A New Efficient Ordering Scheme for Sphere Detection

We present a computationally efficient sequential detection scheme using a modified Fano algorithm (MFA) for V-BLAST systems. The proposed algorithm consists of the following three steps: initialization, tree searching, and optimal selection. In the first step, the proposed detection scheme chooses several candidate symbols at the tree level of one. Based on these symbols, the MFA then finds the remaining transmitted symbols from the second tree level in the original tree structure. Finally, an optimal symbol sequence is decided among the most likely candidate sequences searched in the previous step. Computer simulation shows that the proposed scheme yields significant saving in complexity with very small performance degradation compared with that of sphere detection (SD).

We discuss recent advances towards matter-wave interference experiments with free beams of metallic and dielectric nanoparticles. They require a brilliant source, an efficient detection scheme and a coherent method to divide the de Broglie waves associated with these clusters: We describe an approach based on a magnetron sputtering source which ejects an intense cluster beam with a wide mass dispersion but a small velocity spread of Δv/v < 10%. The source is universal as it can be used with all conducting and many semiconducting or even insulating materials. Here we focus on metals and dielectrics with a low work function of the bulk and thus a low cluster ionization energy. This allows us to realize photoionization gratings as coherent matter-wave beam splitters and also to realize an efficient ionization detection scheme. These new methods are now combined in an upgraded Talbot-Lau interferometer with three 266 nm depletion gratings. We here describe the experimental boundary conditions and how to realize them in the lab. This next generation of near-field interferometers shall allow us to soon push the limits of matter-wave interference to masses up to 10⁶ amu.

This paper proposes a MIMO-OFDM scheme reducing the computational complexity and providing comparable performance to QRD-M detection scheme in IEEE 802.11n. The proposed scheme checks the channel condition per subcarrier and then selects either ordered DFE scheme or adaptive QRD-M detection scheme according to that information. The post-detection SNR which judges channel condition depends on Moore-Penrose pseudo-inverse matrix G. The proposed scheme selects the ordered DFE detection scheme in case of good channel condition and the adaptive QRD-M detection scheme in case of bad channel condition per subcarrier. The adaptive QRDM detection scheme selects the number of variable survival symbols M in accordance with the channel condition at each layer. Compared with the conventional QRD-M (M=16) detection scheme, the proposed detection scheme reduces the computational complexity about 67 percents and achieves similar performance.

Recently, the turbo-detected sphere packing (SP) aided space-time block-coding (STBC) STBC-SP scheme was demonstrated to provide useful performance improvements over conventionally-modulated orthogonal design-based STBC schemes in the context of single-user multiple-input multiple-output (SU-MIMO) systems. Hence, in this letter, we extend its employment to the multiuser MIMO (MU-MIMO) scenario. For the sake of achieving a near maximum a posteriori (MAP) performance while imposing a moderate complexity, we specifically design the K -best sphere detection (SD) scheme for supporting the operation of the SP-modulated system, since the conventional SD cannot be directly applied to such a system. Consequently, with the aid of our modified SD, a significant performance gain can be achieved by the SP-modulated system over its conventionally-modulated counterpart in the context of MU-MIMO systems.

For ADSL applications, very large constellation sizes are used, which can be as large as 32768QAM. It is a big challenge to design turbo TCM for such a large constellation size while still maintaining low decoding complexity. We propose a new turbo coded QAM scheme with a unique concatenated Gray for an ADSL system. This new mapping can not only provide good performance for turbo coded bits but can also efficiently maximize the minimum Euclidean distance of uncoded bits. This new turbo coded QAM scheme not only has as good error correcting performance as the conventional bit-interleaved turbo coded QAM and symbol-interleaved turbo TCM schemes, but also has very low decoding complexity which is only about 17% of the conventional bit-interleaved turbo coded QAM.

A linear detection scheme such as minimum mean squared error (MMSE) algorithm is considered to be an effective means for a large scale antenna system with hundreds of antennas. Even with a linear detection scheme, we suffer from the computational complexity problem especially in computing matrix inversion processes with dimensions greater than a hundred. If this MMSE detection scheme is combined with soft iterative detection, the complexity is further increased. In this paper, we propose an efficient complexity reduction method for MMSE based soft iterative detection scheme with parallel interference cancelation, by utilizing the characteristics of the filtering matrix. The simulation results demonstrate that the detection algorithm with the proposed approach achieves approximating performance to the conventional full complexity MMSE method.

