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Pinching Antenna Assisted Integrated Sensing and Communication Using Quadrature RSMA
Sagnik Bhattacharyya, Shiv Kumar, Keshav Singh, Chih-Peng Li
TL;DR
The paper addresses the limited analysis of multi-user, multi-PA PASS-ISAC and develops a downlink PASS-QRSMA-ISAC framework for communication and sensing. Its analysis covers two-user and generalized settings, and simulations validate the derivations and report gains over considered benchmarks.
Problem
Multi-user, multi-PA PASS-ISAC performance analysis remains unexplored, while ISAC links face path loss, blockage, and interference challenges.
Source: §I · pp. 1–2Method
The paper combines multiple waveguide-mounted PAs with downlink Q-RSMA and develops communication and sensing models and performance analyses for two-user and generalized settings.
Source: §I · pp. 2–3, §VI · p. 15Results
Monte Carlo simulations validate the analytical results and demonstrate gains over the considered PASS-based benchmark schemes.
Source: §I · p. 4, §VI · p. 16Takeaways & Limitations
The communications-sensing trade-off is strongly governed by user distribution, power allocation, waveguide attenuation, and sensing-related parameters.
Source: §VI · p. 16
Abstract
from arXiv · showhide
This paper proposes a pinching antenna system (PASS)-assisted downlink quadrature rate-splitting multiple access (Q-RSMA)-based integrated sensing and communication (PASS-QRSMA-ISAC) framework for simultaneously supporting multi-user communication and target sensing. In the proposed framework, multiple pinching antennas (PAs) deployed along a dielectric waveguide serve communication users (CUs) distributed across a dual-room indoor environment, where line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions coexist. By integrating PASS with Q-RSMA, the proposed framework improves interference management while reducing the dependence on successive interference cancellation. The communication and sensing signal models are developed, followed by the corresponding SNR/SINR expressions. To obtain tractable analytical insights, a two-user single-PA case under ideal waveguide conditions is first analysed, where the per-user communication outage probability (COP), system COP, sensing outage probability (SOP), and ergodic sum-rate are formulated using distance statistics, CDF/CCDF characterisations, and numerical integration methods. The analysis is then extended to the generalised multi-user multi-PA scenario by developing statistical characterisations for the LoS communication, NLoS communication, and sensing channels. Based on these characterisations, the per-user COP, system COP, SOP, and ergodic sum-rate are formulated and evaluated for the generalised framework. Monte Carlo simulations validate the analytical results and demonstrate the performance gains of the proposed PASS-QRSMA-ISAC framework over the considered benchmark schemes, including PASS-RSMA-ISAC, PASS-NOMA-ISAC, and PASS-SDMA-ISAC. The results further highlight the effects of various system parameters on the proposed framework.
I. INTRODUCTION
The paper addresses the unexplored multi-user, multi-PA PASS-ISAC setting by combining flexible pinching antennas with Q-RSMA, whose signal-domain separation reduces reliance on SIC. It develops analyses for both a two-user single-PA case and a generalized multi-user, multi-PA framework, then validates them against simulations and benchmark schemes.
