In this paper, we propose a quantum-native formulation of maximum likelihood detection in random access channel with overloaded multiple-input multiple-output (MIMO), where a number of user terminals share the same channel resource and asynchronously transmit signals. Classical low-complexity linear detectors suffer from significant performance degradation in this scenario, while the exhaustive-search-based maximum likelihood detection achieves the optimal performance but induces an exponential increase in time complexity. To strike this fundamental tradeoff, we formulate the maximum likelihood detection as a binary optimization problem and solve it via the Grover adaptive search (GAS), a quantum exhaustive search algorithm offering quadratic speedup. We further introduce a search space reduction technique using quantum W states and controlled-H gates, which substantially decreases complexity. In addition, we investigate the effects of efficient parameter settings in GAS. Our simulation results show that the proposed detector achieved the optimal detection performance while reducing the query complexity, thereby highlighting its potential as a solution for future quantum-accelerated overloaded MIMO systems.

We propose a quantum-assisted solution of the pilot assignment problem. In particular, we formulate a pilot assignment problem designed to minimize the interference from co-pilot user terminals as a binary optimization problem which can be solved via a quantum exhaustive search through the Grover adaptive search (GAS) algorithm. The performance of GAS in terms of query complexity is enhanced by introducing a modified initial state and an improved initial threshold using the upper bound for the minimum value of the objective function. Simulation results demonstrate that our proposed method can provide quadratic speedup compared to an exhaustive search performed by a classical computer, concretely demonstrating that quantum computers can be employed to solve optimally and efficiently the pilot assignment problem.

We propose a new quantum-assisted maximum likelihood detection (MLD) scheme for generalized spatial modulation (GSM) systems. To that end, we first present a novel MLD formulation for GSM, and then describe a corresponding novel method to prepare the initial state for an associated quantum-computing Grover adaptive search (GAS) algorithm. Simulation results are presented to show that our approach achieves optimal performance and reduces query complexity compared both to the classical MLD and to earlier quantum search methods, which do not achieve an advantage over classical MLD for some parameter settings.

In this paper, we propose a reduced-complexity detector for non-coherent communications with the Grassmann constellation, which enables joint channel and data estimation. Here, we employ deep learning techniques with the aim of reducing computational complexity while maintaining nearly the same channel estimation accuracy as the conventional detector. The conventional maximum likelihood detection is a discrete optimization problem, with complexity increasing exponentially with respect to the transmission rate. In our approach, the Grassmann constellation is detected using a neural network model trained with random signal-to-noise ratios and the corresponding received signals as inputs, and the detected codeword is used for channel estimation. Our simulations demonstrate that the channel estimation accuracy can be maintained with lower complexity compared to the conventional detector, at the cost of a slightly degraded symbol error rate performance. It was also found that the detection complexity can be reduced when the number of receive antennas exceeds four, which is practically relevant.

In this paper, we analyze the performance of the data-carrying reference signal (DC-RS) in time-varying channels. In such scenarios, the spectral efficiency may be reduced because more reference signals need to be transmitted frequently to maintain high channel estimation accuracy. DC-RS has the potential to boost spectral efficiency, which conveys additional data with noncoherent detection, but it has not been analyzed in realistic time-varying channels. By regarding a channel coefficient varying with first-order autoregressive model as an additive independent Gaussian noise for each time slot, we derive the average mutual information of DC-RS. Although the time-varying nature induces performance penalty in general, our numerical simulations demonstrate that the spectral efficiency improves even in rapidly varying channels compared to the case with conventional reference signals, and this trend remains valid upon increasing the mobile speed from 0 to 300 km/h.

We propose a quantum-assisted solution for the maximum likelihood detection (MLD) of generalized spatial modulation (GSM) signals. Specifically, the MLD of GSM is first formulated as a novel polynomial optimization problem, followed by the application of a quantum algorithm, namely, the Grover adaptive search. The performance in terms of query complexity of the proposed method is evaluated and compared to the classical alternative via a numerical analysis, which reveals that under fault-tolerant quantum computation, the proposed method outperforms the classical solution if the number of data symbols and the constellation size are relatively large.

