The strong growth in the number of connected mobile devices has imposed new challenges in efficiently exploiting the available networks resources. Code Domain Non-Orthogonal Multiple Access (NOMA) technique appears as a tremendous efficient solution. Each device uses its assigned code to simultaneously transmit its data along with the user identifier, without any resource reservation exchange, saving precious wireless resources. However, this requires a receiver capable of blindly detecting the active users, which is highly complex. Driven by the promising superposition property of quantum architecture, the goal of this paper is to adapt and apply the quantum Grover algorithm for Active User Detection (AUD) purpose in the context of NOMA, to alleviate the search complexity. This adapted Grover’s algorithm is compared with the optimal classical Maximum Likelihood (ML) AUD receivers, as well as with the basic classical Conventional Correlation Receiver (CCR). A benchmark on the probability of AUD is assessed as a function of the Signal to Noise Ratio (SNR) of the received signal. We show that our adapted Grover’s algorithm is very promising in high SNR regime.
The strong growth in the number of connected mobile devices has imposed new challenges in efficiently exploiting the available networks resources. Code Domain Non-Orthogonal Multiple Access (NOMA) technique appears as a tremendous efficient solution. Each device uses its assigned code to simultaneously transmit its data along with the user identifier, without any resource reservation exchange, saving precious wireless resources. However, this requires a receiver capable of blindly detecting the active users, which is highly complex. Driven by the promising superposition property of quantum architecture, the goal of this paper is to adapt and apply the quantum Grover algorithm for Active User Detection (AUD) purpose in the context of NOMA, to alleviate the search complexity. This adapted Grover’s algorithm is compared with the optimal classical Maximum Likelihood (ML) AUD receivers, as well as with the basic classical Conventional Correlation Receiver (CCR). A benchmark on the probability of AUD is assessed as a function of the Signal to Noise Ratio (SNR) of the received signal. We show that our adapted Grover’s algorithm is very promising in high SNR regime.
-L'un des freins des systèmes NOMA (Non orthogonal Multiple access) est la complexité de détection des utilisateurs actifs (AUD: Active User Detection). Nous proposons dans cet article d'utiliser une version adaptée de l'algorithme quantique de Grover pour la détection des utilisateurs actifs afin de tirer partie de l'accélération apportée par la superposition d'états dans les architectures quantiques, Notre version adaptée de l'algorithme de Grover est comparée aux deux récepteurs classiques de référence : le récepteur optimal du maximum de vraisemblance (ML: Maximum Likelihood) et le récepteur basique d'intercorrelation conventionnel (CCR: Conventional Correlation Receiver). La probabilité de détection des utilisateurs actifs est évaluée en fonction du SNR (Signal to Noise Ratio) du signal reçu, où les résultats obtenus prouvent que notre algorithme de Grover adapté est très prometteur sous un faible niveau de bruit.
Sixth-Generation (6G) networks will require massive connectivity, ultra-low latency, and robust security, making reliable Active User Detection (AUD) essential for interference control and physical layer protection. This letter proposes a Grover-based physical layer security (PLS) framework for a code-domain non-orthogonal multiple access (CD-NOMA) network, where the base station employs artificial-noise (AN)-assisted beamforming and identifies the active set via Grover's quantum search algorithm. We consider two threat models: passive eavesdroppers formed by detected inactive users, and active eavesdroppers selected as the top f% most frequent transmitters among detected active users. By aligning beams and AN with the Grover-based AUD output, the proposed scheme enlarges the main-wiretap rate gap and significantly improves the average secrecy rate compared with compressive sensing and classical correlation receiver baselines, while approaching maximum-likelihood detection performance with a quadratic reduction in search complexity. The impact of the information/AN power split, the base station transmit power, and the fraction of highly active users treated as eavesdroppers on secrecy is characterized through numerical simulations, and design insights are extracted for 6G PLS under both passive and active eavesdropping.
