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Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey

Nguyen Cong Luong, Xiao Lu, Dinh Thai Hoang, Dusit Niyato, Dong In Kim

arXiv:2007.13146v3eess.SPcs.NI

TL;DR

JRC must manage interference and shared hardware, spectrum, and energy while preserving radar and communication performance. This survey synthesizes JRC fundamentals, resource-management approaches, security countermeasures, challenges, and future directions. It identifies lessons across waveform, power-allocation, antenna, interference, and security studies, while emphasizing continuing practical constraints.

  • Problem

    JRC sharing of spectrum, hardware, and energy creates interference and complicates transmitter design and the simultaneous performance of radar and communication functions.

  • Method

    The paper conducts a comprehensive survey of JRC fundamentals, performance metrics, applications, spectrum sharing, power allocation, interference cancellation, security issues, and countermeasures.

  • Results

    The survey reports that distributed-antenna DFRC can outperform collocated systems through spatial diversity, while waveform and power-allocation choices remain constrained by interference and PAPR issues.

  • Takeaways & Limitations

    JRC resource management must jointly consider radar, communication, energy, spectrum, interference, security, and implementation requirements.

Abstract

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Joint radar and communication (JRC) has recently attracted substantial attention. The first reason is that JRC allows individual radar and communication systems to share spectrum bands and thus improves the spectrum utilization. The second reason is that JRC enables a single hardware platform, e.g., an autonomous vehicle or a UAV, to simultaneously perform the communication function and the radar function. As a result, JRC is able to improve the efficiency of resources, i.e., spectrum and energy, reduce the system size, and minimize the system cost. However, there are several challenges to be solved for the JRC design. In particular, sharing the spectrum imposes the interference caused by the systems, and sharing the hardware platform and energy resource complicates the design of the JRC transmitter and compromises the performance of each function. To address the challenges, several resource management approaches have been recently proposed, and this paper presents a comprehensive literature review on resource management for JRC. First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems. Then, we review and analyze resource management approaches, i.e., spectrum sharing, power allocation, and interference management, for JRC. In addition, we present security issues to JRC and provide a discussion of countermeasures to the security issues. Finally, we highlight important challenges in the JRC design and discuss future research directions related to JRC.

I. INTRODUCTION

JRC addresses spectrum congestion and enables radar and communication functions to share spectrum, hardware, and energy resources. This survey reviews JRC fundamentals, resource-management approaches, security issues, and future challenges.

  • Spectrum congestion and growing wireless-device demand motivate reuse or sharing of spectrum with radar and other systems.
  • JRC can improve spectrum and energy efficiency, reduce system size, and minimize system cost while supporting military and civilian applications.
  • Sharing spectrum can create interference, while sharing hardware and energy complicates transmitter design and can degrade system-function performance.
  • Existing surveys insufficiently cover JRC fundamentals, power allocation, security, countermeasures, updated technologies, and emerging research topics.
  • The survey provides JRC fundamentals and applications, reviews spectrum sharing, analyzes power allocation and interference management, and discusses security countermeasures.
  • The paper organizes related studies around spectrum sharing, power allocation, and interference management to support focused review of approaches and open issues.

A. Radar Technology

Radar detects and characterizes objects by transmitting radio waves and analyzing reflected signals. The section introduces radar architecture, operating steps, and metrics for range, velocity, resolution, and waveform analysis.

  • Radar transmits radio waves and analyzes echoes to determine target distance, direction, velocity, shape, and material.
  • Architecture and Main Components: A typical radar includes a transmitter, receiver, switch and antenna, and controller; the transmitter emits signals and the receiver processes returned echoes.
  • Architecture and Main Components: Radar receivers amplify weak signals, shift them to intermediate frequency, and extract information for subsequent processing.
  • Target Identification and Radar Range: Round-trip signal travel time determines target range, and changes in measured range over time can estimate target velocity.
  • Target Identification and Radar Range: Radar range depends on transmitted and received power, antenna gains, wavelength, target scattering, propagation factor, and distance.
  • Higher transmitted-pulse bandwidth improves range resolution, while longer chirp duration improves velocity resolution.
  • The ambiguity function characterizes propagation-delay and Doppler relationships and supports radar-waveform analysis and design.

