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A generalized framework of spectrum sensing disruption for a power-limited adversary is proposed in this paper. In the literature, a conventional sensing attack typically assumes that the adversary has perfect knowledge of the spectral usage status. The framework in this paper considers a more general case where there are uncertainties in the estimates at the adversary. These uncertainties are modeled utilizing the probability of detection and the probability of false alarm. Then, the sum of the conditional probabilities of false detection at the secondary within the spectral range of interest, conditioned on the adversary’s estimated spectrum usage status, is maximized. It is shown that the optimal sensing attack, given perfect estimation is a special case of the proposed framework. When the adversary has perfect spectrum usage information, this framework reduces to a previously demonstrated optimal sensing disruption. When the adversary has imperfect information on the spectral status, the proposed framework is significantly more robust than conventional sensing attacks. Further, when the adversary’s power budget increases, it asymptotically approaches the sensing disruption performance upper bound. [ABSTRACT FROM AUTHOR]

Many applications of wireless sensor networks (WSNs) require deploying sensors in hostile environments, where an adversary may eavesdrop communications. To secure communications in WSNs, the $q$ -composite key predistribution scheme has been proposed in the literature. In this paper, we investigate secure $k$ -connectivity in WSNs operating under the $q$ -composite scheme, in consideration of the unreliability of wireless links. Secure $k$ -connectivity ensures that any two sensors can find a path in between for secure communication, even when $k-1$ sensors fail. We present conditions on how to set the network parameters such that the network has secure $k$ -connectivity asymptotically almost surely. The result is given in the form of a sharp zero–one law. [ABSTRACT FROM AUTHOR]

This paper is concerned with detecting an integer parameter vector inside a box from a linear model that is corrupted with a noise vector following the Gaussian distribution. One of the commonly used detectors is the maximum likelihood detector, which is obtained by solving a box-constrained integer least squares problem, that is NP-hard. Two other popular detectors are the box-constrained rounding and Babai detectors due to their high efficiency of implementation. In this paper, we first present formulas for the success probabilities (the probabilities of correct detection) of these three detectors for two different situations: the integer parameter vector is deterministic and is uniformly distributed over the constraint box. Then, we give two simple examples to respectively show that the success probability of the box-constrained rounding detector can be larger than that of the box-constrained Babai detector and the latter can be larger than the success probability of the maximum likelihood detector when the parameter vector is deterministic, and prove that the success probability of the box-constrained rounding detector is always not larger than that of the box-constrained Babai detector when the parameter vector is uniformly distributed over the constraint box. Some relations between the results for the box constrained and ordinary cases are presented, and two bounds on the success probability of the maximum likelihood detector, which can easily be computed, are developed. Finally, simulation results are provided to illustrate our main theoretical findings. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments. [ABSTRACT FROM AUTHOR]

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, the transmitter (TX) sends a common information symbol to multiple receivers (RXs) and a fusion center (FC) chooses the TX symbol that is more likely, given the likelihood of its observations from all RXs. The transmission of a sequence of binary symbols and the resultant intersymbol interference are considered in the cooperative MC system. Three ML detection variants are proposed according to different RX behaviors and different knowledge at the FC. The system error probabilities for two ML detector variants are derived, one of which is in closed form. The optimal molecule allocation among RXs to minimize the system error probability of one variant is determined by solving a joint optimization problem. Also for this variant, the equal distribution of molecules among two symmetric RXs is analytically shown to achieve the local minimal error probability. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of simpler, non-ML cooperative variants and demonstrate that these simpler cooperative variants have error performance comparable to ML detectors. [ABSTRACT FROM AUTHOR]

This paper considers the design of short non-binary low-density parity-check (LDPC) codes over finite fields of order $m$ , for channels with phase noise. In particular, $m$ -ary differential phase-shift keying (DPSK)-modulated code symbols are transmitted over an additive white Gaussian noise (AWGN) channel with the Wiener phase noise. At the receiver side, non-coherent detection takes place, with the help of a multi-symbol detection algorithm, followed by a non-binary decoding step. Both the detector and the decoder operate on a joint factor graph. As a benchmark, finite length bounds and information rate expressions are computed and compared with the codeword error rate (CER) performance, as well as the iterative threshold of the obtained codes. As a result, performance within 1.2 dB from finite-length bounds is obtained, down to a CER of 10−3. [ABSTRACT FROM AUTHOR]

Resistive memory is a promising technology for achieving unprecedented storage densities and new in-memory computing features. However, to fulfill their promise, resistive memories require array architectures suffering from a severe interference effect called “sneak paths.” In this paper, we address the sneak-path problem through a communication-theory framework. Starting from the fundamental problem of readout with parallel-resistance interference, we develop several tools for detection and coding that significantly improve memory reliability. For the detection problem, we formulate and derive the optimal detector for a realistic array model, and then propose simplifications that enjoy similarly good performance and simpler implementation. Complementing detection for better error rates is done by a new coding scheme that shapes the stored bits to get lower sneak-path incidence. For the same storage rates, the new coding scheme exhibits error rates lower by an order of magnitude compared to known shaping techniques. [ABSTRACT FROM AUTHOR]

The matched-filter (MF) detector is the lowest complexity linear detector in practical MIMO spatial multiplexing systems. Its performance is significantly affected by the number of available antennas and an operating channel SNR. In this paper, we analyze the exact performance of the MF detector without any restrictions on the numbers of the transmit and receive antennas. To this end, we first develop the exact, closed-form expression of the MF detector output over uncorrelated Rayleigh fading channels with an arbitrary number of antennas. Based on this result, we derive the exact closed-form bit error rate expressions for uncoded BPSK, QPSK, and $M$ -ary QAM signaling, which can be seen as the generalized forms of the conventional receiver diversity based on maximum ratio combining. We also elucidate the conditions on the numbers of antennas and channel SNR, where the use of MF detector should become effective. Furthermore, in the case of coded MIMO systems, we develop the optimal metric derived from the exact MF output. The corresponding mutual information justifies the simulated performance of LDPC-encoded MIMO spatial multiplexing with the MF detector over ideally interleaved Rayleigh fading channels. Finally, we extend our analysis to more practical systems with imperfect channel estimation. [ABSTRACT FROM AUTHOR]

