---
title: Joint Communications, Sensing and PNT
url: https://www.emergentmind.com/topics/joint-communications-sensing-and-pnt-jcsap
type: topic
---

# Joint Communications, Sensing and PNT

Searching arXiv for recent and foundational papers on JCSAP and closely related JCAS/ISAC formulations to ground the article.
Joint Communications, Sensing, and PNT (JCSAP) denotes the full-service convergence of communications, sensing, and positioning, navigation, and timing on a single platform or payload, with shared front-ends, spectrum, and, in the most integrated form, jointly designed waveforms and processing [2509.25937]. Within the multi-functional satellite systems taxonomy, JCSAP is the highest level of integration, whereas terrestrial and maritime realizations often emphasize the same underlying principle: the same beams, pilots, and baseband processing can support data delivery, target/environment sensing, and PNT observables such as time-of-arrival, Doppler, angle, and time transfer [2501.05243]. The field therefore spans payload co-design, receiver and precoder optimization, waveform design, statistical performance analysis, and system-level resource management across cellular, mmWave, maritime, and non-terrestrial networks [2607.11164].

## 1. Taxonomy and scope

The literature distinguishes JCSAP from partial integrations rather than treating all integrated sensing-and-communication systems as equivalent [2509.25937].

| Class | Integrated functions | Characterization |
|---|---|---|
| JCAS | Communications and sensing | Shared spectrum/hardware and coordinated waveforms |
| JCAP | Communications and PNT | Communications payload supports or embeds PNT |
| JSAP | Sensing and PNT | Sensing payloads are fused with PNT services |
| JCSAP | Communications, sensing, and PNT | Full-service convergence on a single platform/payload |

The convergence level itself spans three design modalities. In a **cooperative** design, co-located functions share hardware but retain distinct waveforms or bands. In an **integrated** design, functions share band or hardware with time, space, or code multiplexing to avoid harmful interference. In a **joint** design, a single multifunctional signal and receiver chain are co-designed to satisfy all three functions simultaneously [2509.25937]. This distinction is important because many papers instantiate only one or two parts of the full JCSAP stack.

A common misconception is that any JCAS realization is automatically a JCSAP realization. The record is more specific. For example, the multi-modal dynamic spectrum access system with image and wireless data modalities is explicitly a joint communications-and-sensing system for transmitter identification, while “PNT is not covered” and “PNT is explicitly out of scope” [2312.13931]. By contrast, the joint receiver framework for integrated sensing and communications explicitly maps delay, Doppler, and angle estimates to range, radial velocity, bearing, timing, and multilateration, thereby supplying a direct pathway from sensing outputs to PNT observables [2211.05535].

## 2. Architectural integration and hardware realizations

At payload level, JCSAP architectures are typically described in terms of a shared RF front-end and antenna aperture, a partition into RF Unit and Digital Processing Unit, an On-Board Digital Processor for software-defined routing and scheduling, and software-defined payloads with multi-band transceivers and programmable channelizers [2509.25937]. These building blocks support cooperative, integrated, and joint operation while exposing the core system constraints: power, bandwidth, beam agility, synchronization, and regulatory masks.

Several terrestrial architectures illustrate how these principles are realized under different hardware assumptions. A half-duplex 6G design uses a common half-duplex base station that transmits downlink OFDM and a dedicated FMCW receiver co-located at the BS for sensing, thereby avoiding in-band full-duplex OFDM sensing and its severe self-interference burden [2201.00941]. In that framework, random time-division restores the sensing-only unambiguous Doppler span while using only a fraction \(1/M\) of sensing occasions, and flexible sensing-implanted OFDM places sensing on \(1/M\) of the subcarriers while leaving \((M-1)/M\) for data [2201.00941]. By contrast, a full-duplex MIMO architecture with a near-field RIS jointly optimizes digital beamformers and RIS phase configuration to handle self-interference while improving sensing and communications performance [2306.10865].