B. Joint Radar-Communication Approaches

The survey presents three radar-communication integration approaches: separate operation, switching between functions, and more integrated sharing. Their trade-offs concern independence, implementation simplicity, simultaneous operation, and timing decisions.

  • Separate antennas and frequencies let radar and communication operate independently, but require additional hardware and may be costly for civilian systems.
  • A switch can select radar or communication operation without redesigning existing waveforms, providing a simple integration mechanism.
  • Switch-based integration permits only one function at a time, making the timing of radar and communication activation a central decision.
  • Reinforcement learning has been introduced to find real-time operating decisions, but its performance depends strongly on sensor accuracy.

3) Dual function radar communication systems:

Dual-function radar communication integrates communication and radar functions by sharing transmitted waveforms. The surveyed approaches embed data in radar waveforms or radar processing in communication waveforms, including FMCW and OFDM implementations.

  • Integration approaches: Dual-function JRC integrates radar and communication functions into the same transmitted signals through radar-waveform-based or communication-waveform-based solutions.The first embeds communication data in radar signals; the second integrates radar functionality into conventional communication waveforms.
  • Radar waveform-based: Radar-waveform-based methods modify radar signals to carry digitally modulated data symbols.For FMCW radar, data can be embedded by replacing each pulse with a phase-rotated version using continuous phase modulation or differential QPSK.
  • Radar waveform-based: Frequency-modulation-based data embedding uses positive chirp rates for bits “1” and negative rates for bits “0”, but provides low communication rates dependent on pulse repetition interval.
  • Communication waveform-based: Communication-waveform-based JRC commonly uses OFDM because it is used in both radar and communication systems and supports flexible integration.Assigning transmitted symbols to separate subcarriers can reduce high sidelobes after matched filtering.
  • FMCW radar waveform: FMCW radar generates chirps, mixes transmitted and received signals, and processes the resulting intermediate-frequency signal.Its components include a waveform generator, transmitter, receiver, analog-to-digital converter, mixer, and low-pass filter.
  • OFDM radar waveform: OFDM radar uses cyclic prefixes to prevent inter-symbol interference, forms frames from multiple OFDM symbols, and derives received signals over a time-frequency selective radar channel.The channel model includes complex gain, round-trip Doppler shift, and delay; an ambiguity function can determine Doppler and delay.

D. Performance Metrics of JRC Systems

JRC performance evaluation must reflect both communication and radar functions. The paper identifies data communication rate and radar estimation rate as the two key metrics.

  • The two key JRC performance metrics are data communication rate and radar estimation rate.

1) Data Communication Rate:

Data communication rate depends on transmission resources, hardware efficiency, and channel conditions. Radar estimation rate is also treated as a JRC performance metric and is related to target-prediction residuals and channel degradation.

  • Data communication rate: Communication rate measures the number of data bits transmitted to the receiver and is determined from transmit power and channel-related parameters.The formulation includes noise-to-channel-gain efficiency, bandwidth, and transmission efficiency.
  • Factors affecting communication rate: Bandwidth allocation creates a trade-off between radar and communication activities under frequency sharing.Time-sharing and signal-sharing approaches can use the full bandwidth for both functions, whereas frequency sharing divides the allocated bandwidth.
  • Factors affecting communication rate: Transmit power must be allocated between concurrent radar and communication functions when both share one energy source.
  • Factors affecting communication rate: Transmission efficiency depends on JRC hardware configuration, while channel condition depends on environmental factors such as channel gain and noise.
  • Radar estimation rate: Radar estimation rate is evaluated from the information needed to encode Kalman residuals under a degraded radar channel.The formulation uses radar estimation information and a pulse repetition interval related to pulse duration and duty factor; Gaussian residual assumptions introduce an estimation variance.
  • Radar estimation rate: Radar process noise represents information extracted from the target using prior observations.

E. Potential Application Scenarios and Implementation Challenges of JRC Systems

JRC applications span military and civilian platforms that combine radar detection with communications, while sharing spectrum and hardware creates interference and resource-management challenges.