Despite the great promises that the resistive random access memory (ReRAM) has shown as the next generation of non-volatile memory technology, its crossbar array structure leads to a severe sneak path interference to the signal read back from the memory cell. In this paper, we first propose a novel belief propagation (BP) based detector for the sneak path interference in ReRAM. Based on the conditions for a sneak path to occur and the dependence of the states of the memory cells that are involved in the sneak path, a Tanner graph for the ReRAM channel is constructed, inside which specific messages are updated iteratively to get a better estimation of the sneak path affected cells. We further combine the graph of the designed BP detector with that of the BP decoder of the polar codes to form a joint detector and decoder. Tailored for the joint detector and decoder over the ReRAM channel, effective polar codes are constructed using the genetic algorithm. Simulation results show that the BP detector can effectively detect the cells affected by the sneak path, and the proposed polar codes and the joint detector and decoder can significantly improve the error rate performance of ReRAM. [ABSTRACT FROM AUTHOR]

In this paper, we establish a framework for low probability of detection (LPD) communication from a sequential change-point detection (SCPD) perspective, where a transmitter, Alice, wants to hide her transmission to a receiver, Bob, from an adversary, Willie. The new framework facilitates modeling LPD communication and further evaluating its performance under the condition that Willie has no prior knowledge about when the transmission from Alice might start and that Willie wants to determine the existence of the communication as quickly as possible in a real-time manner. We consider three different sequential tests, i.e., the Shewhart, the cumulative sum (CUSUM), and the Shiryaev-Roberts (SR) tests, to model Willie’s detection process. Communication is said to be covert if it ceases before being detected by Willie with high probability. Covert probability defined as the probability that Willie is not alerted during Alice’s transmission is investigated. We formulate an optimization problem aiming at finding the transmit power and transmission duration so as to maximize the total amount of information that can be transmitted subject to a high covert probability. Under the Shewhart test, closed-form approximations of the optimal solutions are derived, which will approximate the solutions obtained from exhaustive search. As for the CUSUM and SR tests, we provide effective algorithms to search for the optimal solutions. Numeric results are presented to show the performance of LPD communication. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cognitive radio (CR) network that consists of multiple secondary users (SUs), each of which is equipped with an energy harvesting capability. We consider the opportunistic use of the idle spectrum, unused by primary users (PUs), through a mechanism of two-step opportunistic spectrum access for the SUs, consisting of random channel sensing followed by random channel access. Unlike the majority of previous studies, which have considered CR networks with energy harvesting under a single user setting, the goal of this paper is to investigate the joint impact of sensing probability, access probability, and energy queue capacity on the maximum achievable throughput in a multiuser CR network incorporating energy harvesting. For two extreme cases, those where the energy queue capacity is either infinite or extremely small, we show that the maximum achievable throughput is not affected by the channel access probability if the channel sensing probability is chosen appropriately and the optimal sensing probability is derived as a function of network parameters such as the energy arrival rate, channel availability probability, and number of contending SUs. [ABSTRACT FROM AUTHOR]

It is well known that matched filtering and sampling (MFS) demodulation together with minimum Euclidean distance (MD) detection constitute the optimal receiver for the additive white Gaussian noise channel. However, for a general nonlinear transmission medium, MFS does not provide sufficient statistics, and, therefore, is suboptimal. Nonetheless, this receiver is widely used in optical systems, where the Kerr nonlinearity is the dominant impairment at high powers. In this paper, we consider a suite of receivers for a two-user channel subject to a type of nonlinear interference that occurs in wavelength-division-multiplexed channels. The asymptotes of the symbol error rate (SER) of the considered receivers at high powers are derived or bounded analytically. Moreover, Monte-Carlo simulations are conducted to evaluate the SER for all the receivers. Our results show that receivers that are based on MFS cannot achieve arbitrary low SERs, whereas the SER goes to zero as the power grows for the optimal receiver. Furthermore, we devise a heuristic demodulator, which together with the MD detector yields a receiver that is simpler than the optimal one and can achieve arbitrary low SERs. The SER performance of the proposed receivers is also evaluated for some single-span fiber-optical channels via split-step Fourier simulations. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and data-driven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called $\alpha $ -sum and $\alpha $ -product of maximum and minimum eigenvalues ($\alpha $ -SMME, $\alpha $ -PMME) are further designed, in which $\alpha $ is a weight coefficient. Furthermore, the theoretical performance analysis of the $\alpha $ -SMME and $\alpha $ -PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector. [ABSTRACT FROM AUTHOR]

This paper presents the design and bit error rate (BER) analysis of a phase-independent non-orthogonal multiple access (NOMA) system. The proposed NOMA system can utilize amplitude-coherent detection (ACD) which requires only the channel amplitude for equalization purposes. In what follows, three different designs for realizing the detection of the proposed NOMA are investigated. One is based on the maximum likelihood (ML) principle, while the other two are based on successive interference cancellation (SIC). Closed-form expressions for the BER of all detectors are derived and compared with the BER of the coherent ML detector. The obtained results, which are corroborated by simulations, demonstrate that, in most scenarios, the BER is dominated by multiuser interference rather than the absence of the channel phase information. Consequently, the BER using ML and ACD are comparable for various cases of interest. The paper also shows that the SIC detector is just an alternative approach to realize the ML detector, and hence, both detectors provide the same BER performance. [ABSTRACT FROM AUTHOR]

