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Design and Performance Evaluation of Joint Sensing and Communication Integrated System for 5G MmWave Enabled CAVs

Qixun Zhang, Xinna Wang, Zhenhao Li, Zhiqing Wei

arXiv:2108.10535v2eess.SYcs.SY

TL;DR

CAVs must share large volumes of raw sensor data to extend sensing, but efficient delivery is challenging. This paper proposes a time-division JSCIS and models resource allocation for one and multiple vehicles. CTRA improves radar total mutual information by 26%, while hardware testing demonstrates JSCIS feasibility at 28 GHz.

  • Problem

    Multiple vehicle sensors generate raw data over several Gbps, making efficient sharing among vehicles a challenge.

    Source: §I · p. 1

  • Method

    The paper designs a time-division JSCIS with dynamic frame allocation, using AoI-based M/M/1 analysis for one vehicle and a non-cooperative game with CTRA for multiple CAVs.

    Source: §I · p. 2

  • Results

    26% improvement in radar total mutual information over conventional algorithms; the hardware testbed reports ranging accuracy within ±0.25 m and a stable 2.8 Gbps at 28 GHz.

    Source: §I · p. 2

  • Takeaways & Limitations

    The hardware testbed demonstrates the feasibility of the proposed JSCIS with ranging accuracy within ±0.25 m and stable 2.8 Gbps data rate at 28 GHz.

    Source: §I · p. 2

Abstract

from arXiv · show

The safety of connected automated vehicles (CAVs) relies on the reliable and efficient raw data sharing from multiple types of sensors. The 5G millimeter wave (mmWave) communication technology can enhance the environment sensing ability of different isolated vehicles. In this paper, a joint sensing and communication integrated system (JSCIS) is designed to support the dynamic frame structure configuration for sensing and communication dual functions based on the 5G New Radio protocol in the mmWave frequency band, which can solve the low latency and high data rate problems of raw sensing data sharing among CAVs. To evaluate the timeliness of raw sensing data transmission, the best time duration allocation ratio of sensing and communication dual functions for one vehicle is achieved by modeling the M/M/1 queuing problem using the age of information (AoI) in this paper. Furthermore, the resource allocation optimization problem among multiple CAVs is formulated as a non-cooperative game using the radar mutual information as a key indicator. And the feasibility and existence of pure strategy Nash equilibrium (NE) are proved theoretically, and a centralized time resource allocation (CTRA) algorithm is proposed to achieve the best feasible pure strategy NE. Finally, both simulation and hardware testbed are designed, and the results show that the proposed CTRA algorithm can improve the radar total mutual information by 26%, and the feasibility of the proposed JSCIS is achieved with an acceptable radar ranging accuracy within 0.25 m, as well as a stable data rate of 2.8 Gbps using the 28 GHz mmWave frequency band.

I. INTRODUCTION

The paper addresses the challenge of sharing high-volume raw sensor data among vehicles and proposes a time-division JSCIS for 5G mmWave CAVs. It evaluates single-vehicle timing with AoI and packet loss, and multiple-vehicle allocation with radar mutual information.

I. INTRODUCTION

  • The time-division JSCIS uses a dynamic 5G NR frame structure so vehicles can allocate sensing and communication durations according to service requirements.The design targets low-latency raw sensing-data delivery among CAVs.Source: §I · p. 2
  • For multiple CAVs, the paper formulates time-resource allocation as a non-cooperative game that maximizes radar total mutual information and proves feasibility and existence of a pure-strategy Nash equilibrium.The CTRA algorithm is designed to find the best feasible equilibrium.Source: §I · p. 2
  • The proposed CTRA algorithm improves radar total mutual information by 26% over conventional algorithms; hardware testing reports ranging accuracy within ±0.25 m and a stable 2.8 Gbps at 28 GHz.The authors use software simulation to evaluate CTRA and a hardware testbed to demonstrate JSCIS feasibility.Source: §I · p. 2

II. SYSTEM MODEL AND PROBLEM FORMULATION

  • For one vehicle, the paper models the source-destination link with M/M/1 queuing and uses packet-loss probability and average AoI to determine a suitable sensing-to-communication duration ratio.The system model treats average AoI as a function of server load rate under packet loss probability.Source: §I · p. 2, §II · p. 3

A. Scenario Description

  • Vehicle A combines radar detections with mmWave links to obtain information about vehicles that are outside its sensing range or in its blind spot.The scenario treats vehicle A as the central vehicle for information processing and resource configuration.Source: A. Scenario Description · p. 3

B. Dynamic Frame Structure Design

The design uses an adjustable 5G NR frame to allocate time between sensing and communication. Radar mutual information (MI) is used to evaluate radar estimation performance.