  • Applications: JRC applications include shipborne, airborne, ground-based, autonomous vehicular, indoor Wi-Fi, and UAV systems.The supplied application material organizes these uses across military and civilian scenarios.
  • Military Applications: Airborne JRC systems support simultaneous long-distance communication and object detection, but integrating communications with look-down/shoot-down radar still needs further investigation.Airborne platforms move faster than shipborne systems and commonly detect targets below the radar horizon.
  • Civilian Applications: Automotive radar operating at 77/79 GHz can detect and recognize objects up to 250 meters, supporting driver-assistance capabilities for autonomous vehicles.The passage links this detection range to capabilities required for a five-star Euro NCAP rating.
  • Civilian Applications: Civilian UAV JRC combines short-range communications and sensing with radar-based collision avoidance for buildings, trees, and other flying objects.Commercial collision-avoidance radars cited for UAVs operate at 77 GHz and 24 GHz.
  • Implementation Challenges: JRC resource sharing requires communication-signal, radar-signal, time-division, or spatial-beamforming approaches to manage spectrum, interference, and radar–communication trade-offs.The approaches differ in whether they share signals, divide time, or project radar signals into communication-channel null spaces.

A. Communication Signal-Based Approaches

Communication-signal-based JRC uses spread-spectrum or OFDM waveforms for radar probing, but data randomness and high PAPR motivate code-assisted and alternative waveform designs. The section also considers OTFS for channels with high Doppler frequencies and other challenging conditions.

  • Spread-Spectrum Waveform: Spread coding supports radar detection through pseudorandom sequences with favorable autocorrelation properties.Spread-spectrum and OFDM are identified as common communication signals for radar probing.
  • OFDM Waveform: OFDM improves spectral efficiency through parallel transmission on orthogonal, partially overlapping subcarriers and supports robustness against multipath fading.The passage also identifies easy synchronization, equalization, and flexibility as OFDM advantages.
  • Communication-Signal-Based Approaches: Communication-signal-based approaches use standard signals such as OFDM for radar probing while carrying data to a remote receiver.Reflected communication signals provide target information to the radar subsystem.
  • OFDM Waveform: Data-dependent OFDM correlation can create high range-profile sidelobes, making target peaks harder to distinguish and reducing detection accuracy.The pioneering OFDM JRC scheme shares transmitted OFDM signals with the radar function, exposing this data-dependence issue.
  • OFDM Waveform: 300 m/s target velocity was clearly detected by a proposed OFDM-based scheme designed to address data-dependent correlation effects.This is the reported simulation outcome for the cited scheme.
  • OFDM Waveform: Golay-coded OFDM lowers BER and ambiguity-function sidelobes relative to original OFDM, improving communication and radar performance.Golay coding is used to eliminate data dependency while adding error-correction capability.
  • OFDM Waveform: High OFDM PAPR can drive transmit circuitry into saturation and cause signal distortion, motivating OCDM-OFDM waveform design.The OCDM-OFDM scheme reserves chirps for peak cancellation while embedding communication data in other chirps.
  • OTFS Waveform: OTFS extends OFDM and CDMA for channels with high Doppler frequencies, massive MIMO, or mmWave operation by providing near-constant channel gain.The supplied passage identifies OTFS as an emerging modulation technique for JRC.

B. Radar Signal-Based Approaches

Radar signal-based approaches embed communication data into radar emissions, using frequency-hopping or chirp waveforms alongside time-division designs. These methods balance spectrum sharing, sensing accuracy, communication performance, data rate, and implementation complexity.

