1,721,019 research outputs found
Quantum-assisted multi-user wireless systems
No full textThe 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.<br/
Fixed-complexity quantum-assisted multi-user detection for CDMA and SDMA
Full text linkIn a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multilevel K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses MK CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-Høyer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems
Quantum search algorithms, quantum wireless, and a low-complexity maximum likelihood iterative quantum multi-user detector design
No full textThe 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
Low-complexity iterative quantum multi-user detection in SDMA systems
No full textThe 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 Durr-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
Low-complexity soft-output quantum-assisted multi-user detection for direct-sequence spreading and slow subcarrier-hopping aided SDMA-OFDM systems
No full textLow-complexity sub-optimal Multi-User Detectors (MUD) are widely used in multiple access communication systems for separating users, since the computational complexity of the Maximum Likelihood (ML) detector is potentially excessive for practical implementation. Quantum computing may be invoked in the detection procedure, by exploiting its inherent parallelism for approaching the ML MUD’s performance at a substantially reduced number of Cost Function (CF) evaluations. In this contribution, we propose a Soft-Output (SO) Quantum-assisted MUD achieving a near-ML performance and compare it to the corresponding SO Ant Colony Optimization (ACO) MUD. We investigate rank deficient Direct-Sequence Spreading (DSS) and Slow Subcarrier-Hopping aided (SSCH) Spatial Division Multiple Access (SDMA) Orthogonal Frequency Division Multiplexing (OFDM) systems, where the number of users to be detected is higher than the number of receive antenna elements used. We show that for a given complexity budget, the proposed SODHA QMUD achieves a better performance. We also propose an adaptive hybrid SO-ML / SO-DHA MUD, which adapts itself to the number of users equipped with the same spreading sequence and transmitting on the same subcarrier. Finally, we propose a DSS-based uniform SSCH scheme, which improves the system’s performance by 0:5 dB at a BER of 105, despite reducing the complexity required by the MUDs employed
EXIT Chart-Aided Convergence Analysis of Recursive Soft m-Sequence Initial Acquisition in Nakagami-m Fading Channels
No full textThis is the dataset of the accepted paper (January, 2018): Abbas Ahmed, Panagiotis Botsinis, SeungHwan Won, Lie-Liang Yang and Lajos Hanzo, "EXIT Chart-Aided Convergence Analysis of Recursive Soft m-Sequence Initial Acquisition in Nakagami-m Fading Channels" IEEE Transactions on Vehicular Technology.
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EXIT chart-aided convergence analysis of recursive soft m-sequence initial acquisition in Nakagami-m fading channels
No full textA delay of less than one millisecond is required by low-latency 5G wireless communication systems for supporting the ‘tactile’ Internet. Hence, conventional initial synchronisation cannot be readily employed because of its potentially excessive delay. In this paper, an EXtrinsic Information Transfer (EXIT) Chart assisted approach is used for the convergence analysis of m-sequences using Recursive Soft Sequence Estimation (RSSE) in the context of Nakagami-m fading channels. Explicitly, the novelty of our work is based on employing a new type of EXIT Charts operating without using interleavers. This is a challenge, because the original EXIT charts rely on the employment of long, high-delay interleavers for ensuring that the inputs to the decoders become uncorrelated. We then evaluate the performance of various classes of m-sequences with the aid of the proposed EXIT charts and demonstrate that the m-sequences generated by the lower-order polynomials maximise the mutual information more promptly with the aid of our RSSE scheme than those, which belong to a higher-order polynomial
Non-dominated quantum iterative routing optimization for wireless multihop networks