Branch Retinal Artery Occlusion (BRAO) has become a serious disorder causing permanent loss of vision. It is mainly caused due to the rupture of blood vessels which is too small in size and thereby increasing the risk for efficient detection. Therefore an efficient detection scheme is necessary to evaluate appropriately and pave the way for systemic therapy to preserve or recover vision in the affected eye. In this paper, a fully automatic detection scheme has been developed for accurate segmentation followed by classification of BRAO volumes using the fundus images. Coming to the point this is the first automatic classification framework using an optimized neural network for classifying BRAO. It's mainly a three-step process where the first step concentrates on removing noise and thereby mproving the quality of fundus image. According to which an boosted anisotropic diffusionbased enhancement filter is used here, which effectively removes the noise. This is justified using image quality metrics. Second step here involves adaptive cluster with super pixel segmentation which clearly segments the affected area with high contrast. For the segmented image IR Analyzer is used for extracting region properties. In the final step gray wolf optimization-based convolution neural network (GWO-CNN) classifier is used. Here GWO-CNN classifier is used to differentiate the normal and abnormal images. The effectiveness of the above method is evaluated and obtained an accuracy of 98.57% when it is implemented in MATLAB 2018 software.

ABSTRACTPhysical‐layer security techniques have contributed to the achievement of various security objectives in an efficient and lightweight manner. Thus, these techniques have been widely considered for limited‐resource networks such as Internet of Things networks. Among the different security objectives, malicious user detection by exploiting physical‐layer parameters has demonstrated efficient performance. In this work, malicious user detection in the recently proposed index modulation‐based orthogonal frequency division multiple access (IM‐OFDMA) is addressed. The proposed malicious user detection scheme exploits the hardware impairments, especially the in‐phase and quadrature imbalance parameters, for both legitimate and malicious users to design a dual‐phase efficient detection scheme. The proposed scheme accounts for the special characteristics of IM‐OFDMA transmission that are different from other multiple‐access techniques. The performance of the proposed scheme was evaluated considering detection probability and false alarm probability performance metrics. Moreover, closed‐form expressions of these metrics were derived for both phases and were validated by Monte Carlo simulation results under different configurations of IM‐OFDMA systems.

An efficient frequency detection scheme for GMSK signal reception is described. In this detection scheme, a three-level eye opening of the frequency detector output at the decision instant offset by a half-bit is used to improve bit error rate (BER) performance. Experimental results for 16 kbit/s differentially encoded GMSK transmission with a premodulation filter bandwidth-bit duration product (Bb T) = 0–25 (typical for mobile radio applications) show that the half-bit offset decision frequency detection scheme can significantly improve BER performance, in comparison with conventional detection schemes.

The false data injection attacks (FDIA) pose significant threats to the security of the power distribution systems, underscoring the critical need for efficient FDIA detection and localization schemes. In this article, a joint FDIA detection and localization scheme based on an adversarial anomaly transformer is proposed, and the experimental analysis is performed using a developed cyber-physical power distribution simulation system testbed. To address the prevalent overgeneralization issue in reconstruction-based unsupervised methods, an anomaly attention mechanism is first introduced into the FDIA detection and localization scheme to amplify the distinguishability between normal and attacked data. Subsequently, a generative adversarial network framework is used to capture the distribution of data accurately. In addition, the drift streaming peaks over threshold dynamic threshold method is used to adapt to fluctuant measurements and enhance online detection applications. Finally, simulations and experiments are conducted on the developed cyber-physical testbed to demonstrate the superiority of proposed scheme.

Block Data Transmission Systems (BDTS) are used in high speed wireless communication systems with time dispersive channel characteristics. Since Maximum Likelihood Block Detection (MLBD) requires huge amount of computation, Sphere Detection (SD) technique has been introduced as an alternative. This paper proposes an An Lattice Sphere Detection (AnLSD) technique for detection in block data transmission systems (BDTS). The An lattice structure offers more dense constellation points than the customary Zn lattice in SD technique. Thus, the proposed AnLSD uses An lattice generator matrix as the channel matrix, and uses the Gram matrix which is obtained from the generator matrix as part of the detection process. Simulation results show that the proposed AnLSD performs very close to the Exhaustive search (ES) using block size of 20 bits. It is also performing better than the other renowned methods tested under a channel with spectral nulls. The proposed AnLSD technique offers significant reduction in term of objective functions evaluation as compared to the other renowned methods.

