1,721,008 research outputs found
Research Data: EXIT-chart Aided Quantum Code Design Improves the Normalised Throughput of Realistic Quantum Devices
No full textResearch data for the paper:
Nguyen, Hung, Babar, Zunaira and Alanis, Dimitrios et al. (2016) EXIT-chart aided quantum code design improves the normalised throughput of realistic quantum devices. IEEE Access.</span
Quantum-assisted multi-objective optimization of heterogeneous networks
No full textSome of the Heterogeneous Network (HetNet) components may act autonomously for the sake of achieving the best possible performance. The attainable routing performance 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 relying on an element of elitism.Although there is a plethora of bio-inspired algorithms that attempt to address the associated multi-component optimization problem, they often fail to generate all the routes constituting the Optimal Pareto Front (OPF). As a remedy, we initially propose an optimal multi-objective quantum-assisted algorithm, namely the Non-dominated Quantum Optimization (NDQO) algorithm, 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’s 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.However again, as the number of nodes 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 parallelism and the quantum parallelism for the sake of achieving a further complexity reduction, which is on the order of O(√N) and O(N√N)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 activating the same order of comparison operators.Apart from the traditional QoS requirements, the network design also has to consider the nodes’ user-centric social behavior. Hence, the employment of socially-aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network’s packet-flow. Therefore, we also propose a novel algorithm, referred to as the Multi-Objective Decomposition Quantum Optimization (MODQO) algorithm, which exploits the quantum parallelism to its full potential by exploiting the database correlations for performing multi-objective routing optimization, while at the same time balancing the tele-traffic load among the nodes without imposing a substantial degradation on the network’s delay and power consumption. Furthermore, we introduce a novel socially-aware load balancing metric, namely the normalized entropy of the normalized composite betweenness of the associated socially-aware network, for striking a better trade-off between the network’s delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full-search. Finally, we compare the MODQO algorithm to the classic NSGA-II evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network’s average delay, whilst simultaneously reducing the network’s average power consumption by 6 dB without increasing the computational complexity
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
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
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
Fifteen years of quantum LDPC coding and improved decoding strategies
No full textThe near-capacity performance of classical low-density parity check (LDPC) codes and their efficient iterative decoding makes quantum LDPC (QLPDC) codes a promising candidate for quantum error correction. In this paper, we present a comprehensive survey of QLDPC codes from the perspective of code design as well as in terms of their decoding algorithms. We also conceive a modified non-binary decoding algorithm for homogeneous Calderbank-Shor-Steane-type QLDPC codes, which is capable of alleviating the problems imposed by the unavoidable length-four cycles. Our modified decoder outperforms the state-of-the-art decoders in terms of their word error rate performance, despite imposing a reduced decoding complexity. Finally, we intricately amalgamate our modified decoder with the classic uniformly reweighted belief propagation for the sake of achieving an improved performance
Research Data: Towards the Quantum Internet: Generalised Quantum Network Coding for Large-scale Quantum Communication Networks
No full textResearch Data for paper: Towards the Quantum Internet: Generalised Quantum Network Coding for Large-scale Quantum Communication Networks </span
Quantum search algorithms for wireless communications
No full textFaster, ultra-reliable, low-power and secure communications has always been high on the wireless evolutionary agenda. However, the appetite for faster, more reliable, greener and more secure communications continues to grow. The state-of-the-art methods conceived for achieving the performance targets of the associated processes may be accompanied by an increase in computational complexity. Alternatively, a degraded performance may have to be accepted due to the lack of jointly optimized system components. In this survey we investigate the employment of quantum computing for solving problems in wireless communication systems. By exploiting the inherent parallelism of quantum computing, quantum algorithms may be invoked for approaching the optimal performance of classical wireless processes, despite their reduced number of cost-function evaluations. In this contribution we discuss the basics of quantum computing using linear algebra, before presenting the operation of the major quantum algorithms, which have been proposed in the literature for improving wireless communications systems. Furthermore, we investigate a number of optimization problems encountered both in the physical and network layer of wireless communications, while comparing their classical and quantum-assisted solutions. Finally, we state a number of open problems in wireless communications that may benefit from quantum computing
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