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Radio Map-Based Spectrum Sharing for Joint Communication and Sensing

Xionran Fang, Wei Feng, Yunfei Chen, Dingxi Yang, Ning Ge, Zhiyong Feng, Yue Gao

TL;DR

The paper tackles interference in distributed joint communication and sensing by leveraging a radio map to supply large-scale CSI, bypassing pilot-based high-frequency interactions. It formulates a non-convex joint power allocation problem that maximizes radar SINR $\rho_i(P_r,P_c)$ while enforcing an ergodic-rate constraint $\bar{R}(P_c,P_r) \ge R_{req}$, and solves it via a sequence of tractable convex problems using a closed-form large-scale-rate approximation $\bar{R}_{ap}$, auxiliary-function scaling, and fractional programming with a quadratic transform. The proposed iterative algorithm converges rapidly, and simulations show that radio-map predictions closely approximate full CSI performance, outperforming unilateral and curve-fitting baselines, thereby enabling loosely coupled spectrum sharing for C&S in 6G networks. The approach underscores the value of environmental awareness through radio maps for efficient interference management and scalable JCAS implementations.

Abstract

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.

Radio Map-Based Spectrum Sharing for Joint Communication and Sensing

TL;DR

The paper tackles interference in distributed joint communication and sensing by leveraging a radio map to supply large-scale CSI, bypassing pilot-based high-frequency interactions. It formulates a non-convex joint power allocation problem that maximizes radar SINR while enforcing an ergodic-rate constraint , and solves it via a sequence of tractable convex problems using a closed-form large-scale-rate approximation , auxiliary-function scaling, and fractional programming with a quadratic transform. The proposed iterative algorithm converges rapidly, and simulations show that radio-map predictions closely approximate full CSI performance, outperforming unilateral and curve-fitting baselines, thereby enabling loosely coupled spectrum sharing for C&S in 6G networks. The approach underscores the value of environmental awareness through radio maps for efficient interference management and scalable JCAS implementations.

Abstract

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.
Paper Structure (14 sections, 1 theorem, 49 equations, 12 figures, 2 algorithms)

This paper contains 14 sections, 1 theorem, 49 equations, 12 figures, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. SYSTEM MODEL
  3. RADIO MAP MODEL
  4. CHANNEL MODEL
  5. SIGNAL MODEL
  6. LARGE-SCALE-CSI-BASED POWER ALLOCATION
  7. PROBLEM FORMULATION
  8. ITERATIVE ALGORITHM
  9. CLOSED-FORM APPROXIMATION
  10. AUXILIARY-FUNCTION-BASED SCALING
  11. SIMULATION RESULTS AND DISCUSSIONS
  12. CONCLUSIONS
  13. PROOF OF LEMMA
  14. PROOF OF CONVERGENCE OF THE ITERATIVE ALGORITHM

Key Result

Lemma 1

On the condition of $N_c\geqslant M_c$, the following equation holds:

Figures (12)

  • Figure 1: Illustration of the pilot-based C&S cooperation framework and the proposed radio-map-based C&S cooperation framework.
  • Figure 2: Illustration of a distributed network consisting of distributed C&S systems. A third party named the radio map is introduced to provide the large-scale CSI.
  • Figure 3: Illustration of the working regime of the proposed radio-map-based C&S cooperation framework. The modules of the communication system, sensing system, and the radio map are depicted by the blue, green, and yellow blocks, respectively.
  • Figure 4: Illustration of the large-scale CSI maps generated by Wireless Insite, the radio map and the curve fitting method. (a) Simulation layout, (b) MLP structure (c) Real large-scale CSI calculated by Wireless Insite, (d) Radio-map-predicted large-scale CSI, and (e) Curve-fitting-method-predicted large-scale CSI. The MLP consists of a two-dimensional input layer, one-dimensional output layer, and five hidden layers with [32,64,128,64,32] neurons. The large-scale CSI maps include one million data points each. These data are obtained by dividing the layout into one million grid points and calculating the channel gain between each grid point and the BS, $\#1$, using the Wireless Insite, radio map and curve fitting method.
  • Figure 5: The number of iterations of the proposed iterative algorithm under 100 randomly generated typologies.
  • ...and 7 more figures

Theorems & Definitions (2)

  • Remark 1
  • Lemma 1