In wireless networks, frequent reference signal transmission for accurate channel reconstruction may reduce spectral efficiency. To address this issue, we consider to use a data-carrying reference signal (DC-RS) that can simultaneously estimate channel coefficients and transmit data symbols. Here, symbols on the Grassmann manifold are exploited to carry additional data and to assist in channel estimation. Unlike conventional studies, we analyze the channel estimation errors induced by DC-RS and propose an optimization method that improves the channel estimation accuracy without performance penalty. Then, we derive the achievable rate of noncoherent Grassmann constellation assuming discrete inputs in multi-antenna scenarios, as well as that of coherent signaling assuming channel estimation errors modeled by the Gauss-Markov uncertainty. These derivations enable performance evaluation when introducing DC-RS, and suggest excellent potential for boosting spectral efficiency, where interesting crossings with the non-data carrying RS occurred at intermediate signal-to-noise ratios.

In this paper, we propose an accurate channel estimation scheme for orthogonal time frequency space modulation, in which we embed multiple superimposed pilots (SPs) instead of a single SP used in the conventional scheme. Multiple SPs are used to mitigate data-pilot interference efficiently and improve the accuracy of channel estimation. Our numerical simulations demonstrate that the proposed scheme outperforms the conventional SP-based scheme in terms of bit error rate and normalized mean squared error of channel estimates, indicating the potential for further improvement in spectral efficiency.

In this paper, we address the joint maximum-likelihood detection of power-domain non-orthogonal multiple access and transform the detection problem into a binary optimization problem. Then, we solve it by a quantum algorithm, Grover adaptive search (GAS), and further accelerate the algorithm. Assuming fault-tolerant quantum computing, GAS provides a quadratic speedup for binary optimization problems in terms of query complexity. The proposed method designs the number of Grover operators using a time-invariant distribution of the number of solutions, which can be obtained in advance. Simulation results demonstrate that the proposed method is capable of reducing the query complexity in the classical domain while achieving the optimal performance.

In this paper, we propose a quantum algorithm that supports a real-valued higher-order unconstrained binary optimization (HUBO) problem. This algorithm is based on the Grover adaptive search that originally supported HUBO with integer coefficients. Next, as an application example, we formulate multiple-input multiple-output maximum likelihood detection as a HUBO problem with real-valued coefficients, where we use the Gray-coded bit-to-symbol mapping specified in the 5G standard. The proposed approach allows us to construct an efficient quantum circuit for the detection problem and to analyze specific numbers of required qubits and quantum gates, whereas other conventional studies have assumed that such a circuit is feasible as a quantum oracle. To further accelerate the quantum algorithm, we also derive a probability distribution of the objective function value and determine a unique threshold to sample better states. Assuming a future fault-tolerant quantum computing, our proposed algorithm has the potential for significantly reducing query complexity in the classical domain and providing a quadratic speedup in the quantum domain.

We propose a novel physical layer security scheme that exploits an optimization method as a one-way function. The proposed scheme builds on nonsquare differential multiple-input multiple-output (MIMO), which is capable of noncoherent detection even in massive MIMO scenarios and thus resilient against risky pilot insertion and pilot contamination attacks. In contrast to conventional nonsquare differential MIMO schemes, which require space-time projection matrices designed via highly complex, discrete, and combinatorial optimization, the proposed scheme utilizes projection matrices constructed via low-complexity continuous optimization designed to maximize the coding gain of the system. Furthermore, using a secret key generated from the true randomness nature of the wireless channel as an initial value, the proposed continuous optimization-based projection matrix construction method becomes a one-way function, making the proposed scheme a physical layer secure differential MIMO system. An attack algorithm to challenge the proposed scheme is also devised, which demonstrates that the security level achieved improves as the number of transmit antennas increases, even in an environment where the eavesdropper can perfectly estimate channel coefficients and experience asymptotically large signal-to-noise ratios.