B. Dynamic Frame Structure Design

  • 5G NR supports subcarrier intervals up to 120 kHz above 6 GHz, and shorter time slots can reduce transmission delay.The NR subframe is 1 ms, and its number of time slots is an integer.Source: B. Dynamic Frame Structure Design · p. 3
  • The P/Q-adjustable frame assigns subframes to sensing and communication, with radar information sent in the next communication subframe.Flexible mini-slot combinations support low-latency emergency information, while ordinary time slots serve ordinary data.Source: B. Dynamic Frame Structure Design · pp. 3–4

1) Radar Metric:

2) Communication Metric:

The paper defines communication capacity and formulates dynamic time allocation for vehicles. Its radar-duration analysis accounts for interference between radar and communication signals when evaluating radar MI.

2) Communication Metric:

  • For an AWGN channel, capacity is the maximum information transmission rate based on Shannon’s theorem, and the paper expresses capacity over time T.The communication metric uses bandwidth B and SINR Γ_com.Source: 2) Communication Metric · p. 4

D. Problem Formulation

  • Each vehicle dynamically adjusts sensing time within a fixed frame according to its sensing and communication requirements.The sensing durations are ordered, and vehicle synchronization for interference analysis is supported by 5G NR PSS and SSS.Source: D. Problem Formulation · pp. 4–5

1) Signal Analysis During Radar Detection Process:

  • During radar sensing, lower-index vehicles contribute communication-signal interference and higher-index vehicles contribute radar-signal interference.The analysis combines these interference types to derive SINR and radar MI for each vehicle and total radar MI across N vehicles.Source: 1) Signal Analysis During Radar… · pp. 5–6

2) Signal Analysis During Communication Transmission Process:

The communication-duration analysis models interference from other vehicles according to their time-allocation indices, then uses the resulting SINR to characterize communication capacity.

2) Signal Analysis During Communication Transmission Process:

  • During vehicle i’s communication interval, lower-index vehicles cause communication-signal interference, while higher-index vehicles cause radar-signal interference.The passage distinguishes the interference sources by whether the other vehicle’s allocation index is smaller or larger than n.Source: 2) Signal Analysis During… · p. 6
  • The two interference types are combined to express vehicle i’s communication SINR for its allocated interval.The communication interference depends on transmission power and path loss; radar interference depends on distance between vehicles.Source: 2) Signal Analysis During… · p. 6
  • The communication rate and channel capacity are defined for vehicle i, and total channel capacity is considered across N cooperatively sensing vehicles.The total communication-capacity expression follows the individual rate and capacity definitions.Source: 2) Signal Analysis During… · p. 6

3) AoI Analysis:

The paper evaluates raw sensing-data timeliness with average AoI in an M/M/1 queue and adjusts the load calculation for packet loss.

3) AoI Analysis:

  • AoI measures information timeliness, and the paper uses average AoI to select a vehicle’s sensing-to-communication duration ratio.The analysis also examines how duration ratios affect packet loss probability and average AoI.Source: 3) AoI Analysis · p. 6
  • The system models multiple vehicle sources sending detection information to a central vehicle through a packet-loss FCFS queue.Each source generates packets according to a Poisson process with rate Λ, and packet service time is exponential with rate μ; server load is ρ=Λ/μ.Source: 3) AoI Analysis · p. 7
  • The average AoI is evaluated for the M/M/1 queue, with packet loss incorporated into the server load rate as ρ′ = p Λ.A transmission succeeds only when receiver SINR is at least γ_c, and the packet-loss rate is 1−p.Source: 3) AoI Analysis · p. 7

4) Objective Function:

The objective is to maximize radar information while accounting for communication capacity. The paper frames JSCIS performance as a constrained, multi-objective design problem.

4) Objective Function:

  • JSCIS performance is described as a dual-objective problem that optimizes radar and communication performance and seeks Pareto optimality.The cited passages identify radar total mutual information as the objective and communication channel capacity as a constraint.Source: 4) Objective Function · pp. 7–8
  • The optimization maximizes radar total mutual information subject to a communication channel capacity constraint.The stated goal is to maximize the information detected by radars.Source: 4) Objective Function · pp. 7–8
  • Constraint C1 provides each vehicle’s available radar-duration set, while C2 requires radar information to be transmitted within communication time T.C3 limits data-transmission efficiency below a threshold for successful packet transmission.Source: 4) Objective Function · p. 8

III. TIME RESOURCE ALLOCATION METHOD BASED ON NON-COOPERATIVE GAME

The paper casts multi-CAV time allocation as a non-cooperative game and proves that a feasible pure-strategy Nash equilibrium exists. It proposes CTRA to reach the equilibrium with low complexity and fast convergence.

III. TIME RESOURCE ALLOCATION METHOD BASED ON NON-COOPERATIVE GAME

  • The resource-allocation problem for multiple CAVs is formulated as a non-cooperative game, with feasibility and existence of a pure-strategy NE proved.The paper also proves CTRA convergence and proposes the algorithm to achieve the NE.Source: §III · p. 8
  • CTRA is proposed to achieve the game’s NE while reducing allocation complexity.The paper characterizes its convergence as fast and proves convergence theoretically.Source: §III · p. 8
  • The proposed game’s pure-strategy NE is shown to be feasible and to exist.These theoretical properties support using an equilibrium as the allocation target.Source: §III · p. 8

A. The Formulation of the Non-cooperative Game

The multi-CAV allocation problem is combinatorial and non-convex, so the paper formulates it as a non-cooperative game with a utility that balances radar information against communication capacity. Under stated conditions, it proves the game has feasible pure-strategy equilibria.

