Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) with an electronically steerable passive array radiator (ESPAR) antenna receiver can improve the bit error rate performance and obtains additional diversity gain without increasing the number of Radio Frequency (RF) front-end circuits. However, due to the large size of the channel matrix, the computational cost required for the detection process using Vertical-Bell Laboratories Layered Space-Time (V-BLAST) detection is too high to be implemented. Using the minimum mean square error sparse-sorted QR decomposition (MMSE sparse-SQRD) algorithm for the detection process the average computational cost can be considerably reduced but is still higher compared with a conventional MIMOOFDM system without ESPAR antenna receiver. In this paper, we propose to use a low complexity submatrix divided MMSE sparse-SQRD algorithm for the detection process of MIMOOFDM with ESPAR antenna receiver. The computational cost analysis and simulation results show that on average the proposed scheme can further reduce the computational cost and achieve a complexity comparable to the conventional MIMO-OFDM detection schemes.

Multiphoton microscopy (MPM) enables noninvasive optical imaging into the deep tissue in animal models in vivo . Recently, 1700 nm has been demonstrated as a promising excitation window for deeper penetration. Signal depletion is currently the limiting factor for imaging depth at the 1700-nm window in MPM. As a result, efficient signal detection is an effective means to further boost imaging depth. GaAsP and GaAs photomultiplier tubes (PMTs) are commonly used for signal detection in MPM. Here, we demonstrate a comparison investigation of signal detection of a GaAsP and a GaAs PMT for several modalities of MPM at 1700 nm. The two PMTs are identical in structure, connected to the same electronics, and mounted at exactly the same place in the microscope, facilitating a fair comparison. Our results show that with a 1667-nm excitation, GaAsP PMT is more efficient for signal detection of a 3-photon fluorescence of quantum dot Qtracker 655, third-harmonic generation signal, and a 4-photon fluorescence of fluorescein, whereas GaAs PMT is far superior in detecting a second-harmonic generation signal. The measured results are in good agreement with theoretical calculations based on wavelength-dependent cathode radiant sensitivities. We expect that our results will offer guidelines for PMT selection for MPM at the 1700-nm window.

<abstract><p>Advanced wireless communication technologies, such as 5G, are faced with significant challenges in accurately estimating the transmitted signal and characterizing the channel. One of the major obstacles is the interference caused by the delay spread, which results from receiving multiple signal copies through different paths. To mitigate this issue, the orthogonal frequency division modulation (OFDM) technique is often employed. Efficient signal detection and optimal channel estimation are crucial for enhancing the performance of multi-carrier wireless communication systems. To this end, this paper proposes a Long Short Term Memory-Projected Layer (LSTM-PL) deep neural network(DNN) based channel estimator to detect received OFDM signal. The results show that the LSTM-PL algorithm outperforms traditional methods such as Least Squares(LS), Minimum Mean Square Error (MMSE) and other LSTM deep learning channel estimation methods like Long Short Term Memory(LSTM)-DNN and Bidirectional-LSTM(Bi-LSTM)-DNN, as evidenced by Symbol-Error Rate (SER) outcomes.</p></abstract>

In this paper, we propose a joint signal detection algorithm under the framework of the expectation propagation (EP) algorithm for uplink massive multiuser multiple-input multiple-output (MIMO) systems with generalized spatial modulation (GSM). By projecting the discrete probability distribution into a multivariate complex Gaussian function, the symbol beliefs of the tridimensional GSM constellation are calculated via the iterative propagation of the mean vectors and covariance matrices. To reduce the computational complexity, an efficient separate signal detection called two-stage EP (TS-EP) algorithm is designed. In the first stage, the active transmit antenna indices are determined via the EP. Each vector-valued variable node (VN) in the factor graph is decomposed into multiple sub-VNs, and the invalid sub-VNs of the determined silent transmit antennas are pruned from the factor graph. In the second stage, since the modulation symbols are independent conditioned on the active transmit antennas, the symbols are independently detected by adopting the EP with univariate complex Gaussian approximations, and the number of probability calculations for each symbol belief is significantly reduced in the subsequent EP update. Simulation results illustrate that the proposed EP and TS-EP signal detection schemes outperform the recently proposed counterparts. Moreover, the proposed TS-EP algorithm strikes a desirable and flexible performance-complexity tradeoff.