  • Frequency-hopping signal: Frequency-hopping approaches embed PSK-modulated communication symbols into orthogonal radar waveforms by phase-modulating FH codes within each radar pulse.The construction generates M orthogonal waveforms, with each of Q FH codes representing a communication symbol, embedding MQ symbols per pulse.
  • Frequency-hopping signal: FFT-based detection achieves communication BER up to 10^-6 at SNR −9 dB while avoiding CSI estimation and reducing receiver complexity.The receiver partitions the signal into Q non-overlapping sub-pulses, estimates dominant frequencies, and detects the embedded symbols.
  • Frequency-hopping signal: Costas hopping waveforms with frequency-diverse-array transmission improve communication SER and radar SINR over an FH baseline, but require phase synchronization.The FDA provides additional transmit and receive degrees of freedom and helps distinguish targets with identical angles but different ranges.
  • Frequency-hopping signal: Using more pairs of orthogonal waveforms increases embedded data to 15 bits per pulse versus 8 bits for the baseline at the same BER and SNR.The scheme transmits multiple bit sequences on different waveform pairs rather than one sequence per radar pulse.
  • Chirp signal: Chirp-based designs combine LFM with OFDM or MSK, but MSK-LFM can exceed the original radar bandwidth for certain data patterns, increasing leakage and degrading performance.MSK-LFM retains a constant envelope, yet its spectrum depends on the data bits; all-zero or all-one patterns can expand the occupied bandwidth.
  • Chirp signal: LDPC coding with BCJR decoding improves BER by around 1.8 dB over the baseline, while introducing high latency.The LDPC-coded symbol sequence is embedded into the LFM signal before transmission and decoded jointly with CPM demodulation.
  • Time-division approaches: Time-division schemes improve communication rate and sensing accuracy, while RA-CSMA improves spectral efficiency and communication range in vehicle networks.RA-CSMA senses channel status and reserves frames, with performance close to ideal TDMA but possible repeated access attempts.

D. Spatial Beamforming

Spatial beamforming manages JRC coexistence by shaping signals or allocating antenna elements between radar and communication functions. Antenna allocation can improve channel capacity and radar resolution, while spectrum-sharing choices still involve function-specific trade-offs.

  • Beamforming design: Spatial beamforming projects radar signals into the null space of the channel to the communication receiver to reduce coexistence interference.The approach is typically used in coexistent radar and communication systems.
  • Beamforming design: Channel selection for military radar and LTE base-station coexistence estimates interference-channel CSI using blind null-space learning before selecting operating channels.The considered systems share the 3.5−3.6 GHz bands.
  • Antenna allocation: Antenna allocation assigns transmit elements between radar and communication functions, while sparse-array design provides another spatial beamforming strategy.The reviewed antenna-allocation scheme is examined for DFRC systems.
  • Antenna allocation: The proposed antenna-allocation scheme achieves lower radar-resolution CRB than spatially partitioned antennas when radar receiving antennas are limited.Its transmit-element combinations also increase channel capacity through spatial bits compared with traditional MIMO.
  • Trade-offs: Spectrum-sharing methods must preserve both radar and communication requirements, and their suitability depends on whether communication or radar performance is the primary objective.Communication-signal approaches can improve spectrum efficiency, whereas radar-signal approaches may provide lower communication data rates.
  • Trade-offs: OFDM-based approaches can improve spectrum efficiency but incur high PAPR and costly hardware, whereas LFM approaches simplify hardware but limit detection range through lower peak output power.The survey therefore emphasizes evaluating each approach’s advantages and disadvantages before selecting a design.

IV. POWER ALLOCATION

Power allocation in DFRC systems jointly allocates a shared transmit-power budget between radar and communications while balancing their performance objectives. The reviewed studies extend this problem across multiuser, vehicular, distributed, wireless-powered, and security-aware settings.

  • Power allocation in DFRC systems: DFRC power allocation uses one shared transmit-power budget for radar and communication signals, unlike conventional communication systems with separate transmitter budgets.The objective is joint radar–communication performance rather than communication performance alone.
  • Extended DFRC settings: Other reviewed designs allocate power during channel training, support multiple users, harvest RF energy before transmission, or minimize communication interception probability.These extensions address channel-estimation accuracy, multiuser transmission, external energy supply, and low-probability-of-intercept requirements.
  • Multiuser DFRC: RSMA-based DFRC can outperform SDMA by up to 48% in weighted sum rate while achieving a better weighted-sum-rate–mean-squared-error tradeoff.The common stream reduces interference at transmit beampattern angles compared with SDMA.
  • Vehicular DFRC: Vehicular DFRC beamforming enables multiple-target detection with a communication-rate decline of less than 5% at a 30-meter target distance while maintaining detection probability one.The design assumes constant target velocity and directional line-of-sight communication links.
  • Distributed and wireless-powered DFRC: Distributed DFRC systems use spatially separated transmit and receive antennas to jointly support communication and target localization.The design objective is expressed through Shannon capacity and the Cramér–Rao bound.

