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Load Balanced ISAC Systems for URLLC Users

Shivani Singh, Amudheesan Nakkeeran, Prem Singh, Ekant Sharma, Jyotsna Bapat

TL;DR

This work addresses energy-efficient downlink CF-mMIMO ISAC for URLLC users while detecting a target. It formulates a mixed-integer non-convex program to minimize total power, incorporating transmit, static, and fronthaul components, and proposes an iterative JPALB algorithm that uses DC programming, SOC reformulations, and binary relaxation with convexification to jointly optimize power allocation and AP load balancing. The approach yields substantial power savings (approximately a 33% reduction) by turning off non-contributing APs under MRT or RZF precoding without compromising URLLC QoS or sensing SINR. The results demonstrate that JPALB effectively balances communication and sensing requirements in a scalable CF-mMIMO ISAC network, with practical implications for energy-efficient 6G deployments where fronthaul and hardware power are significant considerations.

Abstract

This paper presents an energy-efficient downlink cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) network that serves ultra-reliable low-latency communication (URLLC) users while simultaneously detecting a target. We propose a load-balancing algorithm that minimizes the total network power consumption; including transmit power, fixed static power, and traffic-dependent fronthaul power at the access points (APs) without degrading system performance. To this end, we formulate a mixed-integer non-convex optimization problem and introduce an iterative joint power allocation and AP load balancing (JPALB) algorithm. The algorithm aims to reduce total power usage while meeting both the communication quality-of-service (QoS) requirements of URLLC users and the sensing QoS needed for target detection. Proposed JPALB algorithm for ISAC systems was simulated with maximum-ratio transmission (MRT) and regularized zero-forcing (RZF) precoders. Simulation results show approximately 33% reduction in power consumption, using JPALB algorithm compared to a baseline with no load balancing, without compromising communication and sensing QoS requirements.

Load Balanced ISAC Systems for URLLC Users

TL;DR

This work addresses energy-efficient downlink CF-mMIMO ISAC for URLLC users while detecting a target. It formulates a mixed-integer non-convex program to minimize total power, incorporating transmit, static, and fronthaul components, and proposes an iterative JPALB algorithm that uses DC programming, SOC reformulations, and binary relaxation with convexification to jointly optimize power allocation and AP load balancing. The approach yields substantial power savings (approximately a 33% reduction) by turning off non-contributing APs under MRT or RZF precoding without compromising URLLC QoS or sensing SINR. The results demonstrate that JPALB effectively balances communication and sensing requirements in a scalable CF-mMIMO ISAC network, with practical implications for energy-efficient 6G deployments where fronthaul and hardware power are significant considerations.

Abstract

This paper presents an energy-efficient downlink cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) network that serves ultra-reliable low-latency communication (URLLC) users while simultaneously detecting a target. We propose a load-balancing algorithm that minimizes the total network power consumption; including transmit power, fixed static power, and traffic-dependent fronthaul power at the access points (APs) without degrading system performance. To this end, we formulate a mixed-integer non-convex optimization problem and introduce an iterative joint power allocation and AP load balancing (JPALB) algorithm. The algorithm aims to reduce total power usage while meeting both the communication quality-of-service (QoS) requirements of URLLC users and the sensing QoS needed for target detection. Proposed JPALB algorithm for ISAC systems was simulated with maximum-ratio transmission (MRT) and regularized zero-forcing (RZF) precoders. Simulation results show approximately 33% reduction in power consumption, using JPALB algorithm compared to a baseline with no load balancing, without compromising communication and sensing QoS requirements.
Paper Structure (15 sections, 21 equations, 1 figure, 1 algorithm)

This paper contains 15 sections, 21 equations, 1 figure, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Communication model
  4. Power consumption
  5. Sensing Model
  6. Problem Formulation and Proposed Solution
  7. Proposed Solution
  8. URLLC data rate constraints
  9. Sensing SINR constraints
  10. Per-communication-AP power budget constraints
  11. AP activity indicator (binary) constraints
  12. Convergence issues due to the relaxed binary constraints
  13. Iterative JPALB Algorithm
  14. Numerical Results and Discussion
  15. Conclusion

Figures (1)

  • Figure 1: (a) CDF of average total power consumed [W]; (b) power consumed by various components in the network; (c) total power consumed for varying sensing $\textsf{SINR}$$\left(\Gamma_{\text{th}}^{\text{sen}}\right)$. For all plots, $K=32, ~R=2, ~U=8, ~M=4, ~R_{\text{min}} = 2 \text{ b/s/Hz }, ~\Gamma_{\text{th}}^{\text{sen}} = 2 \text{ dB}, ~\sigma_\text{rcs}^2 = 2 \text{ dBsm}$.