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IEEE Access

Quantum Search-Aided Multi-User Detection of IDMA-Assisted Multi-Layered Video Streaming

Panagiotis Botsinis, Yongkai Huo, Dimitrios Alanis, Zunaira Babar, Soon Xin Ng, Lajos Hanzo

Published Jan 1, 2017• 11 citations
Publisher

Abstract

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.

DOI
10.1109/access.2017.2732358

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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.
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 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 potentially excessive complexity of the Maximum Likelihood Multi-User Detector (ML MUD) in large-scale Spatial Division Multiple Access (SDMA) systems dictates the employment of low-complexity sub-optimal MUDs in the context of conventional systems. However, this limitation was circumvented by the recently proposed Dürr-Høyer Algorithm (DHA)-aided Quantum Weighted Sum Algorithm (QWSA)-based Quantum Multi-User Detector (QMUD) employed for performing optimal ML iterative detection in SDMA systems. Focusing our attention on the QWSA, we analyse the QMUD and the evolution of the quantum system with the aid of a simple SDMA uplink scenario. We characterize the performance of the DHA-QWSA QMUD advocated, which is capable of matching the performance of the ML MUD both in terms of its EXIT charts and BER curves.
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.
3G and 4G mobile systems are based on CDMA technology. In order to increase the efficiency of CDMA receivers a large amount of effort is invested to develop suitable multi-user detector techniques. However, at this moment there are only suboptimal solutions available because of the rather high complexity of optimal detectors. One of the possible receiver technologies can be the quantum assisted computing devices which allows high level parallelism in computation. The first commercial devices are estimated by 2004, which meets the advent of 3G and 4G systems. We introduce a novel quantum computation based quantum multi-user detection (QMUD), employing Grover's (see Bell Labs, April 1999)search algorithm, which provides optimal solution. The proposed algorithm is robust to any kind of noise/suboptimal solutions.
A new method to realize computation of the log-likelihood ratio (LLR) in multi-user detection (MUD) by quantum annealing (QA) is proposed herein. One metaheuristic algorithm for solving combinatorial optimization problems is QA. Recently, QA has been applied in various fields to hasten computations. This study specifically examines the application of QA in up-link non-orthogonal multiple access (UL-NOMA) that employs iterative MUD. Our proposed method, QA-aided MUD, uses QA to calculate LLR in iterative MUD. We tested QA-aided MUD with a D-Wave quantum annealer. The error rate analysis demonstrates that QA-aided MUD has comparable performance to that of conventional MUD method. Furthermore, when the annealing time was set to 20 µs, we demonstrated than that the total computation time is 1/10 of the computation time necessary when using a digital computer. We also evaluated the convergence behavior of QA-aided MUD through evolution of mutual information. The results indicate that QA-assisted MUD has almost identical convergence performance to that of the conventional MUD method for LLR calculations.