The key role of spectrum sensing in cognitive radios attracted substantial research attention to improve the performance of detectors. We consider a general model for the receiver noise with the potential of generalization towards modeling noise in various environments. This general noise model describes accurately noise characteristics ranging from the Gaussian noise to the severe impulsive noise. However, many previous studies are based on the ideal Gaussian noise, and they cannot capture the non-Gaussian models. To provide a robust detector against different behaviors of the noise in various environments, we employ a convolutional neural network (CNN) compatible with different noise models. The proposed CNN detector is data-driven, and due to its single-dimensional input layer, it is consistent with the received signal and requires no pre-processing. The likelihood ratio test (LRT), the Wald, and the Rao tests for this problem are derived to enrich the paper with comparative evaluations of the proposed CNN and conventional model-based approaches and other neural networks. Although various simulated scenarios substantiate the general superiority and robustness of the CNN-based against impulsive noise and mismatch of parameters, it requires higher computational complexity than other discussed detectors. [ABSTRACT FROM AUTHOR]

Compressive random access (CRA) is a random access scheme that has been considered for massive machine-type communication (MTC) with non-orthogonal spreading sequences, where the notion of compressive sensing (CS) is used for low-complexity detectors by exploiting the sparse device activity. In this paper, we study the application of power-domain non-orthogonal multiple access (NOMA) to CRA in order to improve the performance of CRA (or increase the number of devices to be supported). We consider Gaussian spreading sequences and derive design criteria through a large-system analysis to determine the power levels for power-domain NOMA. From simulation results, we can confirm that the number of incorrectly detected device activity can be reduced by applying NOMA to CRA. [ABSTRACT FROM AUTHOR]

In many applications, including integer-forcing linear multiple-input and multiple-output (MIMO) receiver design, one needs to solve a successive minima problem (SMP) on an $n$ -dimensional lattice to get an optimal integer coefficient matrix $\boldsymbol {A}^\star \in {Z}^{n\times n}$. In this paper, we first propose an efficient optimal SMP algorithm with an ${\mathcal {O}}(n^{2})$ memory complexity. The main idea behind the new algorithm is it first initializes with a suitable suboptimal solution, which is then updated via a novel algorithm with only ${\mathcal {O}}(n^{2})$ flops in each updating, until $\boldsymbol {A}^{\star }$ is obtained. Different from existing algorithms which find $\boldsymbol {A}^\star $ column by column through using a sphere decoding search strategy $n$ times, the new algorithm uses a search strategy once only. We then rigorously prove the optimality of the proposed algorithm. Furthermore, we theoretically analyze its complexity. In particular, we not only show that the new algorithm is $\Omega (n)$ times faster than the most efficient existing algorithm with polynomial memory complexity, but also assert that it is even more efficient than the most efficient existing algorithm with exponential memory complexity. Finally, numerical simulations are presented to illustrate the optimality and efficiency of our novel SMP algorithm. [ABSTRACT FROM AUTHOR]

Random access has been considered for machine-type communication (MTC). In particular, a random access scheme with a pool of preambles is widely studied, which is similar to multichannel ALOHA, to support a number of MTC devices. In this paper, we study optimal approaches for user activity detection in random access with preambles over fading channels. Since the computational complexity grows exponentially with the number of preambles, a low-complexity detector is derived using Markov chain Monte Carlo (MCMC) approaches that can approximately solve an optimal maximum a posteriori detection problem. The resulting MCMC detector can enjoy a trade-off between performance and complexity, while its complexity to obtain a sample is linearly proportional to the number of preambles. A performance analysis for optimal detection is also studied to see the optimal performance. Simulation results confirm that the MCMC detector performs better than compressive sensing-based approaches and can provide a near optimal performance under certain conditions with a reasonable computational complexity. [ABSTRACT FROM AUTHOR]

This paper investigates the constellation/codebook design of a promising uplink multiple access technique, sparse code multiple access (SCMA), proposed for the fifth generation mobile networks. The application of bit-interleaved coded modulation with iterative multiuser detection is considered for uplink SCMA over fading channels. Extrinsic information transfer chart is used to aid the analysis and the design of multidimensional constellations, and the design criteria for multidimensional constellations and labelings optimization are thus established. Furthermore, a new and simple approach of multi-stage optimization for the multidimensional constellation design is proposed for SCMA, to improve the bit-error rate performance and alleviate the complexity of turbo multiuser detection. Numerical and simulation results are also provided to demonstrate the performance and verify the efficiency of the proposed scheme, compared with the state of the art. [ABSTRACT FROM AUTHOR]

In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a pair of high-resolution analog-to-digital converters (ADCs) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system. [ABSTRACT FROM PUBLISHER]

We consider a low complexity polynomial expansion (PE) detector in a massive multiple-input multiple-output (MIMO) uplink channel. In contrast to most massive MIMO systems in the literature, where single antenna user equipments (UEs) are assumed, multiple antenna UEs are employed in this paper. Moreover, the channel between a base station (BS) and a UE is a jointly correlated Rician fading channel. The PE detector reduces the computational complexity of the minimum mean square error (MMSE) detector by replacing the matrix inversion with an approximate matrix polynomial. The coefficients of the approximate matrix polynomial are computed from the deterministic equivalents of the moments of the channel Gram matrix. We use operator-valued free probability, which is a more general version of free probability, to derive the deterministic equivalents. In particular, we use the operator-valued moment-cumulant formula. The proposed low complexity PE detector is easy to compute. Simulation results show that the proposed detector can achieve performance close to the MMSE detector. [ABSTRACT FROM AUTHOR]