This paper investigates the use of cooperative multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) technique to limit energy consumption used to set up communications among distant hubs in a remote wireless sensor network (WSN). As energy exhaustion is a pertinent issue in WSN field, various methods aim to safeguard such asset, particularly by means for harvesting energy amidst communication among sensor hubs. One such widely utilized strategy is multi-hop communication to reduce the energy required by a single hub to transmit a given message, giving a homogeneous utilization of the energy assets among the hubs in the system. The case of multi-hop communication is not continuously more effective than single-hop. In multi-hop communication, energy efficiency will depend upon the distance between transmit and receive clusters. If the distance is large, then the energy expended will be less than single-hop communication. In this paper, an agreeable MIMO-OFDM transmission strategy for WSN is exhibited, which is contrasted with single-hop. The cooperation among adjacent nodes is analyzed, highlighting its points of interest in connection with both. The inference drawn from performance improvement manifests itself in the utility of applying the proposed strategy for energy-sparing purposes.

This work investigates communication signal detection and direction of arrival (DOA) estimation for an integrated sensing and communications (ISAC) system with multiple hardware imperfections, including power amplifier nonlinearity, in-phase and quadrature phase imbalance, and phase-gain error (PGE). Conventional signal processing techniques struggle with the complex nonlinearities arising from these imperfections. Recently, deep neural networks (DNNs) have been employed to mitigate hardware impairments; however, they require a substantial number of pilot signals for training, leading to significant overhead, making them impractical in many applications. In this work, we design a signal flow inspired neural network (NN) to characterize the nonlinear ISAC system. Then, we propose a Bayesian method to jointly estimate the parameters of the PGE, the communication signals, and the DOAs by developing a message-passing inference algorithm based on the NN. Extensive simulation results demonstrate that the proposed method provides efficient and robust signal detection and DOA estimation performance under PGE, and significantly outperforms state-of-the-art ones.

The faster-than-Nyquist (FTN) signaling is a bandwidth-efficient technology which has drawn attention in the bandwidth-starved world. Since the symbols appear too early, inter-symbol interference (ISI) cannot be avoided in the receiver. Decision feedback equalization (DFE) cancellation is an efficient interference cancellation and signal detection schemes for FTN based communication system. However, since the noise isn't considered, the DFE scheme performs badly at low E b /N 0 . In order to further improve the detection performance, firstly, the detector based on minimum mean square error (MMS E) is investigated to at low E b /No, and then the precoding scheme is introduced in FTN signaling, including: singular value decomposition (SVD), geometric mean decomposition (GMD). Simulation results show that the GMD precoding can perform better than DFE.

This paper proposes a Group Detection (GD) algorithm with Max-Log-MAP Sphere Decoder (MLM-SD) in order to reduce the complexity of signal detection in a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. The proposed algorithm divides spatial streams into multiple partial spatial streams by using Minimum Mean Square Error (MMSE) detector, followed by multiple MLM-SDs with reduced number of spatial streams. Although the spatial diversity gain in the MLM-SD degrades because of the lack of the degrees of freedom exploited by the MMSE detector, its diversity gain is recovered by combining the metrics obtained by the multiple MLM-SDs. In a MIMO wireless LAN multipath fading environment, the complexity of the proposed algorithm is 10% of that of the original MLM-SD and the performance degradation in terms of SNR is slightly less than that of the original MLM-SD in 4-by-4 MIMO architecture with 64QAM achieving 216 Mbps. It is also found that the proposed algorithm is robust against the limitation of the number of searches in sphere decoder.

In this paper, we propose a novel frequency domain turbo equaliser for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission in channels where the maximum excess delay exceeds the length of the guard interval. The turbo equaliser is based on minimum mean square error filtering and utilizes the soft feedback from channel decoding for the joint separation of the spatially multiplexed streams and cancellation of intersymbol interference (ISI) and intercarrier interference (ICI). To reduce complexity, only significant ISI/ICI terms are taken into account for signal detection, while the remaining terms are regarded as noise. Simulation results reveal that the proposed receiver shows a good compromise between complexity and performance

In wireless communication systems, channel state information is often assumed to be available at the receiver. Traditionally, a training sequence is used to obtain the estimate of the channel. Alternatively, the channel can be identified using known properties of the transmitted signal. However, the computational effort required to find the joint ML solution to the symbol detection and channel estimation problem increases exponentially with the dimension of the problem. To significantly reduce this computational effort, we formulate the joint ML estimation and detection as an integer least-squares problem, and show that for a wide range of signal-to-noise ratios (SNR) and problem dimensions it can be solved via sphere decoding with expected complexity comparable to the complexity of heuristic techniques