A. The Formulation of the Non-cooperative Game

  • The allocation problem is combinatorial and non-convex, making exhaustive optimization computationally infeasible for CAVs.The paper therefore uses a non-cooperative game to reduce complexity and select a suitable allocation strategy.Source: A. The Formulation of the… · p. 8
  • Radar and communication durations affect each other, motivating a game in which vehicles choose time-allocation strategies independently.The vehicles are players, and each player’s strategy is its radar-duration proportion in each frame.Source: A. The Formulation of the… · p. 8
  • Each vehicle seeks to maximize its utility, whose first term is radar total mutual information and whose penalty reflects insufficient communication capacity.The penalty applies when communication channel capacity is smaller than radar mutual information.Source: A. The Formulation of the… · p. 8
  • When η exceeds the maximum radar mutual information and feasible C1/C2 strategies exist, violating C2 yields negative utility and cannot maximize the objective.Under those conditions, maximizing utility is equivalent to optimizing the objective in (26).Source: A. The Formulation of the… · p. 8
  • The game is a potential game with potential function U, but the C2 constraint makes feasible equilibrium existence require proof.The passage notes that potential games have at least one pure-strategy NE absent this constraint.Source: A. The Formulation of the… · p. 8

B. The Feasibility of Pure Strategy Nash Equilibrium

  • If η ≥ η̃, scheduling cycles have enough time slots, and a player’s C2 adjustment does not affect others, every pure-strategy NE is feasible.The feasibility analysis assumes enough time slots for each scheduling cycle.Source: B. The Feasibility of Pure Strategy… · p. 8
  • With enough slots, a vehicle violating C2 can reduce radar duration until its radar information is below communication data, contradicting equilibrium.The proof uses that unilateral adjustment to show an infeasible profile cannot be a pure-strategy NE.Source: B. The Feasibility of Pure Strategy… · pp. 8–9
  • The section then turns from feasibility under specified conditions to establishing whether a feasible pure-strategy NE exists.The stated theorem conditions include η ≥ η̃ and enough time slots per scheduling cycle.Source: B. The Feasibility of Pure Strategy… · pp. 8–9

C. The Existence of pure strategy Nash Equilibrium

Under η ≥ η̃ and enough time slots, the game has at least one feasible pure-strategy NE. The paper relates utility maximization to the radar objective and describes CTRA’s initialization and update steps.

C. The Existence of pure strategy Nash Equilibrium

  • η ≥ η̃ and enough time slots ensure the game has at least one feasible pure-strategy NE.The proof argues that a utility-maximizing profile must be feasible and then yields a strategy profile satisfying the NE condition.Source: C. The Existence of pure strategy Nash… · p. 9
  • When η ≥ η̃, the utility at the maximizing profile equals its radar objective, making that profile optimal among feasible strategy profiles.The proof states that no feasible profile can improve the utility further.Source: C. The Existence of pure strategy Nash… · p. 9
  • CTRA initializes vehicle strategies and tracks the highest utility during its iterations.The provided algorithm excerpt gives an initialization and a utility-based update.Source: C. The Existence of pure strategy Nash… · p. 9
  • If there is a single pure-strategy NE, that equilibrium is Pareto optimal when Theorem 2’s condition holds.This conclusion is conditional on uniqueness and the theorem’s conditions.Source: C. The Existence of pure strategy Nash… · p. 9

D. CTRA Algorithm Design Based on Non-cooperative Game

CTRA starts from a feasible allocation and uses best responses to converge to a feasible pure-strategy Nash equilibrium under stated conditions. Its finite-step convergence follows from strictly increasing potential utility, and the evaluation includes simulation and hardware testing.

D. CTRA Algorithm Design Based on Non-cooperative Game

IV. RESULTS AND ANALYSIS

  • The evaluation uses software simulation and a hardware testbed to assess allocation ratios, algorithm performance, and JSCIS feasibility.Source: §IV · p. 10

A. Software Simulation Setup and Results Analysis

Simulations evaluate CTRA against alternative allocation methods and examine how traffic load, scheduling cycles, vehicle count, and radar cross-section affect performance. CTRA outperforms the comparison methods in radar total mutual information, while allocation choices also balance packet loss, AoI, and sensing information delivery.

A. Software Simulation Setup and Results Analysis

B. Hardware Setup and Testbed Results Analysis

The 28 GHz hardware testbed implements radar sensing and raw-data transmission in separate stages using a 5G NR frame structure. Tests report ranging accuracy within ±0.25 m alongside a stable 2.8 Gbps throughput, while accuracy improves with increased radar-slot allocation.

B. Hardware Setup and Testbed Results Analysis

V. CONCLUSION

  • The conclusion reports simulation gains for CTRA and hardware feasibility in the 28 GHz band, with ranging accuracy within ±0.25 m and stable 2.8 Gbps data rate.Source: §V · p. 14
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