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 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.
The past, the current state-of-the-art and the future of wireless communications was briefly summarized in [1], [2]. Against this background, this presentation briefly reports on some of the recent quantum-aided design examples in the field of wireless communications. The high complexity of numerous optimal classic communication schemes, such as that of the Maximum Likelihood (ML) Multi-User Detector (MUD) often prevents their practical implementation [3], [4]. Fortunately the parallel-processing capability of quantum-domain algorithms is capable of potentially circumventing this limitation. In our first design example [5] we follow a quantum-aided 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 Maximum Likelihood (ML) MUD. We then demonstrate its application using the design example of a Direct-Sequence Code Division Multiple Access (DS-CDMA) system employing Bit-Interleaved Coded Modulation (BICM) relying on Iterative Decoding (ID), and compare it to the optimal ML MUD in terms of its performance and complexity. Both our EXtrinsic Information Transfer (EXIT) charts and Bit Error Ratio (BER) 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. In our second design example [6] we have conceived a near-capacity code design for entanglement-assisted classical communication over the quantum depolarizing channel. The proposed system relies on efficient near-capacity classical code designs [7] for approaching the entanglement-assisted classical capacity of a quantum depolarizing channel. It incorporates an Irregular Convolutional Code (IRCC), a Unity Rate Code (URC) and a soft-decision aided Superdense Code (SD), which is hence referred to as an IRCC-URC-SD arrangement. Furthermore, the entanglement-assisted classical capacity of an N-qubit superdense code transmitted over a depolarizing channel is invoked for benchmarking. It is demonstrated that the proposed system operates within 0.4 dB of the achievable noise limit for both 2-qubit as well as 3-qubit SD schemes. More specifically, our design exhibits a deviation of only 0.062 and 0.031 classical bits per channel use from the corresponding 2-qubit and 3-qubit capacity limits, respectively. The proposed system is also benchmarked against the classical convolutional and turbo codes.
Future wired and wireless communication systems will employ pure or combined code division multiple access (CDMA) techniques, such as in the European 3G mobile UMTS or power line communications system, but also several 4G proposals include, for example, multicarrier (MC) CDMA. Previous investigations discovered the drawbacks of single user detectors (SUD), which are widely employed in narrowband IS-95 CDMA systems, and were forced to develop suitable multiuser detection schemes to increase the efficiency against interference. However, at this moment there are only suboptimal solutions available became of the rather high complexity of optimal detectors. One of the possible receiver technologies may be quantum assisted computing devices which allow high level parallelism in computation. The first commercial devices are estimated for the next years, which meets the advent of 3G and 4G systems. We analyze the error probability and give tight bounds in a static and dynamically changing environment for a novel quantum computation based quantum multi-user detection (QMUD) algorithm, employing a quantum counting algorithm, which provides an optimal solution.
Moore's law is expected to lead to the gates of the quantum world in 2017. Therefore, the emerging quantum computing research is expected to give rise to novel quantum search algorithms, which may replace the currently used classical ones in wireless communications, leading to performance improvements and complexity reduction. In this paper, we demonstrate the benefits of quantum-assisted multi-user detection (QMUD) in the uplink of a multi-user system, where the reference user conveys a multilayered video stream to the base station, while using adaptive modulation and different rates per video layer. This is the first study, where a QMUD is employed in a video application. The QMUD does not treat the rest of the users as interference, but rather detects the signals transmitted by all the users. We have evaluated the system's performance both in terms of its bit error ratio and peak signal-to-noise ratio versus the channel's signal-to-noise ratio, while quantifying the complexity reduction achieved by using the QMUD instead of the optimal classical maximum a posteriori probability MUD. The effect of the number of users on the system's performance is also quantified.
With the explosive proliferation of mobile devices, the scarcity of communication resources has emerged as a critical issue in the next generation (NG) communication systems. To overcome this, non-orthogonal multiple access (NOMA) has been shown to provide a beneficial spectral efficiency gain, while supporting massive connectivity. Sparse code multiple access (SCMA) is one of the NOMA schemes, but its application is hampered by its high-complexity multi-user detection. Fortunately, quantum search techniques were shown to substantially reduce the complexity of multi-user detectors (MUD). However, the quantum search-aided multiuser detection of SCMA is an open problem at the time of writing. Hence, we conceive a pair of quantum search-aided MUDs for SCMA, namely, a quantum-assisted message passing algorithm (Q-MPA) -based MUD and a quantum-assisted sphere decoder-based MPA (QSD-MPA) MUD.
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.