I. INTRODUCTION
- Existing work had largely analyzed two-user, single-PA PASS-ISAC, leaving multi-user, multi-PA performance analysis unexplored; Q-RSMA also reduces reliance on SIC under sensing-signal interference.Conventional RSMA's SIC dependence can expose decoding to error propagation, whereas Q-RSMA separates common and private streams across signal components.Source: §I · p. 2
- The paper proposes multi-PA PASS-QRSMA-ISAC to serve users across two rooms while supporting target sensing under heterogeneous LoS and NLoS propagation.The framework develops communication and sensing signal models and corresponding SNR/SINR expressions.Source: §I · pp. 2–3
- For tractable analysis, the two-user single-PA ideal-waveguide case derives per-user and system COP, SOP, and ergodic sum-rate expressions.The formulations use distance statistics, CDF/CCDF characterizations, and numerical integration, accounting for Q-RSMA common and private rates.Source: §I · p. 3
- The generalized multi-user, multi-PA analysis characterizes LoS, NLoS, and sensing channels to formulate COP, SOP, and ergodic sum-rate.Its channel characterization accounts for superposed PA contributions, waveguide attenuation and phase, and user-dependent propagation.Source: §I · p. 3
- The proposed framework's analytical results are validated by Monte Carlo simulations and show gains over the considered benchmark schemes.The benchmarks include PASS-RSMA-ISAC, PASS-NOMA-ISAC, and PASS-SDMA-ISAC in the two-user case, and PASS-RSMA-ISAC in the generalized case.Source: §I · pp. 3–4
II. SYSTEM MODEL
- The modeled indoor network places users in two adjacent rooms and target sensing in Room 1, with multiple PAs on a waveguide connected to the base station.The waveguide runs along the X-axis, and each PA is located at a specified coordinate on it.Source: §II · p. 4
A. Channel Model
The channel model combines propagation along a dielectric waveguide with wireless links from the PAs to users and the sensing target. It distinguishes LoS and NLoS communication channels and models sensing as a transmit path plus an echo-return path.
A. Channel Model
- Waveguide propagation to each PA includes distance-dependent attenuation and phase variation, and its channel vector is shared by communication and sensing transmission.The attenuation coefficient characterizes waveguide loss, while the effective refractive index determines guided wavelength.Source: A. Channel Model · p. 4
- Users in Room 1 have LoS links to the waveguide-mounted PAs, while Room 2 users have NLoS links.The effective user channel combines the PA-to-user links with the corresponding waveguide channel.Source: A. Channel Model · pp. 4–5
- Sensing uses a dominant LoS PA-to-target path and a separate target-to-PA echo-return path, with the nearest PA selected to receive the echo.The collected echo is guided back to the base station through the waveguide.Source: A. Channel Model · p. 5
- The analysis assumes perfect CSI for communication and sensing links and a fixed PA configuration within each transmission block, yielding quasi-static channels over that block.The text points to a separate PASS channel-estimation method rather than deriving it here.Source: A. Channel Model · p. 5
B. Signal Model
The signal model splits each user's data into common and private streams and maps them to separate signal components for Q-RSMA communication. A power split also reserves part of the transmission for sensing, and the receiver separates the components to recover the streams.
B. Signal Model
- Each user's data is split into common and private parts; the common parts form a combined stream, while private parts are encoded separately.The common stream is encoded using a codebook available to all users.Source: B. Signal Model · p. 5
- Q-RSMA maps the common stream to the in-phase component and the combined private streams to the quadrature component.The total transmit power is divided between communication and sensing, with the common and private communication streams assigned power coefficients.Source: B. Signal Model · p. 5
- The total transmit power is assumed to be equally distributed among the PAs, and the composite signal is radiated through those PAs along the waveguide.The power split parameter allocates a fraction to communication and the remainder to sensing.Source: B. Signal Model · p. 5
1) Communication Task:
- At each user, phase compensation precedes separation into in-phase and quadrature components for common- and private-stream processing, respectively.The model then gives the common and private stream SNR/SINR and rate expressions; real-valued symbols introduce a one-half rate scaling factor.Source: 1) Communication Task · p. 6
- The achievable user rate combines its allocated common-stream rate and private-stream rate, subject to the common-rate allocation constraint.The system sum-rate is formed from the users' achievable rates.Source: 1) Communication Task · p. 6
2) Sensing Task:
The analysis establishes a two-user, single-PA ideal-waveguide baseline and develops sensing and communication outage analysis. It covers sensing-echo assumptions, user-level COP, and system COP.
