The objective of the BEACON project is to (re-)introduce analog communications into the design of modern wireless networks. We argue that the extreme energy and latency constraints imposed by the emerging Internet of Everything (IoE) paradigm can only be met within a hybrid digital-analog communications framework. Current network architectures separate source and channel coding, orthogonalize users, and employ long block-length digital source and channel codes, which are either suboptimal or not applicable under the aforementioned constraints. BEACON questions these well-established design principles, and proposes to replace them with a hybrid digital-analog communications framework, which will meet the required energy and latency constraints while simplifying the encoding and decoding processes. BEACON pushes the performance of the IoE to its theoretical limits by i) exploiting signal correlations that are abundant in IoE applications, given the foreseen density of deployed sensing devices, ii) taking into account the limited and stochastic nature of energy availability due to, for example, energy harvesting capabilities, iii) using feedback resources to improve the end-to-end signal distortion, and iv) deriving novel converse results to identify fundamental performance benchmarks. The results of BEACON will not only shed light on the fundamental limits on the performance any coding scheme can achieve, but will also lead to the development of unconventional codes and communication protocols that can approach these limits, combining digital and analog communication techniques. The ultimate challenge for this project is to exploit the developed hybrid digital-analog networking theory for a complete overhaul of the physical layer design for emerging IoE applications, such as smart grids, tele-robotics and smart homes. For this purpose, a proof-of-concept implementation test-bed will also be built using software defined radios and sensor nodes.

Publications:

1. Mahdi Boloursaz Mashhadi, Qianqian Yang, and Deniz Gunduz, CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems, Oct 2019.
2. Qianqian Yang, Mahdi Boloursaz Mashhadi, and Deniz Gunduz, Deep Convolutional Compression for Massive MIMO CSI Feedback, Jul 2019.
3. D. Gunduz, P. de Kerret, N. Sidiroupoulos, D. Gesbert, C. Murthy, M. van der Schaar, Machine learning in the air, IEEE Journal on Selected Areas in Communications, 2019.
4. Q. Yang, P. Hassanzadeh, D. Gündüz and E. Erkip, Centralized caching and delivery of correlated contents over Gaussian broadcast channels‌, submitted, Apr. 2019.
5. E. Ozfatura and D. Gunduz, Mobility-aware coded storage and delivery‌, submitted, Jan. 2019.
6. Q. Yang, M. Mohammadi Amiri and D. Gunduz, Audience-retention-rate-aware caching and coded video delivery with asynchronous demands‌, IEEE Transactions on Communications, to appear.
7. D. Cao, D. Zhang, P. Chen, N. Liu, W. Kang, and D. Gunduz, Coded caching with asymmetric cache sizes and link qualities: The two-user case‌, IEEE Transactions on Communications, to appear.
8. T-Z. Tung and D. Gunduz, SparseCast: Hybrid digital-analog wireless image transmission exploiting frequency domain sparsity‌, IEEE Communications Letters,  vol. 22, no. 12, pp. 2451-2454, Dec. 2018‌.
9. B. Rassouli, M. Varasteh and D. Gunduz, Gaussian multiple access channels with one-bit quantizer at the receiver‌, Entropy, vol. 20, no. 9, Sep. 2018. ‌
10. B. N. Bharath, K. G. Nagananda, D. Gunduz and H. V. Poor, Caching with time-varying popularity profiles: A learning-theoretic perspective, IEEE Transactions on Communications, vol. 66, no. 9, pp. 3837 - 3847, Sep. 2018.
11. Y. Murin, Y. Kaspi, R. Dabora, and D. Gunduz,Finite-length linear schemes for joint source-channel coding over Gaussian broadcast channels with feedback,‌‌IEEE Trans. Information Theory,  vol. 63, no. 5, pp. 2737 - 2772, May 2017.
12. M. Mohammadi Amiri and D. Gunduz, Fundamental limits of coded caching: Improved delivery rate-cache capacity trade-off‌, IEEE Trans. on Communications, vol. 65, no. 2, pp. 806-815, Feb. 2017.‌
13. M. Mohammadi Amiri and D. Gunduz, Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air, submitted, Jan. 2019.
14. A. Elkordy, A. Motahari, M. Nafie, and D. Gunduz, Cache-aided fog radio access networks with partial connectivity‌‌, submitted, Dec. 2018.