2) Power Allocation in CRC Systems:

Power allocation in CRC systems configures radar and communication transmit resources under shared-spectrum constraints, including single- and multiuser, multicarrier, and imperfect-CSI settings. The reviewed results show that resource allocation must balance radar and communication objectives rather than optimize mutual information in isolation.

  • Power allocation in CRC systems: CRC power-allocation studies optimize radar SINR, mutual-information rate, detection probability, or communication throughput subject to communication, interference, and transmit-power constraints.The literature includes single-user and multiple-user systems with perfect or imperfect channel-state information.
  • Multicarrier CRC: Overlapping subcarrier allocation can provide significant gains in radar mutual information and communication rate compared with non-overlapping allocation.Sequential optimization is used to iteratively obtain radar and communication transmit powers.
  • Lessons learned: RSMA power allocation can achieve higher spectrum and energy efficiency than SDMA and NOMA with both perfect and imperfect sender-side CSI.Rate splitting can decode partial interference through common and private streams.
  • Lessons learned: Distributed-antenna DFRC systems outperform collocated-antenna counterparts by exploiting spatial diversity.The review also identifies the communication-to-radar mean-path-loss ratio as important for the range of achievable mutual information.
  • Lessons learned: Multicarrier JRC waveforms increase frequency diversity and mitigate fading and multipath effects, but their time-varying subcarrier envelopes create a PAPR problem for practical power amplifiers.The PAPR constraint is therefore an important design issue for multicarrier JRC power allocation.
  • Lessons learned: A greater maximized mutual information does not necessarily indicate optimal communication or detection performance because maximizing communication mutual information can reduce radar mutual information.This tradeoff is reported for multicarrier JRC power allocation.

V. INTERFERENCE MANAGEMENT

Interference management in JRC addresses cross-interference affecting both radar and communication, using non-cooperative, cooperative, or co-designed system information and processing strategies. The reviewed methods reduce interference through covariance design, sparse estimation, beamforming, and cancellation, while retaining explicit scope limitations.

  • Overview: JRC interference management mitigates cross-interference between radar and communication rather than only interference among communication transmissions.CRC designs are classified as non-cooperative, cooperative, or co-designed according to information exchange and joint subsystem configuration.
  • CRC systems: Co-designed CRC sampling and covariance optimization reduces effective interference power by at least 20% compared with cooperative and non-cooperative designs.The performance gain increases with radar sampling rate, but the gap between the low-complexity and optimal co-design solutions is not investigated.
  • CRC systems: Compressed-sensing interference-removal methods improve communication demodulation in CRC systems, while the two-stage method achieves more than one order of magnitude lower symbol error rate and two orders of magnitude lower computation time than the convex-relaxation method.The comparison is reported for QPSK and a single-radar setting with fully overlapping bandwidth.
  • CRC systems: A two-stage mmWave beamformer mitigates cross-interference between colocated radar and communication transmitter-receivers under perfect channel-state information.The design first subtracts mmWave signals from the radar and then mitigates radar signals at the communication receiver.
  • DFRC: DFRC interference cancellation is more challenging than CRC cancellation because communication signals are correlated with radar target returns and must be reconstructed before subtraction.This correlation arises because both signals originate from the same source.
  • DFRC: Selective cancellation outperforms serial cancellation in processing time and radar dynamic range, while cancelling an interferer’s line-of-sight path can improve radar performance by up to 34 dB.These OFDM DFRC methods rely on reconstructing selected or pilot-derived interfering signals.
  • DFRC: OFDM DFRC cancellation results can represent performance upper bounds because prior methods neglect secondary interference and assume perfect frequency offset.Reference addresses frequency-offset estimation errors using a combined two-dimensional fast Fourier transform and one-dimensional search approach.

C. Summary and Lessons Learned

The survey finds that JRC interference differs across system types and requires techniques matched to channel knowledge, interference structure, hardware limits, and security threats.