2) Sensing Task:
- Sensing uses an echo received after simultaneous PA transmission, with a common round-trip delay assumed when D ≤ c/B.The common-delay assumption follows [40]; the bandwidth is B.Source: 2) Sensing Task · p. 6
- The sensing SINR accounts for residual communication interference because hardware constraints can prevent perfect cancellation during echo recovery.The residual-interference coefficient ∆S represents hardware-induced imperfect cancellation.Source: 2) Sensing Task · p. 6
III. TWO-USER SINGLE-PA PERFORMANCE ANALYSIS
- The baseline sets L = 1, K = 2, M = 1, and κ = 0, with the PA aligned to the sensing target to shorten propagation distance and enhance the echo.The analysis assumes an ideal waveguide.Source: §III · p. 6
1) COP Analysis:
- A user is in outage when either its common or private stream cannot be decoded, so the analysis first derives user-wise COP under differing room channel statistics.User 1's COP uses a CDF-based formulation, while user 2's PDF-based piecewise integrals are approximated with first-kind Gaussian-Chebyshev quadrature.Source: 1) COP Analysis · pp. 6–7
- System outage occurs when either user is in outage, and under independent user-outage events the system COP is 1 − (1 − PO1)(1 − PO2).This follows from the inclusion-exclusion expression for the two user outage probabilities.Source: 1) COP Analysis · p. 8
2) SOP Analysis:
The sensing outage analysis defines failure by target-range estimation error and maps the CRLB-based accuracy requirement to a sensing-SINR threshold.
2) SOP Analysis:
- MSE-based SOP is the probability that target-range estimation error exceeds a prescribed threshold, distinguishing sensing outage from communication outage.The sensing estimate uses the received echo to estimate round-trip delay, which is related to target range.Source: 2) SOP Analysis · p. 8
- The CRLB-based MSE outage condition is converted to an equivalent sensing-SINR threshold, and SOP is evaluated using the CDF of the sensing SINR.The threshold is denoted τS.Source: 2) SOP Analysis · p. 8
- The SOP expression applies only when (1 − α)−ατS∆S > 0; otherwise, POS = 1.Source: 2) SOP Analysis · p. 8
B. Ergodic Sum Rate Analysis
The ergodic sum-rate analysis combines average common and private rates using SINR distribution functions, with a stated independence approximation and numerical integration.
B. Ergodic Sum Rate Analysis
- The common rate is averaged over fading and limited by the weaker of the two users' common-stream decoding capabilities.Its expression uses the users' common-stream CCDFs and their joint CDF.Source: B. Ergodic Sum Rate Analysis · p. 8
- The common-rate calculation can approximate the joint distribution by treating the two users' common-stream SINRs as independent.The passage reports negligible dependence and close agreement between simulated and analytical results under this assumption.Source: B. Ergodic Sum Rate Analysis · p. 9
- Each user's private rate is determined by that user's own decoding capability, without the weaker-user constraint applied to the common rate.The private-rate analysis uses the achievable common- and private-rate expressions in the sum-rate calculation.Source: B. Ergodic Sum Rate Analysis · p. 9
- The corresponding ergodic sum-rate integrals are evaluated numerically with the trapezoidal rule because the expression is intractable.The ergodic sum-rate is defined as the average achievable sum-rate over fading or channel realizations.Source: B. Ergodic Sum Rate Analysis · pp. 8–9
- The derived ergodic rates apply only where the denominators of the δ-based terms remain positive; outside that region, the associated CCDF terms are zero.Source: B. Ergodic Sum Rate Analysis · p. 9
IV. MULTI-USER MULTI-PA PERFORMANCE ANALYSIS
The generalized analysis extends the framework to multiple users and PAs, using tractable channel characterizations to formulate outage and ergodic sum-rate performance.