15. M. Mohammadi Amiri and D. Gunduz, Computation scheduling for distributed machine learning with straggling workers, revised, Oct. 2018.
16. S. Sreekumar, A. Cohen and D. Gunduz, Distributed hypothesis testing with a privacy constraint, submitted, Jul. 2018.
17. M. Tao, D. Gunduz, X. Fan, and J. S. Pujol Roig, Content caching and delivery in wireless radio access networks ‌, IEEE Transactions on Communications, to appear (invited paper).
18. E. Bourtsoulatze, D. Burth Kurka and D. Gunduz, Deep joint source-channel coding for wireless image transmission‌‌, IEEE Transactions on Cognitive Communications and Networking, ‌‌to appear.
19. B. Rassouli and D. Gunduz, Optimal utility-privacy trade-off with total variation distance as a privacy measure‌, IEEE Transactions on Information Forensics & Security, to appear.
20. B. Guler, D. Gunduz and A. Yener, Lossy coding of correlated sources over a multiple access channel: Necessary conditions and separation results‌, IEEE Transactions on Information Theory, vol. 64, no. 9, pp. 6081 - ‌‌6097, Sep. 2018.
21. M. Mohammadi Amiri and D. Gunduz, Caching and coded delivery over Gaussian broadcast channels for energy efficiency‌‌, IEEE Journal on Selected Areas in Communications, vol. 36, no. 8, pp. 1706 - 1720, Aug. 2018.‌‌
22. S. O. Somuyiwa, A. Gyorgy and D. Gunduz, A reinforcement-learning approach to proactive caching in wireless networks‌, IEEE Journal on Selected Areas in Communications, vol. 36, no. 6, pp. 1331 - ‌1344, Jun. 2018.‌‌‌
23. Q. Yang and D. Gunduz, Coded caching and content delivery with heterogeneous distortion requirements‌, IEEE Transactions on Information Theory, vol. 64, no. 6, pp. 4347 - 4364, Jun. 2018.‌
24. M. S. H. Abad, O. Ercetin, and D. Gunduz, Channel sensing and communication over a time-correlated channel with an energy harvesting transmitter, IEEE Transactions on Green Communications and Networking, vol. 2, no. 1, pp. 114-126, Mar. 2018.
25. E. Ozfatura and D. Gunduz, Mobility and popularity-aware coded small-cell caching, IEEE Communications Letters, vol. 22, no. 2, pp. 288 - 291, Feb. 2018.
26. M. Varasteh, B. Rassouli, O. Simeone, and D. Gunduz, Zero-delay source-channel coding with a low-resolution ADC front end‌, IEEE Transactions on Information Theory, vol. 64, no. 2, pp. 1241 - 1261, Feb. 2018.
27. M. Mohammadi Amiri and D. Gunduz, Cache-aided content delivery over erasure broadcast channels‌, IEEE Transactions on Communications, vol. 66, no. 1, pp. 370 - 381, Jan. 2018.
28. G. Giaconi, D. Gunduz, and H. V. Poor, Smart meter privacy with renewable energy and an energy storage device‌, IEEE Transactions on Information Forensics & Security, vol. 13, no. 1, pp. 129 - 142, Jan. 2018.
29. M. Varasteh, B. Rassouli, O. Simeone, and D. Gunduz, Zero-delay source-channel coding with a one-bit ADC front end and correlated receiver side information‌, IEEE Transactions on Communications, vol. 65, no. 12, pp. 5429 - 5444, Dec. 2017.
30. M. Mohammadi Amiri, Q. Yang, and D. Gunduz, Decentralized caching and coded delivery with distinct cache capacities‌, IEEE Transactions on Communicatoins, vol. 65, no. 11, pp. 4657 - 4669, Nov. 2017.‌‌
31. Y. Murin, Y. Kaspi, R. Dabora, and D. Gunduz,On the energy-distortion tradeoff of Gaussian broadcast channels with feedback,‌‌Entropy, vol. 19, no. 6, Jun. 2017.
32. E. Koken, E. Tuncel, and D. Gunduz, Energy-distortion exponents in lossy transmission of Gaussian sources over Gaussian channels, IEEE Trans. Information Theory, vol. 63, no. 2, pp. 1227-1236, Feb. 2017.‌