Programmable propagation surfaces have become a major branch of the field. STAR-RIS introduces simultaneous transmission and reflection with energy splitting, mode switching, and time switching, together with per-element transmission and reflection coefficients obeying \(\alpha_n^t+\alpha_n^r=1\) in energy-splitting operation [2602.09589]. Holographic JCAS replaces conventional array abstractions with a reconfigurable holographic surface whose analog beamforming matrix is \(W=\mathrm{diag}(w)\Phi\), where \(0\le w_i\le 1\), and uses arbitrary inter-element spacing to increase spatial control and derive exact Cramér–Rao Bounds for azimuth and elevation [2502.15248]. A plausible implication is that JCSAP hardware is no longer defined only by antenna count, but by how precisely the platform can shape beams, multipath, and timing geometry across communications and sensing tasks.

A further hardware direction is low-complexity hybrid beamforming. One mmWave design combines wideband analog beamforming for fine ToA extraction and narrowband digital beamforming for high-rank AoA estimation, avoiding the aggregate sampling rate of fully digital wideband arrays [2309.06850]. In the reported example with \(N=16\) and \(B_A=400\) MHz, the proposed architecture uses approximately \(800\) MS/s versus \(6.4\) GS/s for a fully digital implementation, with approximately \(87.5\%\) ADC power reduction [2309.06850]. This is directly relevant to JCSAP because PNT-grade delay and angle extraction must often coexist with stringent power and form-factor constraints.

## 3. Signal models, estimation theory, and PNT observables

A central JCSAP theme is that communication and sensing signals are not merely coexisting; they are statistically coupled in the receiver and in the estimators. A representative receive model is
\[
\mathbf{y}(n)=\mathbf{h}_c s(n)+\alpha(n)\mathbf{g}x(n)+\mathbf{z}(n),
\]
where a communication stream and a target echo are simultaneously present at the receiver [2211.05535]. In that formulation, the communication detector is built from a whitening matrix \(Q=R_c^{-1/2}\) and a maximal-ratio combining vector \(w=Qh_c\), while sensing can be performed either by interference cancellation followed by linear MMSE or by a tailored non-IC MMSE estimator that marginalizes over the discrete communication constellation [2211.05535]. The reported conclusion is precise: the Non-IC design is independent of the detector, avoids error propagation inherent in IC, and achieves optimal sensing MMSE while the ML detector remains optimal for communications [2211.05535].

The PNT mapping is explicit in several formulations. With estimated delay \(\hat{\tau}\), monostatic range is
\[
R=\frac{c\,\hat{\tau}}{2},
\]
and with Doppler \(\hat{f}_D\), radial velocity is
\[
v=\frac{\lambda\hat{f}_D}{2}=\frac{c\hat{f}_D}{2f_c}.
\]
Angle can be extracted by array processing, for example with MUSIC,
\[
P_{\text{MUSIC}}(\theta)=\frac{1}{\mathbf{a}^H(\theta)\mathbf{E}_n\mathbf{E}_n^H\mathbf{a}(\theta)},
\]
and position can then be formed by multilateration or by direct angle–range mapping, depending on geometry [2211.05535]. Time-transfer and synchronization enter through ToA and two-way timing models, and the timing accuracy is commonly summarized by bounds such as
\[
\mathrm{var}(\hat{\tau})\ge \frac{1}{8\pi^2\beta^2\mathrm{SNR}},
\]
which reappears in both terrestrial and satellite JCSAP discussions [2509.25937].

Another line of work formulates sensing performance directly in information-theoretic terms. In stochastic-geometry analysis of mmWave JCAS networks, sensing coverage probability is defined as the probability that the rate of information extracted about the parameters of interest exceeds a threshold, and a sensing SINR surrogate is constructed from the Fisher Information Matrix and mutual-information bounds [2210.02289]. This broadens the performance lens beyond classical detection or estimation error and is especially relevant to JCSAP because PNT accuracy is itself an estimation problem governed by the FIM, CRLBs, and geometry.

## 4. Representative system realizations

The application space of JCSAP is heterogeneous, and the literature contains several concrete realizations that illustrate different integration mechanisms.

