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We present the design and implementation of WaveFlex, the first smart surface that enhances Private 5G networks operating under the shared-license framework in the Citizens Broadband Radio Service frequency band. WaveFlex works in the presence of frequency diversity: multiple nearby base stations operating on different frequencies, as dictated by a Spectrum Access System coordinator. It also handles time dynamism: due to the dynamic sharing rules of the CBRS band, base stations occasionally switch channels, especially when priority users enter the network. Finally, WaveFlex operates independently of the network itself, not requiring access to nor modification of the gNB or UEs, yet it remains compliant with and effective on prevailing cellular protocols. We have designed and fabricated WaveFlex on a custom multi-layer PCB, software defined radio based network monitor, and supporting control software and hardware. Our experimental evaluation benchmarks operational Private 5G and LTE networks running at full line rate. In a realistic indoor office scenario, 5G experimental results demonstrate an 8.58~dB average SNR gain, and an average throughput gain of 10.77 Mbps under a single gNB, and 12.84 Mbps under three gNBs, corresponding to throughput improvements of 18.4% and 19.5%, respectively. 

This paper presents Monolith, a high bitrate, low-power, metamaterials surface-based Orbital Angular Momentum (OAM) MIMO multiplexing design for rank deficient, free space wireless environments. Leveraging ambient signals as the source of power, Monolith backscatters these ambient signals by modulating them into several orthogonal beams, where each beam carries a unique OAM. We provide insights along the design aspects of a low-power and programmable metamaterials-based surface. Our results show that Monolith achieves an order of magnitude higher channel capacity than traditional spatial MIMO backscattering networks. 

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms. 

The first low earth orbit satellite networks for internet service have recently been deployed and are growing in size, yet will face deployment challenges in many practical circumstances of interest. This paper explores how a dual-band, electronically tunable smart surface can enable dynamic beam alignment between the satellite and mobile users, make service possible in urban canyons, and improve service in rural areas. Our design is the first of its kind to target dual channels in the Ku radio frequency band with a novel dual Huygens resonator design that leverages radio reciprocity to allow our surface to simultaneously steer energy in the satellite uplink and downlink directions, and in both reflective and transmissive modes of operation. Our surface, Wall-E, is designed and evaluated in an electromagnetic simulator and demonstrates 94% transmission efficiency and a 85% reflection efficiency, with at most 6 dB power loss at steering angles over a 150 degree field of view for both transmission and reflection. With 75cm2 surface, our link budget calculations predict 4 dB and 24 dB improvement in the SNR of a link entering the window of a rural home in comparison to the free-space path and brick wall penetration, respectively.