Sparse Array Sensor Selection in ISAC with Identifiability Guarantees
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ISACMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Array-Informed Waveform Design for Active Sensing: Diversity, Redundancy, and Identifiability
This paper investigates the combined role of transmit waveforms and (sparse) sensor array geometries in active sensing multiple-input multiple-output (MIMO) systems. Specifically, we consider the fundamental identifiabil…
DiversitySparse Array Design for Direction Finding using Deep Learning
In the past few years, deep learning (DL) techniques have been introduced for designing sparse arrays. These methods offer the advantages of feature engineering and low prediction-stage complexity, which is helpful in ta…
Deep LearningFeature EngineeringISACTransfer LearningSensor Selection via GFlowNets: A Deep Generative Modeling Framework to Navigate Combinatorial Complexity
The performance of sensor arrays in sensing and wireless communications improves with more elements, but this comes at the cost of increased energy consumption and hardware expense. This work addresses the challenge of s…
Integrated sensing and communicationISACNavigateSparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning
Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complex…
Direction of Arrival EstimationTransfer LearningAntenna Array Design for Mono-Static ISAC
Mono-static sensing operations in ISAC perform joint beamforming between transmitter and receiver. However, in contrast to pure radar systems, ISAC requires to fulfill communications tasks and to retain the corresponding…
ISAC