Detecting active users in a non-orthogonal multiple access (NOMA) network poses a significant challenge for 5G/6G applications. Traditional algorithms tackling this task, relying on classical processors, have to make a compromise between performance and complexity. However, a quantum computing based strategy called quantum annealing (QA) can mitigate this tradeoff. In this paper, we first propose a mapping between the AUD searching problem and the identification of the ground state of an Ising Hamiltonian. Then, we compare the execution times of our QA approach for several code domain multiple access (CDMA) scenarios. We evaluate the impact of the cross-correlation properties of the chosen codes in a NOMA network for detecting the active user's set.
To support multiple transmissions in an optical fiber, several techniques have been studied such as Optical Code Division Multiple Access (OCDMA). In particular, the incoherent OCDMA systems are appreciated for their sim-plicity and reduced cost. However, they suffer from Multiple Access Interference (MAI), which degrades the performances. In order to cope with this MAI, several detectors have been studied. Among them, the Maximum Likelihood (ML) detector is the optimal one but it suffers from high complexity as all possibilities have to be tested prior to decision. However, thanks to the recent quantum computing advances, the complexity problem can be circumvented. As a matter of fact, quantum algorithms, such as Grover, exploit the superposition states in the quantum domain to accelerate the computation. Thus, in this paper, we propose to adapt the quantum Grover's algorithm in the context of Multi-User Detection (MUD), in an OCDMA system using non-orthogonal codes. We propose a way to adapt the received noisy signal to the constraints defined by Grover's algorithm. We further evaluate the probability of success in detecting the active users for different noise levels. Aside from the complexity reduction, simulations show that our proposal has a high probability of detection when the received signal is not highly altered. We show the benefits of our proposal compared to the classical and the optimal ML detector.
The key features of 5G, such as ultra-reliable low latency (URLLC) and massive machine-type communication (mMTC), are designed to address the need for low latency and the ability to connect a large number of devices in the IoT context. To support these constraints, mobile devices transmit information without previously establishing a connection with the base station (BS). This requires the Base Station to detect in real-time the active users (process known as Active User Detection (AUD)). With classical processors, one can employ the Maximum Likelihood (ML) method (the optimal detector, but suffers from high complexity and delay), or suboptimal ones (which are simpler, but less reliable). Meanwhile, quantum algorithms, particularly Durr and Hoyer (DHA) algorithm, addressing minimum searching problems, can significantly reduce complexity while keeping good performances. However, these algorithms were designed for generic problems, and their initialization and parameterization are blindly done. Nonetheless, we can have access to prior information on the system’s behavior. Therefore, in this paper, we aim to adapt and improve these quantum algorithms by using prior knowledge on the system for the AUD problem. We first propose a novel algorithm, the Improved Iterative Minimum Searching Algorithm (IIMSA) where we define more efficiently the parameters. Then, further enhancements of IIMSA are obtained thanks to a better initialization of the algorithms by exploiting classical preprocessing of the received signals with classical Conventional Correlation Receiver (CCR) or Zero Forcing (ZF). The obtained results show that these proposed algorithms operate more efficiently (i.e., less complexity with better accuracy).
The key features of 5G, particularly URLLC and mMTC, are designed to achieve low latency and high scalability. To support these requirements, a proposed approach for random access scenarios, known as the GF scheme, eliminates handshakes between the BS and mobile users to reduce latency and accommodate a large number of devices. However, this introduces a new challenge known as AUD, where users send messages indicating their activity status, requiring the BS to detect and decode them. Decoding these messages, however, demands high computational complexity, which increases exponentially with the number of users. Several algorithms, such as ZF and CCR, have been proposed to mitigate this complexity, but they suffer from suboptimal performance. On the other hand, the optimal solution, known as ML, performs well but suffers from high complexity. To address this, quantum algorithms, like Grover's algorithm, have been proposed due to their ability to reduce search complexity while also keep detecting active users performance better. However, Grover's algorithm requires adaptation in this context, as the optimal number of iterations depends on the number of solutions, which is always unknown in the AUD case. To address this, the BBHT and DHA algorithms have been proposed to minimize complexity when the solution is unknown, but both still exhibit relatively high computational demands. In this paper, we propose an Enhanced BBHT that aims to reduce this complexity while maintaining detection performance. Our findings demonstrate that the Enhanced BBHT reduces computational complexity while keeping the performance stable.