  • CRC interference management: Null-space projection can eliminate radar interference, but imperfect CSI may misalign the projection and impair detection.Accurate CSI is required for effective use.
  • CRC interference management: Communication systems can subtract known radar waveforms, but intercepted waveform information enables jammers to design harmful radar interference.Information sharing therefore requires protection alongside interference mitigation.
  • DFRC interference management: In DFRC systems, communication performance is affected by cumulative cochannel interference, whereas radar performance is mainly limited by the strongest interferer.
  • DFRC interference management: DFRC transceivers need sufficient dynamic range and analog-to-digital resolution so mutual interference does not distort digitized signals.
  • DFRC interference management: Greater antenna directionality can extend radar field coverage while reducing overall detectable range when interference levels increase.Antenna directionality should therefore be configured with interference mitigation in mind.
  • Security approaches: Security studies address jamming and eavesdropping using secrecy optimization, artificial noise, and radar-communication designs involving eavesdropping targets or radar receivers.The surveyed approaches include secrecy-rate optimization, radar-SINR maximization, transmit-power minimization, Taylor approximation, and alternating optimization with semidefinite relaxation.

B. Summary and Lessons Learned

The survey identifies encryption, physical-layer security, artificial noise, and game-theoretic defense as responses to JRC attacks, while emphasizing synchronization, multipath, access, and signal-separation challenges.

  • Security lessons: DFRC radar signals can carry data that radar targets eavesdrop, so encryption is proposed to protect transmitted information.
  • Security lessons: Physical-layer security with power allocation can reduce eavesdropper decodability while preserving the performance requirements of both radar and communication functions.Encryption can increase radar-transmitter and communication-receiver hardware complexity.
  • Security lessons: Beamformed pseudorandom distortion can reduce eavesdropper decodability and benefit target detection, but requires target-location information and is applicable to single-target scenarios.
  • Security lessons: Game theory can model simultaneous jamming and eavesdropping, helping JRC systems learn or predict attack strategies and select defenses through equilibrium analysis.
  • Open implementation challenges: LFM-OFDM integration faces dechirp-timing and multipath challenges because frequency shifts across subcarriers can cause inter-carrier interference and non-orthogonality.Accurate receiver synchronization and advanced frequency-estimation methods are needed to address these effects.
  • Open implementation challenges: Future JRC systems must address massive access and distinguish target echoes from communication signals arriving from many mobile users under noise and interference.

2) Location-Dependent Resource Management:

Location, mobility, and spatial distributions are important but incompletely modeled dimensions of JRC resource management, especially for dynamic autonomous-vehicle environments and emerging integrated systems.

  • Location-dependent resource management: Most reviewed JRC studies do not explicitly model component locations, leaving the effects of mobility in targets and communication receivers insufficiently characterized.Mobility introduces temporal changes into system behavior.
  • Location-dependent resource management: Stochastic geometry can model the randomness of coexisting radar, communication, and JRC systems operating on the same frequency band.Such modeling supports analysis of large-scale spatial effects on JRC performance and resource-management design.
  • Location-dependent resource management: Static time allocation between radar and communication is inadequate for autonomous vehicles because their surrounding environments are uncertain and dynamic.
  • Location-dependent resource management: DFRC-equipped vehicles may require substantial spectrum to sense traffic and transmit large sensing files such as images or videos.
  • Location-dependent resource management: Security research commonly treats separate threats, but full-duplex active eavesdroppers can combine eavesdropping and jamming under uncertain attack patterns and locations.Reinforcement learning and deep reinforcement learning are proposed for finding defense strategies.
  • Emerging integrations: Future work should integrate JRC with intelligent reflecting surfaces and edge computing while jointly considering communication, radar, and computing performance.IRS can provide passive beamforming with negligible energy consumption, whereas edge offloading can consume bandwidth and affect JRC functions.
  • Conclusion: JRC deployment remains constrained by synchronization, dechirp timing, multipath effects, and security because radar and communication signals differ in waveform and performance requirements.The survey nevertheless identifies JRC as promising for military and civilian applications because it performs both functions simultaneously while improving resource efficiency and reducing cost.
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