In this paper, we propose an optimal but low-complexity optimization method for nonsquare differential massive MIMO. While a discrete nonlinear optimization is required for the conventional nonsquare differential coding, we newly modify it to perform a low-complexity continuous linear optimization. This novel method exhibits immediate convergence as compared to the conventional method. Additionally, the proposed differential coding can be regarded as a differential counterpart of the coherent generalized spatial modulation. Our numerical comparisons demonstrate that the proposed method achieves the best coding gain for any number of transmit antennas, although the optimization cost is nearly negliaible,

As the enabling technologies move up to the mmWave and even to the TeraHertz bands for the next-generation wireless systems, the signal processing of high-bandwidth orthogonal frequency division multiplexing (OFDM) becomes increasingly power-thirsty, owing to the following OFDM deficiencies: (1) the high peak-to-average power ratio (PAPR); (2) the bandwidth efficiency loss due to the cyclic prefix (CP) overhead; (3) the sensitivity to carrier frequency offset; (4) the complex out-of-band (OOB) filtering. Over the past six decades, a variety of waveforms have been developed in order to mitigate these deficiencies, which are generally achieved at the cost of compromising some of OFDM’s beneficial properties, such as its subcarrier (SC) orthogonality, its high throughput and its straighforward adoption to multiple-input multiple-output (MIMO) systems. Against this background, we propose a new waveform termed as multi-band discrete Fourier transform spread-OFDM with index modulation (MB-DFT-S-OFDM-IM), where the component multi-carrier techniques are conceived to constructively function together in order to mitigate the OFDM deficiencieswithout compromising the beneficial OFDM properties. More explicitly, first of all, the PAPR is reduced by the DFT-precoding. Secondly, thanks to the IM design, MB-DFT-S-OFDM-IM is capable of achieving a high throughput that is strictly equal to or higher than the OFDM throughput. Thirdly, MB-DFT-S-OFDM-IM achieves a beneficial frequency diversity gain, which leads to a higher tolerance to carrier frequency offset. Fourthly, the OOB filters are placed in each sub-band before DFT, so that the SC orthogonality remains intact, which is unique to the proposed MB-DFT-S-OFDM-IM structure. Last but not least, we extend the proposed MB-DFT-S-OFDM-IM to support a variety of MIMO schemes, where the IM philosophy is integrated with the space-, time- and frequency-domains within a singleunifiedplatform.

A simplified rectangular differential spatial modulation (S-RDSM) scheme is conceived for massive multiple-input multiple-output (MIMO) systems dispensing with the channel state information (CSI). In the proposed S-RDSM scheme, the information bits are first mapped to a conventional SM symbol and then rectangular differential encoding is invoked between a pair of SM symbols. Then a non-coherent detector relying on a forgetting factor is developed, which requires no CSI at the receiver. Explicitly, a low-complexity hard limited maximum likelihood (HL-ML) detector is conceived for our generalized S-RDSM scheme, which is characterized by our theoretical analysis. Furthermore, we derive the optimal forgetting factor in closed form, which is capable of significantly reducing the complexity of the associated optimization. Finally, the upper bounds of the average bit error probability (ABEP) are derived using the moment generating function (MGF), and are validated by our simulation results. Both the theoretical and simulation results have shown that the proposed S-RDSM system outperforms the existing non-coherent schemes, despite operating at 10% of the benchmarker's complexity, whilst approaching the performance of its coherent SM counterpart at a comparable complexity.