A task-oriented dynamic spectrum access system couples camera images with wireless sensing returns for transmitter identification [2312.13931]. An edge device captures a \(32\times 32\times 3\) image, compresses it from dimension \(3072\) to \(n_{c,1}=20\), transmits a channel-optimized feature vector, receives a sensing return generated by reflections of that same waveform, re-encodes the sensing signal into \(n_{c,2}\) symbols, and lets a fusion-center decoder make the binary decision [2312.13931]. Under the default setting, the reported accuracy is \(0.97\) in AWGN and \(0.88\) in Rayleigh, with joint sensing and image-task features consistently outperforming sensing-only baselines across SNRs [2312.13931]. The system is archetypal JCS, but it also illustrates a boundary condition of the broader field: the paper explicitly excludes PNT.

OFDM-based mmWave JCS has been pushed toward super-resolution with subspace methods. A MUSIC-based downlink system operating at \(63\) GHz with \(8\times 8\) BS UPAs, \(N_c=256\), \(M_s=64\), and \(B=122.88\) MHz replaces grid-limited FFT sensing with sequential AoA, range, and Doppler MUSIC processing, then uses the sensed delay in a Kalman filter to enhance CSI for communications demodulation [2211.04064]. The reported gains are more than \(20\) dB in sensing MSE relative to FFT baselines, together with substantial BER reductions when CSI enhancement is used [2211.04064]. Because AoA, range, and Doppler are directly estimated, the same outputs naturally support 3D localization and tracking.

Waveform design has also been used to recover sensing capability lost in conventional time-sharing. In half-duplex 6G JCS, random time-division and flexible sensing-implanted OFDM recover the sensing-only unambiguous Doppler span while sharing resources with communications, a property termed super sensing range [2201.00941]. With \(f_c=60\) GHz, \(B=122.88\) MHz, and \(M=4\), the design preserves \(v_{\max}\approx 1080\) km/h for sensing-only, RTD, and FSI-OFDM, whereas periodic TDD at \(1/M\) sensing fraction reduces the unambiguous speed to approximately \(270\) km/h [2201.00941]. The same framework also uses CP-assisted dual-window processing to extend range coverage without lengthening the symbol.

At network level, multi-cell coordination changes the sensing–communications trade-off. In a multi-cell MISO downlink with block-level precoders, coordinated beamforming treats inter-cell reflections as interference, whereas coordinated multi-point treats them as known signal contributions, leading to a lower azimuth-angle CRB at the same minimum-SINR target [2402.18405]. The paper’s simulations show that neglecting inter-cell reflections degrades angle estimation, and that CoMP consistently outperforms CBF in sensing while also improving communication performance [2402.18405]. This is directly relevant to JCSAP because multi-anchor angle estimates are fundamental to geometric localization.

Non-terrestrial realizations make the PNT dimension explicit. An integrated satellite-terrestrial maritime system uses a shore-based TBS and a LEO satellite that jointly provide communications and bistatic target sensing in the VHF band with \(f_c=160\) MHz and \(B=15\) MHz [2607.11164]. The system derives a CRB for target position through Jacobians from \((\theta,\phi,\tau)\) to \((x,y,z)\), then solves a joint beamforming optimization that maximizes user sum rate subject to localization-accuracy and power constraints [2607.11164]. The architecture uses GNSS pulse-per-second synchronization between the TBS and satellite, and the sensing algorithm is based on differential evolution for angle estimation [2607.11164]. In this case, communications, sensing, and PNT are all explicit components of the formulation.

## 5. Performance metrics and trade-offs

JCSAP is intrinsically multi-objective, and its metrics span link-layer, sensing, and navigation domains. Communications is commonly quantified through BER, SINR, achievable rate, or weighted sum-rate, for example
\[
C=B\log_2(1+\mathrm{SNR}),
\qquad
\mathrm{SINR}=\frac{P_r|h|^2}{N_0B+I},
\]
while sensing is evaluated through detection probability, false alarm probability, MSE, SCNR, or CRB/SPEB-type bounds [2509.25937]. PNT metrics add pseudorange error, localization RMSE, GDOP or PDOP, velocity RMSE, timing error, and Allan deviation for clock stability [2509.25937].