This paper proposes a cross-layer optimization framework for the wireless sensor networks. In a wireless sensor network, each sensor makes a local observation of the underlying physical phenomenon and sends a quantized version of the observation to a central location via wireless links. As the sensor observations are often partial and correlated, the network performance is a complicated and nonseparable function of individual data rates at each sensor. In addition, due to the shared nature of wireless medium, nearby transmissions often interfere with each other. Thus, the traditional "bit-pipe" model for network link capacity no longer holds. This paper deals with the joint optimization of source quantization, routing, and power control in a wireless sensor network. We follow a separate source and channel coding approach and show that the overall network optimization problem can be naturally decomposed into a source coding subproblem at the application layer and a wireless power control subproblem at the physical layer. The interfaces between the layers are precisely the dual optimization variables. In addition, we introduce a novel source coding model at the application layer, which allows the efficient design of practical source quantization schemes at each sensor. Finally, we propose a dual algorithm for the overall network optimization problem. The dual algorithm, when combined with a column-generation method, allows an efficient solution for the overall network optimization problem. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC) enables radio-frequency (RF) powered backscatter devices (BDs) (e.g., sensors and tags) to modulate their information bits over ambient RF carriers in an over-the-air manner. This technology, also called “modulation in the air,” has emerged as a promising solution to achieve green communication for future Internet of Things. This paper studies an AmBC system by leveraging the ambient orthogonal frequency division multiplexing (OFDM) modulated signals in the air. We first model such AmBC system from a spread-spectrum communication perspective, upon which a novel joint design for BD waveform and receiver detector is proposed. The BD symbol period is designed as an integer multiplication of the OFDM symbol period, and the waveform for BD bit “0” maintains the same state within the BD symbol period, while the waveform for BD bit “1” has a state transition in the middle of each OFDM symbol period within the BD symbol period. In the receiver detector design, we construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. For the system with a single-antenna receiver, the maximum-likelihood detector is proposed to recover the BD bits, for which the optimal threshold is obtained in closed-form expression. For the system with a multi-antenna receiver, we propose a new test statistic which is a linear combination of the per-antenna test statistics and derive the corresponding optimal detector. The proposed optimal detectors require only knowing the strength of the backscatter channel, thus simplifying their implementation. Moreover, practical timing synchronization algorithms are proposed for the designed AmBC system, and we also analyze the effect of various system parameters on the transmission rate and detection performance. Finally, extensive numerical results are provided to verify that the proposed transceiver design can improve the system bit-error-rate performance and the operating range significantly and achieve much higher data rate, as compared with the conventional design. [ABSTRACT FROM PUBLISHER]

Jointly detecting the signals from the source and relay in a spatial multiplexing (SM) multiple-input multiple-output (MIMO) relaying system improves the transmit reliability significantly. However, the existing joint detection schemes for SM MIMO relaying systems, which achieve full diversity, such as the near maximum likelihood (ML) decoder, suffer from high complexity. In this paper, we propose a weighted combining (WC) algorithm, which is applied before the detector in the SM MIMO decode-and-forward relaying system. The proposed algorithm merges the received signal vectors from the source and relay into a combined signal without expanding their dimension, and formulates an equivalent MIMO channel matrix for the combined signal, resulting in a lower complexity for the subsequent detection. We analyze the performance of the proposed WC algorithm with ML detection in terms of the diversity order and computational complexity. An approximate upper bound on the symbol error probability (SEP) for the proposed algorithm is also derived. Simulation results show that in symmetric networks, the proposed WC algorithm achieves substantially lower complexity, while maintaining an SEP performance similar to that of the benchmark NML decoder. The consistency of the derived upper bound on the SEP is also verified by simulations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the pulse amplitude modulation (PAM) decomposition and the spectral efficiency of duobinary continuous phase modulation (CPM), where duo- binary CPM represents a ternary CPM whose symbols are generated using a duobinary encoder. We show that this precoder ensures a higher spectral efficiency compared with binary CPM and we propose a PAM decomposition characterized by the presence of a single main pulse and by uncorrelated pseudo-symbols for a half integer modulation index value for this ternary CPM. We show that shaped offset quadrature phase shift keying belongs to this CPM family and we provide a new reduced complexity trellis detector capable of reaching identical performance as the one introduced by Perrins as well as new linear detectors. [ABSTRACT FROM AUTHOR]

For many applications envisioned for the Internet of Things (IoT), it is expected that the sensors will have very low costs and zero power, which can be satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their constituent meta-materials can reflect wireless signals with environment-sensitive reflection coefficients, meta-IoT sensors can achieve simultaneous sensing and transmission without any active modulation. However, to maximize the sensing accuracy, the structures of meta-IoT sensors need to be optimized considering their joint influence on sensing and transmission, which is challenging due to the high computational complexity in evaluating the influence, especially given a large number of sensors. In this paper, we propose a joint sensing and transmission design method for meta-IoT systems with a large number of meta-IoT sensors, which can efficiently optimize the sensing accuracy of the system. Specifically, a computationally efficient received signal model is established to evaluate the joint influence of meta-material structure on sensing and transmission. Then, a sensing algorithm based on deep unsupervised learning is designed to obtain accurate sensing results in a robust manner. Experiments with a prototype verify that the system has a higher sensitivity and a longer transmission range compared to existing designs, and can sense environmental anomalies correctly within 2 meters. [ABSTRACT FROM AUTHOR]

Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithm (QAOA) constitutes a promising candidate for demonstrating tangible quantum advantages based on NISQ devices. In this paper, we consider the maximum likelihood (ML) detection problem of binary symbols transmitted over a multiple-input and multiple-output (MIMO) channel, where finding the optimal solution is exponentially hard using classical computers. Here, we apply the QAOA for the ML detection by encoding the problem of interest into a level- $p$ QAOA circuit having $2p$ variational parameters, which can be optimized by classical optimizers. This level- $p$ QAOA circuit is constructed by applying the prepared Hamiltonian to our problem and the initial Hamiltonian alternately in $p$ consecutive rounds. More explicitly, we first encode the optimal solution of the ML detection problem into the ground state of a problem Hamiltonian. Using the quantum adiabatic evolution technique, we provide both analytical and numerical results for characterizing the evolution of the eigenvalues of the quantum system used for ML detection. Then, for level-1 QAOA circuits, we derive the analytical expressions of the expectation values of the QAOA and discuss the complexity of the QAOA based ML detector. Explicitly, we evaluate the computational complexity of the classical optimizer used and the storage requirement of simulating the QAOA. Finally, we evaluate the bit error rate (BER) of the QAOA based ML detector and compare it both to the classical ML detector and to the classical minimum mean squared error (MMSE) detector, demonstrating that the QAOA based ML detector is capable of approaching the performance of the classical ML detector. [ABSTRACT FROM AUTHOR]