No full textRouting in Wireless Multihop Networks (WMHNs) relies on a delicate balance of diverse and often conflicting parameters, when aiming for maximizing the WMHN performance. Classified as a Non-deterministic Polynomial-time hard problem (NP-hard), routing in WMHNs requires sophisticated methods. As a benefit of observing numerous variables in parallel, quantum computing offers a promising range of algorithms for complexity reduction by exploiting the principle of Quantum Parallelism (QP), while achieving the optimum full-search-based performance. In fact, the so-called Non-Dominated Quantum Optimization (NDQO) algorithm has been proposed for addressing the multi-objective routing problem with the goal of achieving a near-optimal performance, while imposing a complexity of the order of and in the best- and worst-case scenarios, respectively. However, as the number of nodes in the WMHN increases, the total number of routes increases exponentially, making its employment infeasible despite the complexity reduction offered. Therefore, we propose a novel optimal quantum-assisted algorithm, namely the Non-Dominated Quantum Iterative Optimization (NDQIO) algorithm, which exploits the synergy between the hardware and the quantum parallelism for the sake of achieving a further complexity reduction, which is on the order of and in the best- and worst-case scenarios, respectively. Additionally, we provide simulation results for demonstrating that our NDQIO algorithm achieves an average complexity reduction of almost an order of magnitude compared to the near-optimal NDQO algorithm, while having the same order of power consumptio
Quantum-assisted routing optimization for self-organizing networks
No full textSelf-Organizing Networks (SONs) act autonomously for the sake of achieving the best possible performance. The attainable routing depends on a delicate balance of diverse and often conflicting Quality-of-Service (QoS) requirements. Finding the optimal solution typically becomes an NP-hard problem, as the network size increases in terms of the number of nodes. Moreover, the employment of user-defined utility functions for the aggregation of the different objective functions often leads to suboptimal solutions. On the other hand, Pareto Optimality is capable of amalgamating the different design objectives by providing an element of elitism. Although there is a plethora of bio-inspired algorithms that attempt to address this optimization problem, they often fail to generate all the points constituting the Optimal Pareto Front (OPF). As a remedy, we propose an optimal multi-objective quantum-assisted algorithm, namely the Non-dominated Quantum Optimization algorithm (NDQO), which evaluates the legitimate routes using the concept of Pareto Optimality at a reduced complexity. We then compare the performance of the NDQO algorithm to the state-of-the-art evolutionary algorithms, demonstrating that the NDQO algorithm achieves a near-optimal performance. Furthermore, we analytically derive the upper and lower bounds of the NDQO algorithmic complexity, which is of the order of O(N) and O(N√N) in the best- and worst-case scenario, respectively. This corresponds to a substantial complexity reduction of the NDQO from the order of O(N2) imposed by the brute-force (BF) method
Research Data: Joint Quantum-Assisted Channel Estimation and Data Detection
No full textThis DOI contains the datasets of Figures 6-20 of the paper titled Joint Quantum-Assisted Channel Estimation and Data Detection. Each folder is named according to the corresponding figure, where the dataset of each curve is stored in a .dat file. To regenerate the figures please use the command "gle Figure_Name.gle" (Graphics Layout Engine -GLE- should be installed on your machine). Each folder already includes the generated color and grayscale versions of the figures.
Paper Abstract:
Joint Channel Estimation (CE) and Multi-User Detection (MUD) has become a crucial part of iterative receivers. In this paper we propose a Quantum-assisted Repeated Weighted Boosting Search (QRWBS) algorithm for CE and we employ it in the uplink of MIMO-OFDM systems, in conjunction with the Maximum A posteriori Probability~(MAP) MUD and a near-optimal Quantum-assisted MUD (QMUD). The performance of the QRWBS-aided CE is evaluated in rank-deficient systems, where the number of receive Antenna Elements (AE) at the Base Station (BS) is lower than the number of supported users. The effect of the Channel Impulse Response (CIR) prediction filters, of the Power Delay Profile (PDP) of the channels and of the Doppler frequency have on the attainable system performance is also quantified. The proposed QRWBS-aided CE is shown to outperform the RWBS-aided CE, despite requiring a lower complexity, in systems where iterations are invoked between the MUD, the CE and the channel decoders at the receiver. In a system, where U=7 users are supported with the aid of P=4 receive AEs, the joint QRWBS-aided CE and QMUD achieves a 2 dB gain, when compared to the joint RWBS-aided CE and MAP MUD, despite imposing 43% lower complexity.</span
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