There are different access systems proposed for next-generation wireless communications. As the number of users in wireless systems increases, the challenge of reliable communications can be faced with the aid of exhaustive search-based signal detection. However, exhaustive maximum-likelihood searches impose a high complexity. In classical communication systems, performance improvement is achieved at the cost of substantially increased computational complexity. In this study, we consider an OFDMA downlink and SC-FDMA uplink scenario. We propose the employment of quantum search methods for signal detection. Specifically, we employ Grover's algorithm for database search in signal detection. We observe from our simulations that the error performance provided by the classical ML search is attainable by our method. As a benefit of using quantum search, the complexity imposed is substantially reduced compared to using classical ML detectors. We also compare the performance of the proposed scheme with Grover's search-based MC-CDMA detector. We investigate with multiple users as well and find that the quantum search-based scheme is effective in improving performance, while the computational burden is very low.
There are different access systems proposed for next-generation wireless communications. As the number of users in wireless systems increases, the challenge of reliable communications can be faced with the aid of exhaustive search-based signal detection. However, exhaustive maximum-likelihood searches impose a high complexity. In classical communication systems, performance improvement is achieved at the cost of substantially increased computational complexity. In this study, we consider an OFDMA downlink and SC-FDMA uplink scenario. We propose the employment of quantum search methods for signal detection. Specifically, we employ Grover's algorithm for database search in signal detection. We observe from our simulations that the error performance provided by the classical ML search is attainable by our method. As a benefit of using quantum search, the complexity imposed is substantially reduced compared to using classical ML detectors. We also compare the performance of the proposed scheme with Grover's search-based MC-CDMA detector. We investigate with multiple users as well and find that the quantum search-based scheme is effective in improving performance, while the computational burden is very low.
Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is considered as candidates for future broadband wireless services. In this paper a novel signal detection scheme based on Grover's quantum search algorithm is proposed for MIMO-OFDM systems. Grover's quantum search algorithm is based on the concept and principles of quantum computing, such as quantum bit, quantum register and quantum parallelism. An analysis is given to the theoretical basis of Grover's algorithm and the performance of Grover's algorithm is evaluated. A novel signal detector based on Grover's algorithm (GD) for MIMO-OFDM system is proposed. The simulation results show that the proposed detector has more powerful properties in bit error rate than MMSE detector and VBLAST-MMSE detector. The performance of the proposed GD detector is close to optimal when the failure probability is 0.001. When the failure probability is 0.00001, the performance of GD detector declines. In this case, our proposed improved Grover's algorithm based detector is still close to the optimal ML detector. The complexity of GD and IGD is O(√N). It's much better than classical ML detector which complexity is O(N).
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.
Multi-Carrier Code Division Multiple Access (MC-CDMA) is considered as one of the major techniques used in 4G broadband wireless services.It combines the advantages of the OFDM systems, of robustness to the multi path effects, and the advantages of CDMA systems, which are high privacy and security.However, a problem appears in the detection of transmitted information because of the effect of noise, fading and other multipath effects.In this paper, Grover's quantum search algorithm based MC-CDMA detector is proposed as a solution for this problem.Grover's quantum search algorithm is based on the concepts of quantum computing, such as quantum bit, quantum register and quantum parallelism.The performance of the proposed detector was realized and compared with previous works.The simulation results showed the superiority of the proposed detector in BER performance.The performance of the proposed detector showed that it is very close to the optimum.
The high complexity of numerous optimal classic communication schemes, such as the maximum likelihood (ML) multiuser detector (MUD), often prevents their practical implementation. In this paper, we present an extensive review and tutorial on quantum search algorithms (QSA) and their potential applications, and we employ a QSA that finds the minimum of a function in order to perform optimal hard MUD with a quadratic reduction in the computational complexity when compared to that of the ML MUD. Furthermore, we follow a quantum approach to achieve the same performance as the optimal soft-input soft-output classic detectors by replacing them with a quantum algorithm, which estimates the weighted sum of a function's evaluations. We propose a soft-input soft-output quantum-assisted MUD (QMUD) scheme, which is the quantum-domain equivalent of the ML MUD. We then demonstrate its application using the design example of a direct-sequence code division multiple access system employing bit-interleaved coded modulation relying on iterative decoding, and compare it with the optimal ML MUD in terms of its performance and complexity. Both our extrinsic information transfer charts and bit error ratio curves show that the performance of the proposed QMUD and that of the optimal classic MUD are equivalent, but the QMUD's computational complexity is significantly lower.
The high complexity of numerous optimal classical communication schemes, such as the Maximum Likelihood (ML) and Maximum A posteriori Probability (MAP) Multi-User Detector (MUD) designed for coherent detection or the ML and MAP Multiple-Symbol Differential Detectors (MSDD) conceived for non-coherent receivers often prevents their practical implementation. In this thesis we commence with a review and tutorial on Quantum Search Algorithms (QSA) and propose a number of hard-output and iterative Quantum-assisted MUDs (QMUD) and MSDDs (QMSDD). We employ a QSA, termed as the Durr-Hyer Algorithm (DHA) that finds the minimum of a function in order to perform near-optimal detection with quadratic reduction in the computational complexity, when compared to that of the ML MUD / MSDD. Two further techniques conceived for reducing the complexity of the DHA-based Quantum-assisted MUD (QMUD) are also proposed. These novel QMUDs / QMSDDs are employed in the uplink of various multiple access systems, such as Direct Sequence Code Division Multiple Access systems, Space Division Multiple Access systems as well as in Direct-Sequence Spreading and Slow Subcarrier Hopping SDMA systems amalgamated with Orthogonal Frequency Division Multiplexing and Interleave Division Multiple Access systems. Furthermore, we follow a quantum approach to achieve the same performance as the optimal Soft Input Soft-Output (SISO) classical detectors by replacing them with a quantum algorithm, which estimates the weighted sum of all the evaluations of a function. We propose a SISO QMUD / QMSDD scheme, which is the quantum-domain equivalent of the MAP MUD / MSDD. Both our EXtrinsic Information Transfer (EXIT) charts and Bit Error Ratio (BER) curves show that the computational complexity of the proposed QMUD / QMSDD is significantly lower than that of the MAP MUD / MSDD, whilst their performance remains equivalent. Moreover, we propose two additional families of iterative DHA-based QMUD / QMSDDs for performing near-optimal MAP detection exhibiting an even lower tunable complexity than the QWSA QMUD. Several variations of the proposed QMUD / QMSDDs have been developed and they are shown to perform better than the state-of-the-art low-complexity MUDs / MSDDs at a given complexity. Their iterative decoding performance is investigated with the aid of non-Gaussian EXIT charts.
In this paper, we propose a quantum-native formulation of maximum likelihood detection (MLD) for overloaded multiple-input multiple-output (MIMO) systems in a random access channel, where numerous user terminals share the same channel resource and asynchronously transmit signals. Classical linear detectors suffer from significant performance degradation in this scenario, whereas the exhaustive-search MLD achieves the optimal performance but incurs an exponential computational complexity. To overcome this trade-off, we formulate the MLD as a binary optimization problem and solve it via Grover adaptive search (GAS) -- a quantum exhaustive search algorithm offering quadratic speedup in fault-tolerant quantum computing. We then introduce a search space reduction technique to substantially decrease the required computational resources. In addition, we investigate efficient parameter settings for GAS through probability analysis to improve convergence performance. We demonstrate that the proposed detector achieves the optimal detection performance while reducing the required Grover rotation count to reach the solution by up to approximately 65% compared with the conventional GAS, showing its potential as a viable solution for future quantum-accelerated wireless systems.