The Laplacian noise has received much attention during the recent years since it affects many communication systems. We consider in this paper the probability of error of an M-ary phase shift keying (PSK) constellation operating over a generalized fading channel in presence of a dominant additive Laplacian noise. In this context, the decision regions of the receiver are determined using the maximum likelihood and the minimum distance detectors. Once the decision regions are extracted, the resulting symbol error rate expressions are computed and averaged over an extended generalized-K fading distribution. Generic closed form expressions of the conditional and the average probability of error are obtained in terms of the Fox's H function. Simplifications for some special cases of fading are presented and the resulting formulas end up being often expressed in terms of well known elementary functions. Finally, the mathematical formalism is validated using some selected analytical-based numerical results as well as Monte-Carlo simulation-based results. [ABSTRACT FROM PUBLISHER]

This paper considers wide-band spectrum sensing and optimization for cognitive radio (CR) networks with noise variance uncertainty. It is assumed that the considered wide-band contains one or more white sub-bands. Under this assumption, we consider throughput maximization of the CR network while appropriately protecting the primary network. We address this problem as follows. First, we propose novel ratio based test statistics for detecting the edges of each sub-band. Second, we employ simple energy comparison approach to choose one reference white sub-band. Third, we propose novel generalized energy detector (GED) for examining each of the remaining sub-bands by exploiting the noise information of the reference white sub-band. Finally, we optimize the sensing time (To ) to maximize the CR network throughput using the detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to noise ratio (SNR wall and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED is quantified analytically. We show that the optimal To depends on the noise variance information. In particular, with 10TV bands, \SNR=-20\ \dB and 2s frame duration, we found that the optimal To is 28.5 ms (50.6 ms) with perfect (imperfect) noise variance scenario. [ABSTRACT FROM PUBLISHER]

In this paper we analyze design aspects of a single-carrier digital communications receiver in the presence of impulsive noise. We use the additive white symmetric $\alpha$-stable noise (\AWS\alpha\SN) to model the channel noise. By introducing passband sampling, efficient constellations and suitable baseband detectors, we show that the uncoded error performance of the conventional (linear) receiver can be enhanced given the real and imaginary components of the transmitted symbol are decoded separately. The performance may be improved further by sacrificing the linearity of the system. Various non-linear estimation and joint-detection schemes are discussed and their error performance analyzed. It is shown that if the receiver bandwidth is large enough, impulsive noise may be effectively countered in a single-carrier communications system. [ABSTRACT FROM AUTHOR]

Ambient backscatter devices (tags and readers) use existing radio frequency (RF) signals to transmit data. Most prior works consider single-antenna tags, but this paper investigates the case of multiple-antenna tags, which are capable of simultaneous energy harvesting and data transmission. However, the multi-antenna channel between the tag and the reader, and the unpredictable nature of RF signals due to uncontrollable RF sources (e.g., location and transmit power), make signal detection highly challenging. Thus, the detection process becomes a hypothesis testing problem with unknown parameters. Consequently, we design a blind detector based on the generalized likelihood ratio test (GLRT) without using channel state information (CSI), signal power and noise variance. The decision threshold and detection probability of it are also analyzed in detail. Furthermore, to maximize its detection performance, we develop the optimal backscatter antenna selection scheme. Interestingly, we show that the detector performs best when only two backscatter antennas are selected. Finally, extensive simulation results validate the analysis and illustrate the effectiveness of the proposed detector. [ABSTRACT FROM AUTHOR]

This paper considers the uplink of a cloud radio access network (C-RAN) comprised of several multi-antenna remote radio units (RUs) which compress the signals that they receive from multiple mobile users (MUs) and forward them to a CU via wireless fronthaul links. To enable reliable high rate fronthaul links, we employ a hybrid radio frequency (RF)/free space optical (FSO) system for fronthauling. Moreover, to strike a balance between complexity and performance, we consider three different quantization schemes at the RUs, namely per-antenna vector quantization (AVQ), per-RU vector quantization (RVQ), and distributed source coding (DSC), two different RF fronthaul transmission modes, namely orthogonal transmission and non-orthogonal transmission, and two different detectors at the CU, namely the linear minimum mean square error detector and the optimal successive interference cancellation detector. For this network architecture, we investigate the joint optimization of the quantization noise covariance matrices at the RUs and the RF time allocation to the multiple-access and fronthaul links for rate region maximization. To this end, we formulate a unified weighted sum rate maximization problem valid for each possible combination of the considered quantization, RF fronthaul transmission, and detection schemes. To handle the non-convexity of the unified problem, we transform it into a bi-convex problem which facilitates the derivation of an efficient suboptimal solution using alternating convex optimization and golden section search. Moreover, by introducing a backoff parameter to reduce the probability of infeasibility, we generalize the proposed optimization framework to account for imperfect channel estimation. Our simulation results show that for each combination of the considered quantization, RF fronthaul transmission, and detection schemes, C-RAN with hybrid RF/FSO fronthauling can achieve a considerable sum rate gain compared to conventional systems employing pure FSO fronthauling, especially under unfavorable atmospheric conditions. In addition, employing a more sophisticated quantization scheme can significantly improve the system performance under adverse atmospheric conditions. In contrast, in clear weather conditions, when the FSO link capacity is high, the simple AVQ scheme performs close to the optimal DSC scheme. Furthermore, our simulation results suggest that the proposed algorithm can be adapted to the quality of the channel estimates by tuning the backoff parameter. [ABSTRACT FROM AUTHOR]

In this paper, we consider the design of spiral constellations for channels affected by phase noise. The strength of the proposed constellations resides both on the performance and on the extreme simplicity of the design. The symbols can in fact be expressed in analytical form, and are uniquely defined through a single parameter that accounts for the phase and thermal noise variances. The structure of the proposed constellations allows to easily determine the points that are closest to any point in the complex plane, therefore we also propose a low complexity detector that is suitable for phase noise channels in medium-high signal-to-noise ratio conditions. The performance of the proposed constellations are assessed in terms of information rate and error rate. Despite their simplicity, the new spiral constellations have excellent performance, especially when the constellation size grows large. [ABSTRACT FROM AUTHOR]