IV. MULTI-USER MULTI-PA PERFORMANCE ANALYSIS
- The generalized setting jointly serves multiple users across two rooms using multiple PAs.Source: §IV · p. 9
- Because the exact effective-channel distribution is intractable, the analysis assumes independent PA locations and independent PA-level channel contributions.The effective channel includes waveguide attenuation, phase variation, and user-dependent propagation distances.Source: §IV · p. 9
- Under these approximations, LoS communication and sensing channels use moment matching, while NLoS communication channels use a PA-conditioned semi-analytical characterization.These characterizations support the outage-probability and ergodic sum-rate analyses.Source: §IV · p. 9
1) Channel Characterization of CUs:
Because summing PA contributions produces a channel distribution that is difficult to derive exactly, the analysis conditions on user location and uses room-specific channel characterizations.
1) Channel Characterization of CUs:
- For Room 2 users, the analysis conditions on both CU and PA locations, averages Rayleigh fading in closed form, then numerically integrates over locations.This PA-conditioned evaluation avoids replacing location-dependent channel power with its spatial average, which may mismatch finite-L cases.Source: 1) Channel Characterization of CUs · pp. 9–10
- For tractability, PA locations are modeled independently without a hard minimum-spacing constraint; imposing λ/2 spacing removes few configurations and negligibly affects evaluated channel statistics.The stated negligible impact applies to the considered system parameters.Source: 1) Channel Characterization of CUs · p. 9
- For Room 1 users, the conditioned summed channel is approximated as complex Gaussian, making its power gain non-central chi-square with two degrees of freedom.The moment-matched approximation captures the summed channel’s mean and variance.Source: 1) Channel Characterization of CUs · p. 10
- Room 1 channel randomness is primarily from PA locations and deterministic LoS phases, whereas Room 2 also includes Rayleigh small-scale fading.The distinction motivates the moment-matched approximation for Room 1 and conditional fading average for Room 2.Source: 1) Channel Characterization of CUs · pp. 9–10
2) Channel Characterization for Sensing:
The sensing-channel analysis characterizes the transmit-side channel by conditioning on target location and models the echo return through the PA nearest the target.
2) Channel Characterization for Sensing:
- Conditioned on target location, moment matching approximates the transmit-side sensing channel as complex Gaussian and characterizes its power-gain distribution.The spatially averaged CDF is obtained by averaging over target locations.Source: 2) Channel Characterization for Sensing · pp. 10–11
- The echo return is collected through the selected PA nearest the target, so its return-side gain is deterministic for fixed target and selected-PA locations.The effective sensing-channel power combines transmit-side power, target reflectivity, and return-side gain.Source: 2) Channel Characterization for Sensing · p. 10
- Nearest-PA selection depends on horizontal separation from the target; its vertical coordinate affects only the return-side channel gain.The selected PA location is characterized with a conditional density, then spatial averaging yields the sensing-channel distribution.Source: 2) Channel Characterization for Sensing · pp. 10–11
B. Outage Analysis
The multi-user multi-PA outage analysis derives communication outage from common- and private-stream decoding and evaluates system communication and sensing outage using spatially averaged channel characterizations.
B. Outage Analysis
- A user is in communication outage if either its common stream decoded in-phase or its intended private stream decoded in-quadrature is in outage.The per-user COP is characterized conditionally on user location and then averaged over the user’s room.Source: B. Outage Analysis · p. 11
- The common- and private-stream target rates determine their respective outage thresholds; infeasible threshold conditions yield per-user COP of one.The thresholds are defined from each stream’s target data rate.Source: B. Outage Analysis · p. 11
- System COP is approximated using representative Room 1 and Room 2 user outage probabilities and the number of users in each room.This uses the assumption that users within a room have statistically identical spatially averaged outage probabilities.Source: B. Outage Analysis · p. 11
- SOP uses the sensing-SINR threshold associated with the CRLB-based target-range estimation criterion, with spatial averaging evaluated by the trapezoidal rule.If the stated sensing-threshold feasibility expression is non-positive, SOP equals one.Source: B. Outage Analysis · p. 11
C. Ergodic Sum Rate Analysis
The multi-PA ergodic sum-rate analysis approximates common and private rates with CCDF-based integrals evaluated numerically. Simulations closely match the analytical results, and QRSMA-PASS has a sum-rate advantage over RSMA-PASS and NOMA-PASS in the practical high-power region with imperfect SIC.
