Sparse Array Sensor Selection in ISAC with Identifiability Guarantees

TL;DR

This paper explores sensor selection in integrated sensing and communications (ISAC) systems, deriving bounds for codebook size and analyzing array geometries to optimize both sensing and communication performance.

Contribution

It introduces a novel framework for array sensor selection in ISAC with identifiability guarantees and derives tight bounds for codebook size in different array configurations.

Findings

ULA achieves large codebook size with minimal sensors

Nonredundant arrays have limited codebook options

Sensor inclusion criteria are critical for identifiability

Abstract

This paper investigates array geometry and waveform design for integrated sensing and communications (ISAC) employing sensor selection. We consider ISAC via index modulation, where various subsets of transmit (Tx) sensors are used for both communications and monostatic active sensing. The set of Tx subarrays make up a codebook, whose cardinality we maximize (for communications) subject to guaranteeing a desired target identifiability (for sensing). To characterize the size of this novel optimal codebook, we derive first upper and lower bounds, which are tight in case of the canonical uniform linear array (ULA) and any nonredundant array. We show that the ULA achieves a large codebook - comparable to the size of the conventional unconstrained case - as satisfying the identifiability constraint only requires including two specific sensors in each Tx subarray (codeword). In contrast, nonredundant arrays, which have the largest identifiability for a given number of physical sensors, only have a single admissible codeword, rendering them ineffectual for communications via sensor selection alone. The results serve as a step towards an analytical understanding of the limits of sensor selection in ISAC and the fundamental trade-offs therein.