In this paper, we investigate differential amplitude/phase modulation (APM) schemes for correlated Rayleigh fading channels. Considering the noncoherent maximum-likelihood receiver, a theoretical pairwise error probability (PEP) formula for differential signal vectors with arbitrary power values is derived. The design criterion for the signal set of differential vectors is then proposed based on the asymptotic analysis of the PEP. For both uncoded and coded APM schemes, the design criteria and optimization procedures for bit labeling rules are investigated. The designed differential APM schemes are shown to provide significant improvement in bit-error-rate performance, particularly in the error-floor regions. [ABSTRACT FROM PUBLISHER]

The detection of a weak signal in additive Cauchy noise is of great importance in many applications. A locally optimum detector (LOD) exists for such a scenario; however, it is non-linear in nature. In general, implementation of non-linear detectors is difficult in practice, and linear detectors with good properties, such as high asymptotic relative efficiency (ARE) with respect to the LOD, are often desirable. In this paper, we propose a linear detector for a weak signal in additive Cauchy noise. The proposed test statistic is a linear combination of order statistics. For the special case of a constant signal in additive Cauchy noise, we prove the asymptotic normality of the trimmed linear detector, and show that the ARE of the trimmed linear detector with respect to the LOD is unity. Extensive simulation results are provided to demonstrate that the loss in the performance of the linear detector is very small compared with the non-linear LOD. We also discuss the hardware complexities of the LOD and the linear detector, and demonstrate the advantages of the linear detector over the LOD, in terms of hardware implementation. [ABSTRACT FROM AUTHOR]

This paper studies the temporal characteristics of the intelligent reflecting surface (IRS)-based visible light communication (VLC) channel using radiometric concepts. Throughout this study, we account for the delays experienced by the transmitted power along the continuum of paths originating at the source, passing through the IRS, reaching the detector. Then, we derive the impulse response of multi-element phase-tunable metasurface and orientation-tunable mirror array-based reflector setups for a general setting of source, reflector, and detector dimensions and relative positions. In addition, we derive simpler expressions for the two special cases, namely, the point source and the large-source small-reflector. Moreover, we present the exact expression for the delay spread and derive lower, upper bounds and asymptotic expressions when the number of reflecting elements increases for both reflector types. Finally, we study the impact of several system parameters on the temporal characterization of the two IRS-based VLC systems. [ABSTRACT FROM AUTHOR]

Cloud-based cell-free massive multiple-input multiple-output (CFmMIMO) technology, which exploits a large number of distributed antennas to cooperatively serve multiple users, constitutes an appealing technique for B5G/6G wireless communications. However, the distributed nature of cloud-based CFmMIMO imposes great challenges in analyzing the error probability bounds, and very few efforts have so far been paid to optimize the detector design. In this paper, we try to add a stroke to this blank by analyzing the symbol error rate (SER) and design near-optimal detection algorithms. Specifically, considering non-identical large-scale coefficients and channel estimation errors, we first leverage the pairwise error probability to derive an asymptotic SER bound for uplink cloud-based CFmMIMO systems, which is verified by simulation results. Furthermore, motivated by the concepts of successive interference cancellation (SIC) and error correction mechanism (ECM), we design two distinct types of near-optimal detectors for cloud-based CFmMIMO systems and analyze their complexity and convergence performance. Finally, extensive simulation results show that our proposed SIC and ECM based detectors outperform conventional matched filtering (MF) and minimum mean squred error (MMSE) counterparts. In particular, the MMSE-SIC and MMSE-ECM detectors approach the derived asymptotic bound, and the MF-ECM detector strikes a balance between the SER and complexity in ultra CFmMIMO scenarios. [ABSTRACT FROM AUTHOR]

The level crossing rate (LCR) and the average outage duration (AOD) are two important second order statistics that allow a deeper understanding of the behavior of the channel. In this paper, we study these metrics in order to assess the performance of free space optical (FSO) communication links in the presence of weak atmospheric turbulence and rice-induced pointing errors. More specifically, we derive an integral and a Gauss–Laguerre quadrature representation for both the LCR and the AOD in the single hop case and for their respective bounds in the multihop case. Selected numerical simulations are presented to show the accuracy of the derived results and to study the effect of certain system parameters on these two performance metrics. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the achievable rate for ambient backscatter communications under three different channels: the binary input and binary output (BIBO) channel, the binary input and signal output (BISO) channel, and the binary input and energy output (BIEO) channel. Instead of assuming Gaussian input distribution, the proposed study matches the practical ambient backscatter scenarios, where the input of the tag can only be binary. We derive the closed-form capacity expression as well as the capacity-achieving input distribution for the BIBO channel. To show the influence of the signal-to-noise ratio (SNR) on the capacity, a closed-form tight ceiling is also derived when SNR turns relatively large. For BISO and BIEO channel, we obtain the closed-form mutual information, while the semi-closed-form capacity value can be obtained via one dimensional searching. Simulations are provided to corroborate the theoretical studies. Interestingly, the simulations show that: (i) the detection threshold maximizing the capacity of BIBO channel is the same as the one from the maximum likelihood signal detection; (ii) the maximal of the mutual information of all channels is achieved almost by a uniform input distribution; and (iii) the mutual information of the BIEO channel is larger than that of the BIBO channel, but is smaller than that of the BISO channel. [ABSTRACT FROM AUTHOR]

