade Trappe)

Selected Publications:

  1. A. Dimas, D. S. Kalogerias, and A. P. Petropulu, “Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications,” IEEE Access, to appear in 2019 (accepted).
  2. D. S. Kalogerias and A. P. Petropulu, “Spatially Controlled Relay Beamforming,” IEEE Transactions on Signal Processing, vol. 66, no. 24, pp. 6418 – 6433, December 2018.
  3. D. S. Kalogerias and A. P. Petropulu, “Spatially Controlled Relay Beamforming: 2-Stage Optimal Policies,” Working Extended Arxiv Preprint, May 2017.
  4. D. S. Kalogerias and A. P. Petropulu, “Enhancing QoS in Spatially Controlled Beamforming Networks via Distributed Stochastic Programming,” 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orleans, LA, USA, March 2017.

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