In orthogonal frequency division multiplexing (OFDM) systems, since virtual subcarriers are not used for transmission, approach of conventional uniformly placed pilot tones is not applicable in some situations. In this letter, based on nonuniform pilot tone placement, we derive optimal pilot sequences, which can achieve the minimum mean square error of the least squares estimate for multiple-input multiple-output (MIMO) OFDM systems. Simulation results demonstrate the effectiveness of the proposed approach.

A performance comparison between convolutional coded vertical Bell-lab layered space time (V-BLAST) and groupwise space time trellis coded (GSTTC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is presented in this paper. Both systems have the same data rate and employ receivers with comparable complexity, which are zero-forcing, minimum mean square error (MMSE) and list sphere decoder (LSD) based turbo receivers. Simulation results show that V-BLAST significantly outperform the GSTTC systems when MMSE or LSD based turbo receiver is applied, while GSTTC perform similarly to or better than the V-BLAST systems when zero-forcing detection algorithm is applied.

In this paper, we propose a receiver architecture that can demodulate multiple data streams with different frequency offsets and propagation delays in multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) ad hoc networks. The receiver performs joint synchronization and interference cancellation to separate these interfering data streams and achieve synchronization for each stream. We propose a novel training-based minimizing mean square error (MMSE) algorithm to jointly obtain the frequency offsets and the equalization delays. The effects of frequency offsets and propagation delays are compensated with these estimated parameters. We also propose a direct training-based equalizer design algorithm to obtain the equalizer coefficients. This algorithm involves solving an optimization problem that considers symbol error rate. Simulation shows good uncoded bit error rate performance for our proposed algorithms.

Cognitive radio technology enables spectrum sensing (SS), which allows the secondary user (SU) to access vacant frequency bands in the periods when the primary user (PU) is not active. Due to its minute implementation complexity, the SS approach based on energy detection (ED) of the PU signal has been analyzed in this paper. Analyses were performed for detecting PU signals by the SU in communication systems exploiting multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission technology. To perform the analyses, a new algorithm for simulating the ED process based on a square-law combining (SLC) technique was developed. The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems. The influence of a false alarm on the detection probability of PU signals impacted by operating parameters such as the signal-to-noise ratios, the number of samples, the PU transmit powers, the modulation types and the number of the PU transmit and SU receive branches of the MIMO-OFDM systems have been analyzed in the paper. Simulation analyses are performed by running the proposed algorithm, which enables precise selection of and variation in the operating parameters, the level of noise uncertainty and the detection threshold in different simulation scenarios. The presented analysis of the obtained simulation results indicates how the considered operating parameters impact the ED efficiency of symmetric and asymmetric MIMO-OFDM systems.

Genetic tools form the basis for the study of molecular mechanisms. Despite many recent advances in the field of genetic engineering in bacteria, genetic toolsets remain scarce for non-model organisms, such as the obligatory human pathogenStreptococcus pyogenes.In this study, we set out to develop a comprehensive set of plasmids, promoters and reporters forS. pyogenes. We present an expansion to the current genetic toolbox that comprises new replicative and site-specific integrative plasmids. Moreover, we established a collection of constitutive promoters with a wide variety of strengths as well as a set of novel inducible regulatory elements, including a zinc-inducible promoter, an erythromycin-inducible riboswitch and an IPTG-inducible promoter that outperform previously described inducible systems in terms of tightness and inducibility. In addition, we demonstrated the applicability of two codon-optimized fluorescent proteins, mNeongreen and mKate2, as reporters inS. pyogenes. For this, we adapted a novel chemically defined medium called RPMI4Spy. This medium showed a highly reduced autofluorescence compared to other growth media and allowed efficient signal detection in plate reader assays and fluorescence microscopy. Finally, we developed a plasmid-based system for genome engineering inS. pyogenesfeaturing the counterselection markerpheS*, which improved the generation of scarless gene deletions.This new toolbox simplifies previously laborious genetic manipulation procedures and lays the foundation for new methodologies to study gene functions inS. pyogenes,leading to a better understanding of its virulence mechanisms and physiology.