Source-linked AI summary
Quantum-Aided Active Device Detection in Energy-Harvesting Symbiotic Radio Networks
Remon Polus, Deemah Tashman, Soumaya Cherkaoui
TL;DR
Uplink SR needs active-device detection amid growing demand for energy- and spectrum-efficient IoT connectivity. The paper combines energy-harvesting LDS backscatter with Grover-based ADD; for K = 6, it reports 12 oracle calls versus 64 ML comparisons while closely approaching the ML benchmark.
Problem
Quantum-assisted ADD had not been investigated in SR, although accurate detection is important for decoding, interference management, and throughput.
Source: §I · p. 1Method
The system has IoT devices harvest cellular uplink energy and backscatter with LDS codes, while Grover’s algorithm searches for active devices at the base station.
Source: §I · p. 1Results
For K = 6, Grover requires 12 oracle calls versus 64 ML comparisons, a 5.3× reduction, while detection closely approaches the ML benchmark.
Source: §IV · p. 4Takeaways & Limitations
The simulations indicate potential for Grover-based detection in large-scale SR deployments; future work includes near-term quantum hardware and larger-scale IoT.
Source: §IV · p. 4, §V · p. 4
Abstract
from arXiv · showhide
Massive connectivity in next-generation networks demands energy- and spectrum-efficient solutions for large-scale Internet of Things (IoT) deployments. Symbiotic radio (SR) enables passive IoT devices to communicate by backscattering existing cellular transmissions. A key challenge in uplink SR is active device detection (ADD), which directly affects decoding reliability, interference management, and system throughput. We propose an energy-harvesting code-domain non-orthogonal multiple access (NOMA)-SR system in which IoT devices harvest energy from ambient uplink signals and backscatter information using low-density spreading (LDS) codes. To reduce the complexity of ADD, Grover's quantum search algorithm is employed, providing a quadratic reduction in oracle-query complexity over exhaustive maximum-likelihood (ML) search. Numerical results show that the proposed approach closely approaches ML performance while substantially reducing the number of search iterations, demonstrating its potential for scalable ambient IoT systems.
I. Introduction
Growing IoT connectivity heightens energy and spectrum demands, while uplink SR faces the challenge of detecting active devices. This paper proposes an energy-harvesting CD-NOMA-SR system and applies Grover’s search to ADD.
I. Introduction
- SR lets passive IoT devices backscatter ambient radio-frequency signals, reducing reliance on active RF components while adding multipath diversity for the primary link.The paper presents this coexistence as a promising approach for ambient IoT in 6G networks.Source: §I · p. 1
- In uplink SR, the base station must identify the active-device subset in real time; accurate ADD matters for decoding, interference management, and throughput.The authors state that quantum-assisted ADD had not yet been investigated in SR, where energy harvesting, backscatter channels, and multi-user interference also affect detection.Source: §I · p. 1
- The proposed system combines energy harvesting, code-domain NOMA, and LDS backscatter so IoT devices can communicate without batteries.Devices harvest energy from cellular uplink signals and backscatter information to the base station using LDS codes.Source: §I · p. 1
- Grover’s algorithm is applied to ADD under Rayleigh fading and AWGN as a lower-complexity detection approach for large-scale SR networks.The paper motivates the algorithm by its quadratic speedup for unstructured search problems.Source: §I · p. 1
II. System Model
The model has a cellular user transmit to the base station while K IoT devices harvest that uplink energy and later backscatter their information for joint decoding.
II. System Model
- During the energy-harvesting phase, K IoT devices collect energy from the cellular uplink to support battery-less operation.The frame divides into an energy-harvesting interval τ0 and a data-transmission interval T − τ0.Source: §II · p. 2
- Once sufficiently charged, devices backscatter information with distinct LDS codes, which the base station jointly decodes alongside the cellular transmission.Source: §II · p. 2
- The backscattered signals create additional propagation paths that benefit the cellular user through improved diversity and reliability at the base station.Source: §II · p. 2
A. Energy Harvesting
IoT devices harvest energy from the cellular signal, accumulate it during the harvesting interval, and activate only when the available energy meets the stated cost threshold.
A. Energy Harvesting
- Device k harvests power from the cellular signal, whose transmit power, channel to device k, and LDS chip are denoted p, h_k, and s(mN + n).Source: A. Energy Harvesting · p. 2
- Harvested energy accumulates over the energy-harvesting interval τ0, and a device activates only when E_k ≥ E_c.E_c includes reflection-coefficient adjustment energy E0 and circuit consumption p_c over the backscatter bit duration T_b.Source: A. Energy Harvesting · p. 2
- After sufficient harvesting, active devices backscatter the cellular signal and multiply their symbols by unique LDS codes to avoid mutual interference.Source: A. Energy Harvesting · p. 2
B. LDS Codes
The LDS factor matrix specifies which chips are nonzero in each codeword; the described configuration assigns unique codewords to devices and shares chips across devices.