Two decades of full-diversity high-rate MIMO research has created perfect Space-Time Block Codes (STBCs), including the Golden code. However, the major stumbling block of their wide-spread employment is their limited energy-efficiency. On one hand, the superposition of their signals results in a high Peak-to-Average Power Ratio (PAPR). On the other hand, the total number of equivalent Inter-Antenna Interference (IAI) contributions that the receiver has to deal with is increased to IAI = M2upon using M Transmit Antennas (TAs), which is a substantial extra price compared to the IAI = M of V-BLAST. Against this background, we propose a new family of Finite-Cardinality Generalized Space-Time Shift Keying (FC-GSTSK). More explicitly, the proposed FC-GSTSK is capable of outperforming both V-BLAST and STBC, which is the ultimate objective of full-diversity high-rate MIMO design. Furthermore, following the index modulation philosophy, the proposed FC-GSTSK replaces the signal-additions by the data-carrying signal-selection process. As a benefit, the FC-GSTSK substantially reduces the PAPR of signal transmission. As a further advantage, the equivalent IAI imposed on signal detection is reduced back to the same level as that of the V-BLAST. Moreover, the proposed FC-GSTSK is even capable of consistently outperforming the perfect STBCs in terms of its Peak Signal to Noise-power Ratio (PSNR) that takes into account the power consumption at the transmitter. As a further advance, the reduced-RF-chain based version of FC-GSTSK is also capable of outperforming both Generalized Spatial Modulation (GSM) and Space-Time Block Coded Spatial Modulation (STBC-SM) without increasing the PAPR and the equivalent IAI.

The matrix-based differential encoding invoked by Differential Space-Time Modulation (DSTM) typically results in an infinite-cardinality of arbitrary signals, despite the fact that the transmit antennas (TAs) can only radiate a limited number of patterns. As a remedy, the recently developed differential spatial modulation (DSM) is capable of avoiding this problem by conceiving a beneficial sparse signal matrix design, which also facilitates low-complexity single-RF signal transmission. Inspired by this development, the differential space-time block code using index shift keying (DSTBC-ISK) further introduces a beneficial diversity gain without compromising the DSM's appealingly low transceiver complexity. However, the DSTBC-ISK's performance advantage tends to diminish as the throughput increases, especially when an increased number of Receive Antennas (RAs) is used. By contrast, the classic Differential Group Code (DGC) that actively maximizes its diversity gain for different multiple-input-multiple-output (MIMO) system setups is capable of achieving a superior performance, but its detection complexity grows exponentially with the throughput. Against this background, we propose the differential space-time shift keying using Diagonal Algebraic Space-Time scheme, which is the first DSTM that is capable of achieving the DGC's superior diversity gain at high throughputs without compromising the DSM's low transceiver complexity. As a further advance, we also conceive a new differential space-time shift keying using Threaded Algebraic Space-Time arrangement, which is capable of achieving an even further improved diversity gain at a substantially reduced signal detection complexity compared to the best DGCs. Furthermore, in order to strike a practical tradeoff, we develop a generic multi-element and multi-level-ring Amplitude Phase Shift Keying design, and we also arrange for multiple reduced-size DSTM sub-blocks to be transmitted in a permuted manner, which exhibits an improved diversity-throughput tradeoff.

In this paper, we propose a simple yet powerful mapping scheme that converts any conventional square-matrix-based differential space-time coding (DSTC) into a nonsquare-matrix-based DSTC. This allows DSTC schemes to be used practically in open-loop large-scale multiple-input multiple-output scenarios. Our proposed scheme may be viewed as the differential counterpart of coherent spatial modulation (SM), of the generalized SM, of Bell Laboratories layered space-time architecture, and of subcarrier-index modulation. The fundamental impediment of the existing DSTC schemes is the excessive complexity imposed by the unitary constraint. Specifically, the transmission rate of conventional DSTC schemes decays as the number of transmit antennas increases. Our proposed scheme eliminates this impediment and thus achieves a significantly higher transmission rate. We introduce four novel construction methods for the nonsquare codewords, some of which include an arbitrary number of nonzero elements in each codeword column. Our analysis shows that the proposed encoding technique reduces the complexity of both the inverse Fourier transform and the detection processes. Our proposed scheme is shown to approach the performance of its coherent counterpart for low-mobility scenarios, where the number of transmit antennas is increased up to 256.