In this paper, we conceive a compressed sensing (CS)-aided multi-dimensional index modulation (IM) scheme, where the benefits of space–time shift keying, orthogonal frequency-division multiplexing relying on the frequency domain IM, and spatial modulation are explored. Explicitly, extra information bits are transmitted through the active indices of both the transmit antennas and subcarriers, while striking a flexible design tradeoff between the throughput and the diversity order. Furthermore, CS is invoked in both the transmitter and the receiver of our multi-dimensional system for the sake of improving the system’s design flexibility, while reducing the detector’s complexity. We first present the maximum likelihood (ML) detector of the proposed CS-aided multi-dimensional IM system for characterizing the best-case bound of the proposed system’s performance. Specifically, an upper bound is derived for the average bit error probability, and it is observed that the derived theoretical upper bound becomes very tight with the ML detector simulation curves as the signal-to-noise ratio increases. Then, we propose a reduced complexity detector imposing only a modest bit-error-ratio degradation, where we analyze the computational complexities of both the ML detector and the reduced complexity detector. Furthermore, a soft-input soft-output decoder is proposed for attaining a near-capacity performance, which is analyzed with the aid of extrinsic information transfer (EXIT) charts. The maximum achievable rate of the proposed CS-aided multi-dimensional IM system relying both on the ML detection and on our reduced-complexity-based detector is also evaluated using EXIT charts. In addition, the discrete-input continuous-output memoryless channel capacity of the proposed CS-aided multi-dimensional IM scheme is formulated. [ABSTRACT FROM AUTHOR]

Diffusion-based molecular communication (DMC) is one of the most promising approaches for realizing nano-scale communications for healthcare applications. The DMC systems in in-vivo environments may encounter biological entities that release molecules identical to the molecules used for signaling as part of their functionality. Such entities in the environment act as external noise sources from the DMC system’s perspective. In this paper, the release of molecules by external bio-inspired noise sources is particularly modeled as a compound Poisson process. The impact of compound Poisson noise sources (CPNSs) on the performance of a point-to-point DMC system is investigated. To this end, the noise from the CPNS observed at the receiver is characterized. Considering a simple on-off keying modulation and formulating symbol-by-symbol maximum likelihood (ML) detector, the performance of the DMC system in the presence of the CPNS is analyzed. For the special case of CPNS in a high-rate regime, the noise received from the CPNS is approximated as a Poisson process whose rate is normally distributed. In this case, it is proved that a simple single-threshold detector is an optimal ML detector. Our results reveal that in general, adopting the conventional simple homogeneous Poisson noise model may lead to overly optimistic performance predictions, if a CPNS is present. [ABSTRACT FROM AUTHOR]

We study the optimal resource allocation in the MIMO-based wireless powered underground sensor network (WPUSN) for throughput maximization. Compared to existing WPUSNs that rely on single-antenna wireless power transfer techniques to transmit geological data in real time, the MIMO-based WPUSN can be adaptively replenished by beamforming. Note that, WPUSN has two remarkable features: the severe wireless path loss and diverse data traffic demands from different underground sensors. Further, by considering the quality of service (QoS) with respect to diverse data traffic demands and communication reliability, the throughput of the network is considered as a crucial measure, which suffers from a significant loss since it has to encounter a seriously unreasonable energy scarcity under the strong-heterogeneity underground environment. To this end, the MIMO is used to eliminate the unreasonable distribution of harvested energy resources and improve effective throughput for WPUSNs. In this paper, we formulate a non-convex optimization problem to maximize the system throughput in MIMO-based WPUSNs with QoS assurance. Specifically, we find there exists no mutual forbearance between the time allocation and the beamforming weight, and transform this non-convex problem to a solvable convex-constrained problem with convex sub-problems. Finally, we give a closed-form solution and show its advantages by simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a practical synchronization waveform that is resilient to frequency error for machine-type communications with applications in massive Internet of Things (IoT). Mathematical properties of the waveform are derived, which are keys to addressing the practical issues. In particular, it is shown that this type of waveform is asymptotically optimal in the presence of a frequency error, in the sense that its asymptotic performance is the same as the optimal matched-filter detector that is free of frequency error. This asymptotic property enables optimization of the waveform under the constraints imposed by an application. It is also shown that such optimized waveform comes in pairs, which facilitates the formation of a new waveform capable of frequency error estimation and timing refinement at the receiver. Detailed comparisons with the LTE narrowband IoT primary synchronization signal are provided. [ABSTRACT FROM AUTHOR]

Index modulation (IM) is an attractive concept for next generation communication systems. However, as an emerging technique, there are still some challenges need to be tackled in this frontier. In this paper, we consider two aspects of IM, which are diversity and detection complexity. Specifically, we propose a linear precoding assisted index modulation (LPIM) scheme for orthogonal frequency division multiplexing (OFDM) systems. We commence by analyzing the diversity and coding gains of the proposed scheme. Moreover, a detailed codebook design criterion is proposed. Then, our LPIM codebook is designed based on the maximum diversity and coding gain criteria. In contrast to the signaling model of the existing full diversity precoder designed for OFDM, we introduce a modeling method to link the zero-valued IM symbols to the origin of a Lattice for implementing our full diversity precoder designed for OFDM-IM. Both analytical and computer simulation results are provided for characterizing the attainable performance of OFDM-LPIM, demonstrating that it is capable of achieving full diversity as well as an attractive coding gain. However, the maximum-likelihood (ML) detection complexity of OFDM-LPIM is excessive, hence a low-complexity generalized iterative residual check detector (GIRCD) is proposed, which is inspired by the existing sparse recovery algorithms. Finally, computer simulation results are provided for demonstrating that GIRCD can provide a beneficial trade-off between bit error ratio performance and complexity. [ABSTRACT FROM AUTHOR]