Filtered multitone (FMT) modulation divides the communication band into several subbands to shorten the span of symbols affected by multipath in underwater acoustic (UWA) communications. However, there is still intersymbol interference (ISI) in each subband of FMT modulation degrading communication performance. Therefore, ISI suppression techniques must be applied to FMT modulation UWA communications. The suppression performance of traditional adaptive equalization methods often exploited in FMT modulation UWA communications is limited when the effect of ISI spans tens of symbols or large constellation sizes are used. Turbo equalization consisting of adaptive equalization and channel decoding can improve equalization performance through information exchanging and iterative processes. To overcome the shortcoming of traditional minimum mean square error (MMSE) equalization and effectively suppress the ISI with relatively low computation complexity, an FMT modulation UWA communication using low-complexity channel-estimation-based (CE-based) MMSE turbo equalization is proposed in this paper. In the proposed method, turbo equalization is first exploited to suppress the ISI in FMT modulation UWA communications, and the equalizer coefficients of turbo equalization are adjusted using the low-complexity CE-based MMSE algorithm. The proposed method is analyzed in theory and verified by simulation analysis and real data collected in the experiment carried out in a pool with multipath propagation. The results demonstrate that the proposed method can achieve better communication performance with a higher bit rate than the FMT modulation UWA communication using traditional MMSE adaptive equalization.

This correspondence introduces a new approach to design space-time convolutional codes (STCCs) with large constellation size in systems with any number of transmit antennas. Our design procedure is based on utilizing quadrature phase-shift keying (QPSK) STCCs as component codes, and, consequently, unlike existing techniques, the search space does not grow exponentially with the constellation size. Our approach is further based on the fact that an ntimesm multiple-input multiple-output (MIMO) system is equivalent to n distinct 1timesm systems. By employing a common design for each individual 1timesm system, we arrive at an approach whose complexity does not grow with the number of transmit antennas. To describe our approach, we first demonstrate that a system employing an STCC can be implemented with only a single transmit antenna when there are multiple receive antennas. The idea is to transmit more than one symbol from a single transmit antenna during a symbol period by superimposing the encoded symbols on top of each other. This objective is achieved by inducing randomness into the system, that creates additional channel paths, called virtual paths. The design of the distributions of the induced random variables is studied for slow Rayleigh and Rician fading channels by utilizing an upper bound on the pairwise block error probability. Simulation results evaluate the performance of this technique for the case of two transmit antennas and several different number of receive antennas, a spectral efficiency of 4 b/s/Hz for slow Rayleigh and Rician fading channels

An energy-efficient event detection and dissemination (EEDD) scheme for event-driven wireless sensor networks applications is introduced. Based on clustering-based architecture, EEDD includes sensor and aggregation management, sleep and wakeup scheduling, and a customized multi-channel MAC/routing protocol with power control. Bit map based MAC approach is adapted to use not only in intra-cluster communication, but also in inter-cluster communication. Lifetime is prolonged by integrated design of role-based sleep and medium access, while urgent events can be transmitted immediately.

Molecular communication (MC) is the new nano-bio-communication paradigm that inspired by the nature to perform communication tasks at nanoscale networks. Although power utilization and large computation abilities are important factors that play a role in designing detection schemes for traditional communication systems, the MC is more sensitive to these factors due to its operating scale and power resources. Transmitters and receivers can be bio-cellular, bio-organisms, or nano-machines that can perform simple tasks due to their natural limitation with respect to size, power supply, and required simplicity. Accordingly, there is a need for efficient low-complexity detection schemes that can enable reliable communications despite impairments such as inter-symbol interference (ISI), noise and distortion. In this chapter, we propose a novel detection scheme for molecular communication via diffusion (MCvD) that achieves low computational complexity, yet deliver reliable information detection. Our proposed scheme encompasses a multi-layer structure where each layer performs a certain level of pin-pointing (or zoom-in) of the estimated received bits in a fashion that can be regarded as a form of sub-optimal minimum mean-square estimation (MMSE) that tradeoffs complexity for performance. Our detection method has been able to achieve un-coded bit error rate levels down to \(10^{-5}\) at signal-to-noise ratios where traditional threshold-based detection achieves worse than \(10^{-2}\).