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Maximum-likelihood multiuser detection incurs a large computational complexity, and its low-complexity detection scheme suffers from a performance loss, where this tradeoff is inevitable and inherent in a classical computer. In this paper, we use the Grover adaptive search (GAS) to break the tradeoff, which is a quantum exhaustive search algorithm guaranteed to obtain the optimal solution, achieving a quadratic speedup. Specifically, we design two specific parameters of GAS to achieve the optimal performance with a reduced complexity: the initial threshold and the number of Grover rotations. The initial threshold of GAS can be optimized using a solution of semi-definite programming, and it is possible to calculate the distribution of the number of solutions smaller than the initial threshold in advance, which depends on instantaneous channel coefficients. In addition, we analyze the number of quantum gates required for GAS and show that the gate count can be reduced by bypassing the higher-order terms in the objective function, leading to a reduced circuit runtime. Our analysis and simulation results demonstrate that the proposed approach achieves the same performance as the optimal maximum-likelihood detection while reducing the query complexity of GAS, implying that the large constant overhead of quadratic speedup can be further reduced.

This paper presents a quantum-assisted index modulation for next-generation IoT wireless networks. The NP-hard index selection problem is first formulated by a quadratic unconstrained binary optimization (QUBO) problem consisting of constraints of feasible solutions. To minimize the number of qubits required for a quantum circuit, this formulation is then simplified by a dictionary-based approach that partially exploits a classical computer. For both formulations, the numbers of required qubits and non-zero elements in QUBO matrices are analyzed algebraically, and found to be in close agreement with the actual measurement. It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem. This promising result implies that the on-off structure of index modulation is suitable for quantum computation, and future fault-tolerant quantum computers may be useful for obtaining high-performance index activation patterns.

Sixty years of coherent versus non-coherent tradeoff as well as the twenty years of coherent versus non-coherent tradeoff in Multiple-Input Multiple-Output (MIMO) systems are surveyed. Furthermore, the advantages of adaptivity are discussed. More explicitly, in order to support the diverse communication requirements of different applications in a unified platform, the 5G New Radio (NR) offers unprecendented adaptivity, abeit at the cost of a substantial amount of signalling overhead that consumes both power and the valuable spectral resources. Striking a beneficial coherent versus non-coherent tradeoff is capable of reducing the pilot overheads of channel estimation, whilst relying on low-complexity detectors, especially in high-mobility scenarios. Furthermore, since energy-efficiency is of salient importance both in the operational and future networks, following the powerful Index Modulation (IM) pholosophy, we conceive a holistic adaptive pholosophy striking the most appropriate coherent/non-coherent, single-/multiple-antenna and diversity/multiplexing tradeoffs, where the number of RF chains, the Peak-to-Average Power Ratio (PAPR) of signal transmission and the maximum amount of interference tolerated by signal detection are all taken into account. We demonstrate that this intelligent tripple-fold adaptivity offers significant benefits in next-generation applications of mmWave and Terahertz solutions, in space-air-ground integrated networks, in full-duplex techniques and in other sophisticated channel coding assisted system designs, where powerful machine learning algorithms are expected to make autonomous decisions concerning the best mode of operation with minimal human intervention.

In this paper, we propose, implement, and evaluate a simple yet powerful estimation method for the quality of service of a large-scale Wi-Fi network, which is achieved using our passive and low-complexity measurements. Specifically, our proposed method estimates a time-varying frame error rate at an access point (AP) by counting null function data frames, which are emitted periodically by stationary user equipments (UEs), such as mobile phones and tablets. We implemented our method as a user-level script and a production-level firmware. Our real-world measurements in an uncontrolled 1500-seat hall demonstrated that our method exhibited high scalability against the increase in the numbers of APs and UEs with the aid of its simple structure and was capable of detecting the quality-of-service loss induced by an unexpected dynamic frequency selection.

This paper presents a proposal of an open-source index modulation (IM) toolkit, which facilitates reproducible research and accelerates open innovation in IM studies. The proposed toolkit is implemented based on massively parallel algorithms that are designed for state-of-the-art graphics processing units (GPUs). Since high-performance GPUs are available at low cost, along with the intensive development in deep learning, this toolkit achieves large scale but significantly fast Monte Carlo simulations at low cost. Two large-tensor-based parallel algorithms are introduced for bit error ratio and average mutual information simulations. Additionally, the design of active indices is newly formulated into an integer linear programming problem that guarantees optimality, which is applicable to the generalized spatial modulation and subcarrier-index modulation schemes. Performance comparisons demonstrated that the proposed GPU-aided algorithms were up to 145 times faster than the conventional CPU-aided efficient counterparts. Furthermore, the designed active indices achieved the theoretical optimum performance in contrast to widely used conventional methods. A comprehensive database of these designed active indices is released online and is available to any researcher.

The existing time-division duplex (TDD) and frequency-division duplex (FDD) techniques rely on a guard time and/or a guard band to avoid self-interference (SI) between the uplink and downlink channels, which results in the wastage of precious spectral resources. The full-duplex (FD) schemes of in-band FD (IBFD), as well as multicarrier division duplex (MDD), may overcome this drawback while retaining the key benefits of both TDD and FDD. Moreover, the MDD exhibits the exclusive benefits of the reduced peak-to-average-power ratio (PAPR) for signal transmission as well as the SI-free signal detection. Against this background, in this paper, we propose a novel FD scheme conceived for frequency-selective millimeter-wave (mmWave) channels, which have not been investigated in the open literature. Furthermore, we propose a novel projection-aided iterative eigenvalue decomposition (P-IEVD) algorithm, which performs null-space SI cancellation in the inherent beamforming structure of mmWave communication. Our simulation results confirm that the MDD is capable of outperforming its half-duplex (HD) counterparts of the TDD/FDD, even the IBFD can only achieve a better bandwidth efficiency than the MDD when a sufficiently high SNR is provided.

The achievable performance of subcarrier-index modulation (SIM) is analyzed in terms of its minimum Euclidean distance, constrained and unconstrained average mutual information, as well as its peak-to-average power ratio (PAPR). Our performance investigations identify the beneficial operating region of the SIM scheme over its conventional orthogonal frequency-division multiplexing (OFDM) counterpart, hence providing general design guidelines for the SIM parameters. More specifically, an SIM scheme is shown to be beneficial for the scenario of a relatively low transmission rate below 2 b/s/Hz. In addition, we demonstrate that the PAPR of the SIM scheme is comparable with that of its OFDM counterpart under the idealized simplifying assumption of having Gaussian input symbols.