B. LDS Codes
- The LDS factor matrix F has N rows for chips and K columns for codewords, with exactly J nonzero chips in each codeword.An entry f_n,k indicates whether chip n is nonzero in codeword k.Source: B. LDS Codes · p. 2
- The described system accommodates up to 6 devices, each assigned a unique codeword.Source: B. LDS Codes · p. 2
- In the stated configuration, each codeword has J = 2 nonzero entries and each chip is shared among 3 devices.The passage identifies 3 nonzero entries per row of the factor matrix.Source: B. LDS Codes · p. 2
C. Received Signal at the BS
The base-station signal combines the cellular direct link, IoT backscatter links, and noise. Because the direct link is typically much stronger, the receiver decodes and removes it before processing the backscatter signal.
C. Received Signal at the BS
- The received base-station signal comprises the cellular direct link, backscatter links from active IoT devices, and additive white Gaussian noise.Source: C. Received Signal at the BS · pp. 2–3
- The direct-link channel is h0, each IoT-to-base-station channel is gk, and all channels are assumed to follow Rayleigh distributions.Source: C. Received Signal at the BS · p. 2
- Because the direct-link signal is typically much stronger, the receiver first decodes the cellular signal and removes it using successive interference cancellation (SIC).Source: C. Received Signal at the BS · pp. 2–3
III. Grover’s Algorithm for Active IoT Device Detection
The detector searches the activity-pattern space with Grover’s algorithm rather than exhaustive classical detection. Its oracle marks the best-matching codeword, while the diffuser amplifies the marked state; the received signal is quantized for binary quantum circuits.
III. Grover’s Algorithm for Active IoT Device Detection
- The ADD search space grows as 2^K, making exhaustive classical detection prohibitive for large deployments; quantum superposition evaluates candidate activity patterns in parallel.Source: §III · p. 3
- The oracle marks the codeword that best matches the received superposed backscatter signal by phase-inverting states satisfying Y(x) = ν.Source: §III · p. 3
- The diffuser amplifies the marked state through inversion-about-the-mean, and iterating it with the oracle increases the probability of observing the correct solution.Source: §III · p. 3
- The search-space size is B = 2^K, representing all activity combinations, and L is the number of feasible solutions.Source: §III · p. 3
- The received signal is quantized for compatibility with binary quantum circuits, assuming unipolar symbols in {0, 1}; s sets the number of quantization levels.Source: §III · p. 3
A. Average Probability of Success
Detection success is defined by whether the estimated activity matches the true activity, and the metric is averaged over trials. Fig. 2 plots average success probability against transmit power while varying K.
A. Average Probability of Success
- Detection succeeds when each estimated device activity matches its true activity, and average probability of success is computed over Ztotal trials.Source: A. Average Probability of Success · p. 3
- Fig. 2 plots average probability of success against transmit power in dBm, varying K.Source: Figure 2 · p. 3
IV. Numerical Results
Grover-based detection approaches the ML benchmark while reducing search operations; the results also show how device count and chips per device affect detection performance.
IV. Numerical Results
- A simplified network architecture is used because classical simulation of the quantum algorithm can be computationally intensive.Simulation parameters are summarized in Table I.Source: §IV · p. 3, Table I · p. 3
- 12 oracle calls versus 64 ML comparisons are required at K = 6, a 5.3× reduction in search operations.Table II compares Grover oracle calls with exhaustive ML comparisons for K = 2, 4, and 6.Source: Table II · p. 4, §IV · p. 4
- With K = 6 and N = 6, the J = 3 configuration outperforms J = 1 as transmit power varies.The passages attribute the advantage to greater spreading diversity from allocating multiple chips to each device, improving signal distinguishability and detection reliability.Source: Figure 3 · p. 4, §IV · p. 4
- Grover-based detection closely approaches the ML benchmark while requiring quadratically fewer search iterations; at K = 6, increased multi-user interference causes some degradation, yet performance improves with transmit power.The K = 6 case demonstrates applicability to larger device sets.Source: §IV · p. 4
V. Conclusion
The conclusion presents an energy-harvesting CD-NOMA symbiotic radio framework and reports that Grover-based detection approaches the ML upper bound with fewer search iterations.
V. Conclusion
- The proposed detector closely approaches the ML upper bound while substantially reducing the number of search iterations.Source: §V · p. 4
- IoT devices harvest ambient cellular uplink energy and backscatter information using LDS codes for battery-less connectivity.Source: §V · p. 4
- Grover’s quantum search algorithm is used at the base station to identify active devices with reduced search complexity.Source: §V · p. 4
- Future work will investigate near-term quantum hardware and extensions to multi-user primary networks and larger-scale IoT deployments.Source: §V · p. 4