Multi-functional OFDM Signal Design for Integrated Sensing, Communications, and Power Transfer

TL;DR

The design of a wideband OFDM signal to power a sensor while simultaneously performing target-sensing and communication and the results highlight the great potential of the co-designed ISCAP system for further efficiency enhancement.

Abstract

The wireless domain is witnessing a flourishing of integrated systems, e.g. (a) integrated sensing and communications, and (b) simultaneous wireless information and power transfer, due to their potential to use resources (spectrum, power) judiciously. Inspired by this trend, we investigate integrated sensing, communications and powering (ISCAP), through the design of a wideband OFDM signal to power a sensor while simultaneously performing target-sensing and communication. To characterize the ISCAP performance region, we assume symbols with non-zero mean asymmetric Gaussian distribution (i.e., the input distribution), and optimize its mean and variance at each subcarrier to maximize the harvested power, subject to constraints on the achievable rate (communications) and the average side-to-peak-lobe difference (sensing). The resulting input distribution, through simulations, achieves a larger performance region than that of (i) a symmetric complex Gaussian input distribution with identical mean and variance for the real and imaginary parts, (ii) a zero-mean symmetric complex Gaussian input distribution, and (iii) the superposed power-splitting communication and sensing signal (the coexisting solution). In particular, the optimized input distribution balances the three functions by exhibiting the following features: (a) symbols in subcarriers with strong communication channels have high variance to satisfy the rate constraint, while the other symbols are dominated by the mean, forming a relatively uniform sum of mean and variance across subcarriers for sensing; (b) with looser communication and sensing constraints, large absolute means appear on subcarriers with stronger powering channels for higher harvested power. As a final note, the results highlight the great potential of the co-designed ISCAP system for further efficiency enhancement.

Figures (5)

• Figure 1: System and OFDM Signal Model for ISCAP.
• Figure 2: The average S-P region given different achievable rate constraints ($C_{\min}=0,~0.12,~0.47,~0.82,~1.17$ bits/s/Hz from left to right). 'OPT' (red) refers to the optimized asymmetric complex Gaussian input distribution from Section \ref{['section_opt']}. 'Symmetric' (black) refers to the optimized symmetric complex Gaussian input distribution. 'CSCG' (blue) refers to the optimized CSCG input distribution. 'Coexist' (magenta) refers to the non-co-design signal superposed by the optimal communication and sensing signals through power-splitting. Generally, 'OPT' achieves the largest S-P region, followed by the co-designed 'Symmetric' and 'CSCG', which all outperform the 'Coexist' signal. $P(Z_{\mathrm{DC}})=0$ means that the exerted rate and aISPLD constraints are not feasible at the point.
• Figure 3: (a) Left: Magnitude response of one powering channel realization, Right: Magnitude response of one communications channel realization; (b) For the channel realizations in (a), the 'OPT' input distribution corresponding to points A, B, C, and D in Fig. \ref{['fig_R_E']}. For each subcarrier (numbered 1 through 8), its complex Gaussian distribution is represented by an ellipse whose center is the mean, and whose width and height correspond to the variance of the real and imaginary parts, respectively. For detailed insights on the figures on this page, see Section \ref{['subsec:sp_region']}.
• Figure 4: (a) The average C-P region given $S_{\max}=-0.948$, comparing the 'OPT', 'Symmetric', 'CSCG' and 'Coexist' inputs. (b) The 'OPT' input constellation of points E, F, G and H in Fig. \ref{['fig_C_E']} corresponds to the channel realizations in Fig. \ref{['fig_R_E_Channel']}.
• Figure 5: S-C-P region of the 'OPT' input with $30$ dBm transmit power.

Theorems & Definitions (7)

• Definition 1: Real Gaussian Distribution
• Definition 2: Complex Gaussian Distribution
• Definition 3: Symmetric and Asymmetric Complex Gaussian Distribution
• Remark 1
• Remark 2
• Remark 3
• Remark 4