We analyze the symbol error probability (SEP) of $M$ -ary pulse amplitude modulation ($M$ -PAM) receivers equipped with optimal low-resolution quantizers. We first show that the optimum detector can be reduced to a simple decision rule. Using this simplification, an exact SEP expression for quantized $M$ -PAM receivers is obtained when Nakagami- $m$ fading channel is considered. The derived expression enables the optimization of the quantizer and/or constellation under the minimum SEP criterion. Our analysis of optimal quantization for equidistant $M$ -PAM receiver reveals the existence of error floor which decays at a double exponential rate with increasing quantization bits, $b$. Moreover, by also allowing the transmitter to optimize the constellation based on the statistics of the fading channel, we prove that the error floor can be eliminated but at a lower decay exponent than the unquantized case. Characterization of this decay exponent is provided in this paper. We also expose the outage performance limitations of SEP-optimal uniform quantizers. To be more precise, its decay exponent does not improve with $b$. Lastly, we demonstrate that the decay exponent of a quantized receiver can be complemented by receive antenna diversity techniques. [ABSTRACT FROM AUTHOR]

Hardware impairment is inevitable in many wireless systems. It is particularly severe in low-cost applications due to the imperfect components used. In this paper, the channel estimation and non-coherent detection problems of hardware impaired signals are studied for a single-carrier, single-antenna and single-hop system. Specifically, three different cases are investigated: signals with additive distortion only, signals with in-phase and quadrature imbalance only, and signals with both impairments. The maximum likelihood and Gaussian approximation methods are used to derive the new non-coherent detectors for amplitude modulated signals, while the maximum likelihood and moment-based methods are employed to design the new channel estimators for all signals. Numerical results show that the new non-coherent detectors outperform the existing non-coherent detectors in the presence of hardware impairment. The performance gain can be as high as 8 dB. They also show that the new channel estimators have much higher accuracy than the existing estimator. In some conditions, the accuracy of the new estimator is about 100 times that of the existing estimator. [ABSTRACT FROM AUTHOR]

In this paper, we propose a receiver structure for single-carrier uplink transmission with frequency domain equalization (FDE) that is exposed to power amplifier (PA) nonlinearities. A two-stage approach is adopted, in which linear communication channel is equalized at the first stage, and it is followed by post-distortion, where nonlinear distortion is reduced. In the literature, nonlinear processing techniques are proposed, which perform memoryless compensation of nonlinear distortion together with FDE. However, in this study, we show that even if memoryless nonlinearity exists, the received signal is impaired by nonlinear inter-symbol-interference. Therefore, we propose a class of symbol rate post-distortion techniques, which use neighboring received symbols to suppress the nonlinear interference. Three different post-distortion methods, Gaussian process regression (GPR), neural network (NN) and Volterra series (VS) based post-distorters, are considered. Also, a combiner, which intelligently combines the outputs of fractional delayed bank of FDE’s after post-distortion, is proposed to overcome performance degradation of FDE for frequency selective channels under nonlinear distortion. Performances of the proposed techniques are compared with state-of-the-art approaches in terms of bit error rate (BER) and achievable information rate (AIR) metrics. Simulation results demonstrate that post-distortion methods together with bank of FDE outperform state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this paper, we provide a comprehensive analysis of the error floor along with code optimization guidelines for structured and regular non-binary low-density parity-check (NB-LDPC) codes in magnetic recording (MR) applications. While the topic of the error floor performance of binary LDPC codes over additive white Gaussian noise (AWGN) channels has recently received considerable attention, very little is known about the error floor performance of NB-LDPC codes over other types of channels, despite the early results demonstrating superior characteristics of NB-LDPC codes relative to their binary counterparts. We first show that, due to the outer looping between the detector and the decoder in the receiver, the error profile of NB-LDPC codes over partial-response (PR) channels is qualitatively different from the error profile over AWGN channels—this observation motivates us to introduce new combinatorial objects aimed at capturing decoding errors that dominate the PR channel error floor region. We call these objects balanced absorbing sets (BASs), which are viewed as a special subclass of previously introduced absorbing sets (ASs). Aided by these new objects (BASs), we develop a method that combines analytical equations and biased simulations to predict the error floor performance of NB-LDPC codes over PR channels without the need to execute extensive Monte Carlo (MC) simulations. We show that explicitly incorporating the inter-symbol interference of MR channels into our prediction method makes the accuracy of the error floor estimate within 0.2 of an order of magnitude from the traditional MC simulation. In addition, we prove that, due to the more restrictive definition of BASs (relative to the more general class of ASs), an additional degree of freedom can be exploited in the code design for PR channels. We then demonstrate that the proposed code optimization aimed at removing dominant BASs offers performance improvements in the frame error rate in the error floor region by up to 2.5 orders of magnitude over the unoptimized designs. Our code optimization technique carefully, yet provably, removes BASs from the code while preserving its overall structure (node degree, quasi-cyclic property, regularity, and so forth). The resulting codes outperform the existing binary and NB-LDPC solutions for PR channels by about 2.5 and 1.25 orders of magnitude, respectively. [ABSTRACT FROM AUTHOR]

Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission scheme in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as generalized SM (GSM), can use the maximum-likelihood (ML) detector jointly detecting the antenna-subset (AS) index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity can be prohibitively high when the dimension of the GSM system is large. In this paper, we propose new methods to solve the problem. The main idea is to split the detection into two stages, one for the AS index and the other for the symbol vector. For the detection of the AS index, we develop two methods, referred to as Gaussian approximation and QR projection. Once the AS index is detected, conventional low-complexity ML detectors can be applied for the detection of the symbol vector. The diversity order for the proposed methods are further analyzed and an enhanced method is also proposed to achieve near-optimum performance. Finally, the proposed methods are extended to conduct soft detection of GSM systems. Simulations show that our methods significantly outperform existing ones while the detection complexity remains similar. [ABSTRACT FROM AUTHOR]

A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (MAP) error performance. In specific, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not considered by the conventional detector with a limited complexity. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a turbo equalization receiver. Furthermore, we propose a compatible training technique to improve the detection performance of the proposed DL-SPDA with turbo equalization. In particular, the neural network is optimized in terms of the mutual information between the transmitted sequence and the extrinsic information. We also investigate the maximum-likelihood bit error rate (BER) performance of a finite length coded FTN system. Simulation results show that the error performance of the proposed algorithm approaches the MAP performance, which is consistent with the analytical BER. [ABSTRACT FROM AUTHOR]