Wirelessly Powered Data Aggregation for IoT via Over-the-Air Function Computation: Beamforming and Power Control

Abstract

As a revolution in networking, Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e.g., sensors and actuators). One design challenge is efficient wireless data aggregation (WDA) over tremendous IoT devices. This can enable a series of IoT applications ranging from latency-sensitive high-mobility sensing to data-intensive distributed machine learning. Over-the-air (functional) computation (AirComp) has emerged to be a promising solution that merges computing and communication by exploiting analogwave addition in the air. Another IoT design challenge is battery recharging for dense sensors which can be tackled by wireless power transfer (WPT). The coexisting of AirComp and WPT in IoT system calls for their integration to enhance the performance and efficiency of WDA. This motivates the current work on developing the wirelessly powered AirComp (WP-AirComp) framework by jointly optimizing wireless power control, energy and (data) aggregation beamforming to minimize the AirComp error.To derive a practical solution, we recast the non-convex joint optimization problem into the equivalent outer and inner sub-problems for (inner) wireless power control and energy beamforming, and (outer) the efficient aggregation beamforming, respectively. The former is solved in closed form while the latter is efficiently solved using the semidefinite relaxation technique. The results reveal that the optimal energy beams point to the dominant eigen-directions of the WPT channels, and the optimal power allocation tends to equalize the close-loop (down-link WPT and up-link AirComp) effective channels of different sensors. Simulation demonstrates that controlling WPT provides additional design dimensions for substantially reducing the AirComp error.

Broadband Analog Aggregation for Low-Latency Federated Edge Learning

“…In particular, a comprehensive framework for MIMO AirComp that consists of beamforming optimization and a matching limited-feedback design is proposed in [25]. The framework was extended in subsequent work to wirelessly-powered AirComp system [26], where the beamformer was jointly optimized with the wireless power control to further reduce the AirComp distortion, and massive MIMO AirComp system [27], where a reduced-dimension two-tier beamformer design was developed by exploiting the clustered channel structure to reduce the channel-feedback overhead and signal processing complexity. It is also worth mentioning that, while AirComp is mostly deployed in computation-centric sensor networks as discussed above, the AirComp operation has been also leveraged in rate-maximization cellular systems such as two-way relaying [28] and MIMO lattice decoding [29].…”

Simultaneous Signal-and-Interference Alignment for Two-Cell Over-the-Air Computation

“…On the other hand, in the regime of noisy channels (moderate to low SNRs), besides AirComp DoF, the reliability (or cost) of each AirComp stream is measured by an additional metric such as expected error due to noise (or transmission power). Then optimizing the said matrices provides a mean of improving the reliability or reducing the cost of individual AirComp streams (see single-cell examples in [3], [15]).…”

Outage Performance of Full‐Duplex Relay Networks Powered by RF Power Station in Ubiquitous Electric Internet of Things

“…Unfortunately, although wireless sensors are deployed on the high-voltage line, they cannot be driven directly by the power of the high-voltage line. So, recently passive Internet of Things [6] has attracted attention and become an important part of UEIoT, where the sensors can be wirelessly charged by harvesting energy of the radio frequency (RF) signals emitted by wireless power stations [7][8][9]. The authors in [7] realized a practical wireless powered sensor network and investigated the efficiency of RF energy harvesting (EH).…”