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Power consumption Reduction in ELAA-Assisted ISAC Systems

Xiaomin Cao, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou

TL;DR

Power consumption in extremely large antenna arrays (ELAAs) for integrated sensing and communication (ISAC) systems is addressed. The authors formulate a joint subarray activation optimization to minimize the total power consumption $P_C$ under QoS constraints for both NFUEs and FFUEs, as well as sensing beampattern gain $\kappa$. The problem is non-convex and combinatorial due to binary activation variables; they propose a successive convex approximation (SCA) based algorithm with continuous relaxation and penalty terms to obtain a high-quality suboptimal solution with polynomial complexity. Numerical results show up to 50% power reduction compared with fully activated ELAA baselines while satisfying SINR and sensing requirements. The work demonstrates a practical trade-off between beamforming gain and activation granularity in ELAA-based ISAC, enabling scalable deployments.

Abstract

In this paper, we consider power consumption reduction in extremely large antenna arrays (ELAAs) for integrated sensing and communication (ISAC) applications. Although ELAAs are critical for achieving high-resolution near-field sensing, fully activating all antenna elements in conventional digital architectures leads to prohibitive power demands. To address this, we propose an energy-efficient subarray activation framework that selects an optimal subset of subarrays to minimize the total power consumption, subject to quality-of-service (QoS) constraints for both sensing and communication. We formulate a novel optimization problem and solve it using a successive convex approximation (SCA)-based iterative algorithm. The simulation results confirm that the proposed method significantly reduces power consumption while maintaining dual-function performance.

Power consumption Reduction in ELAA-Assisted ISAC Systems

TL;DR

Power consumption in extremely large antenna arrays (ELAAs) for integrated sensing and communication (ISAC) systems is addressed. The authors formulate a joint subarray activation optimization to minimize the total power consumption under QoS constraints for both NFUEs and FFUEs, as well as sensing beampattern gain . The problem is non-convex and combinatorial due to binary activation variables; they propose a successive convex approximation (SCA) based algorithm with continuous relaxation and penalty terms to obtain a high-quality suboptimal solution with polynomial complexity. Numerical results show up to 50% power reduction compared with fully activated ELAA baselines while satisfying SINR and sensing requirements. The work demonstrates a practical trade-off between beamforming gain and activation granularity in ELAA-based ISAC, enabling scalable deployments.

Abstract

In this paper, we consider power consumption reduction in extremely large antenna arrays (ELAAs) for integrated sensing and communication (ISAC) applications. Although ELAAs are critical for achieving high-resolution near-field sensing, fully activating all antenna elements in conventional digital architectures leads to prohibitive power demands. To address this, we propose an energy-efficient subarray activation framework that selects an optimal subset of subarrays to minimize the total power consumption, subject to quality-of-service (QoS) constraints for both sensing and communication. We formulate a novel optimization problem and solve it using a successive convex approximation (SCA)-based iterative algorithm. The simulation results confirm that the proposed method significantly reduces power consumption while maintaining dual-function performance.
Paper Structure (7 sections, 16 equations, 3 figures, 1 algorithm)

This paper contains 7 sections, 16 equations, 3 figures, 1 algorithm.

Figures (3)

  • Figure 1: Convergence analysis of the proposed algorithm ($M_s\!=\!50$).
  • Figure 2: Power consumption for fixed number of antennas ($M_t=400$, $K_F=K_N$).
  • Figure 3: Power consumption with different user configurations ($S$, $M_t=400$).