We show that certain signal constellations invoked for classic differential encoding result in a phenomenon we term as the unbounded differential constellation size (UDCS). Various existing differential transmission schemes that suffer from this issue are identified. Then, we propose an enhanced algebraic field extension-based differential spatial modulation (AFE-DSM) scheme and its enhanced counterpart that strikes a diversityrate tradeoff (AFE-DSM-DR), both of which overcome the UDCS issue without compromising its full transmit diversity advantage. Furthermore, the proposed schemes are extended to incorporate amplitude and phase shift keying (APSK) in order to exploit all the available degrees of freedom. Additionally, we propose a pair of detection schemes specially designed for APSK-aided differential transmission schemes. Explicitly, we conceive the buffered minimum mean squared error (B-MMSE) detector and buffered maximum likelihood (B-ML) detector, which exploit the knowledge of previously detected symbols in order to further improve the detection performance. Our simulation results have shown that the proposed detectors are capable of bridging the performance gap between the conventional differential detector (CDD) and the coherent detector that has full channel state information. Specifically, when employing the proposed APSKaided AFE-DSM scheme operating at a rate of 2 b per channel use, the B-MMSE and B-ML detectors are observed to give about 3and 3.5-dB signal-to-noise ratio gain with respect to their CDD counterpart at a bit error ratio of 10-5.

In this paper, a novel differential space-time coding scheme is conceived for open-loop noncoherent multiple-input multiple-output (MIMO) downlink scenarios, where the transmission rate increases logarithmically in a scalable manner upon increasing the number of transmit antennas. More specifically, the proposed scheme relies on the projection of a differentially encoded square matrix to its rectangular counterpart and so is capable of reducing the number of symbol intervals needed for block transmission. This is especially beneficial for massive MIMO scenarios, in which the number of transmit antennas is very high. Another advantage exclusive to the presented scheme is that no channel state information (CSI) is required at either the transmitter or the receiver, which eliminates pilot overhead, CSI estimation, CSI feedback, and time-division duplex reciprocity. Furthermore, the rectangular transmission matrix of the proposed scheme contains only a single non-zero element per column, and hence, the transmitter may rely on only a single RF chain, similar to the conventional coherent spatial modulation scheme.

In this paper, we propose a near-capacity irregular precoded power-imbalanced (PI) optical spatial modulation (OSM) scheme. The PI-OSM scheme was developed for negating the effects of high channel correlation, which is often observed in optical wireless communications. In our proposed scheme, multiple PI-OSM subcodes are used to create a novel irregular precoded PI-OSM architecture. This allows us to match the extrinsic information transfer curves of the inner and outer codes in a flexible manner, hence reducing the signal-to-noise ratio required for achieving an infinitesimally low bit error rate (BER). Our numerical simulation results demonstrate that the irregular precoded PI-OSM scheme achieves a high performance, particularly in noise-dominant channels, and it outperforms the repetition-coded benchmark scheme with pulse-amplitude modulation.

Motivated by the recent generalized spatial modulation (GSM)-multiple input multiple-output (MIMO) concept, in this paper we conceive a non- coherently detected GSM architecture, which is achieved with the aid of Cayley unitary differential encoding. More specifically, the proposed differentially-encoded transmitter structure enables us to eliminate any limitations imposed by pilot insertions and channel state information (CSI) estimation, while attaining the GSM scheme's increased transmission rate as well as reduced number of RF chains at the transmitter. Furthermore, we extend the proposed non-coherent GSM scheme to operate in a single-antenna transmitter structure. Our performance results reveal that a high transmission rate is achieved, while attaining reduced-RF branches and dispensing with the channel estimation at the receiver.