C. Ergodic Sum Rate Analysis
- Average common and private rates are approximated using CCDF-based integrals, which are evaluated numerically with the trapezoidal rule.The multi-PA framework’s ergodic sum-rate evaluation uses channel-gain distributions derived earlier in the paper.Source: C. Ergodic Sum Rate Analysis · p. 11
V. RESULTS AND DISCUSSION
- Simulation results closely match the analytical results for per-user COP, system COP, sum rate, and SOP.The comparisons include PASS-RSMA-ISAC, PASS-NOMA-ISAC, and PASS-SDMA-ISAC benchmarks.Source: §V · p. 11
A. Communication Task Performance
- QRSMA-PASS achieves the best per-user COP for both users; the Room 2 NLoS user has higher outage than the Room 1 LoS user.Higher transmit power decreases both users’ COP.Source: Figure 2 · p. 12, A. Communication Task Performance · p. 12
- In the multi-user multi-PA scenario, QRSMA-PASS outperforms RSMA-PASS across K, while increasing K worsens COP for both frameworks.Room 1 users have better COP than Room 2 users because their links are LoS-dominant rather than NLoS.Source: Figure 3 · p. 13, A. Communication Task Performance · p. 13
- With ∆>0, QRSMA-PASS achieves better sum-rate performance than RSMA-PASS and NOMA-PASS in the practical high-power region.Under perfect SIC, QRSMA-PASS has lower sum rate than RSMA-PASS; its advantage over NOMA-PASS appears at higher transmit power.Source: Figure 4 · p. 13, A. Communication Task Performance · p. 14
- Sum rate first rises and then falls with βC as power shifts between the common and private streams.The common-rate gain initially outweighs private-rate loss; beyond the optimal region, reduced private-stream power dominates.Source: A. Communication Task Performance · p. 14
- Figure 5 presents sensing outage probability (SOP) performance with respect to transmit power Pt; its caption does not specify the direction of the trend or any waveguide conditions.Source: Figure 5 · p. 14
B. Sensing Task Performance
The section examines sensing outage across system conditions and the communication–sensing power split in PASS-QRSMA-ISAC. Its conclusion reports validation of the analysis and gains over the considered PASS benchmarks.
B. Sensing Task Performance
- Analytical and simulated SOP closely match under perfect and imperfect interference cancellation; SOP falls as transmit power rises, while stricter sensing requirements worsen reliability.A larger ρTh relaxes the sensing accuracy requirement and improves SOP.Source: B. Sensing Task Performance · p. 14
- SOP decreases as σRCS increases; nonzero κ and imperfect cancellation worsen it, with their effects more visible at higher transmit power.Source: Figure 6 · p. 15, B. Sensing Task Performance · p. 15
- In multi-PA sensing, larger κ, room dimension D, or imperfect interference cancellation worsens SOP; larger D amplifies attenuation by increasing the in-waveguide path length.Source: B. Sensing Task Performance · p. 15
C. ISAC Performance
- Increasing α eventually lowers system COP but raises SOP, and imperfect cancellation widens the SOP gap at higher α.Fig. 7 plots system COP on Y1 and SOP on Y2 against α.Source: Figure 7 · p. 15, C. ISAC Performance · p. 15
VI. CONCLUSION
- Monte Carlo simulations validate the analysis and demonstrate QRSMA gains over the considered benchmark schemes within PASS.The reported communication–sensing trade-off depends on user distribution, power allocation, waveguide attenuation, and sensing-related parameters.Source: §VI · pp. 15–16
- Future work targets imperfect CSI and mobility-induced channel variations, optimization-based PA and power design, and more advanced sensing tasks.Source: §VI · p. 16