The trade-offs are rarely monotone. In multi-cell coordinated JSC, the relative CRB grows rapidly as the minimum-SINR requirement increases because more power must be steered to users and away from sensing directions [2402.18405]. In holographic JCAS, a weighted multi-objective design explicitly maximizes communication rate while minimizing \(\mathrm{CRB}(\theta_t)+\mathrm{CRB}(\phi_t)\), thereby tracing different Pareto points by changing the weights \(\alpha\) and \(\beta\) [2502.15248]. In STAR-RIS-enabled systems, throughput, SCNR, CRB, and fairness are coupled through transmission/reflection coefficients, phase quantization, and hardware losses [2602.09589].

There are also non-obvious interference interactions. In multi-user MIMO JCAS, the use of the communication signal for sensing can prevent a loss in communication performance if channel interference occurs, whereas the kurtosis of the transmit alphabet limits sensing performance [2601.20647]. The analytical result is that sensing should illuminate a sector with the single stream having the lowest kurtosis, and in overlapping sectors the affected UE’s communication signal can be reused for sensing to avoid an explicit interference term in the SINR [2601.20647]. This result links waveform statistics directly to the communications–sensing operating point.

System geometry matters just as much as waveform or precoder design. In a stochastic-geometry framework for mmWave JCAS, network densification improves sensing SINR performance—in contrast to communications [2210.02289]. The explanation given in that work is tied to the steeper two-way path loss of radar: shrinking link distances improves the desired sensing path faster than interference rises, whereas downlink communication becomes increasingly interference-limited in dense deployments [2210.02289]. For JCSAP, this means that communications-optimal and PNT-optimal network densification need not coincide.

## 6. Limitations, controversies, and open directions

A recurring controversy concerns what “integration” should mean in practice. Some designs avoid in-band full-duplex entirely by adding a dedicated FMCW sensing chain to a half-duplex BS, arguing that aggressive self-interference suppression before ADC is otherwise required because echo power may be \(100\) dB below self-interference [2201.00941]. Other designs embrace full-duplex and use SI-aware RIS or joint beamformer/RIS optimization to loosen the requirement for additional SI cancellation [2306.10865]. The literature therefore does not converge on a single hardware doctrine; it instead exposes a design spectrum shaped by complexity, dynamic range, and calibration.

Another persistent limitation is realism. Many papers rely on AWGN, Rayleigh, LoS, or single-target models, and several explicitly identify the need for richer models of clutter, multipath, hardware impairments, calibration drift, and synchronization error [2312.13931]. Maritime and satellite studies emphasize rain attenuation, Earth-curvature LoS boundaries, LEO Doppler, feeder-link coordination, and multilayer synchronization because tiny timing errors lead to meter-level ranging errors [2501.05243]. STAR-RIS and holographic formulations add further realism gaps, including amplitude–phase coupling, phase quantization, mutual coupling, insertion losses, and the difficulty of acquiring cascaded CSI under mobility [2602.09589].

PNT itself remains unevenly integrated across the literature. Some works derive direct mappings from delay, Doppler, and angle to range, velocity, timing, and localization [2211.05535]; others use CRB or SPEB formulations that can be propagated to position error bounds [2502.15248]; and still others remain strictly JCAS despite being highly relevant to JCSAP [2312.13931]. This suggests that a mature JCSAP system requires not only shared signals and hardware, but also explicit state estimation, synchronization, and integrity models.

The open directions are correspondingly broad. Surveys identify waveform co-design under hardware nonidealities, synchronization under extreme LEO dynamics, interference-aware resource management, AI-native controllers, digital twins, cooperative satellite constellations, reconfigurable surfaces, and JCSAP-specific standardization gaps as major unresolved problems [2509.25937]. Satellite studies add multi-target detection, multilayer clock management, ISL-assisted time transfer, and robust TDOA/FDOA fusion [2501.05243]. A plausible implication is that the field is moving from pairwise integrations toward system-level convergence, where communications reliability, sensing fidelity, and PNT integrity must be optimized together rather than treated as adjacent objectives.

Source: https://www.emergentmind.com/topics/joint-communications-sensing-and-pnt-jcsap