Narrowband Internet of Things (NB-IoT) is an emerging standard serving massive wireless communications devices. It is implemented based on the legacy long-term evolution (LTE) technology and thus shares many system configurations with it. In fact, NB-IoT is deployed on some of the spectrum allocated to LTE. This sometimes introduces interference to the frequency bands with LTE transmission. Signal identification has been found as a critical and effective method for spectrum awareness and the improvement of resource allocation performance thus mitigating potential interference. While not been studied in previous work, this article fills the hole in identifying NB-IoT transmissions in different communications scenarios. In particular, we develop a convolutional neural network (CNN)-based signal identification method to distinguish NB-IoT signals from other cellular signals. The performance of the signal identification method is tested with different CNN training setups. Our experiments demonstrate that the proposed model can successfully identify the existence of NB-IoT signal with 98.13% accuracy in the Rayleigh fading channel where the signal-to-noise ratio (SNR) = 10 dB. Our results also show that the proposed signal identification method is able to provide promising identification accuracy under various and unknown SNR environments in both additive white Gaussian noise (AWGN) and Rayleigh fading channels.

This paper proposes a Generalized Memory-Assisted Adaptive-Threshold (G-MAAT) detection scheme for diffusion-based Molecular Communication (MC) systems. The proposed scheme applies to MC systems in which a single-type of molecules is assumed to carry the information from the single transmitting nanomachine (Tx Bio-NM), through a fluid medium, to the single receiving nanomachine (Rx Bio-NM). Thus, the information is represented by the received concentration of the information molecules, where the communication takes place in a three-dimensional diffusion-based unbounded environment. We present the generalized model of adaptive-threshold detection algorithm with N-Memory depth suitable for binary On-OFF Keying (BOOK signaling), where the Rx Bio-NM adapts the 1/0 bit detection threshold based on past received bits. This adjustment of the 1/0 detection threshold based on past received bits helps to alleviate the inter-symbol interference resulting from residual (tail) diffusion molecules arriving at the receiver due to past bit transmissions and reception noise. Furthermore, the complexity of G-MAAT is calculated and the performance evaluation in various noisy channel sources shows a promising improvement in the un-coded Bit Error Rate (BER) performance of the proposed scheme compared with other threshold detection mechanisms, which considerably more complex approaches found in the literature.

Data mining has recently drawn a lot of interests as an effective way of generating a concept map in an adaptive learning system that provides students with the personalized learning guidance. Even with significant progresses witnessed in this field, the data mining-based concept map generation needs further improvement both in accuracy and complexity before it can be employed in actual education services. This paper proposes a classification-based approach to significantly reduce computational complexity of concept map generation while maintaining the accuracy of the generated concept map, and demonstrates through simulations that the approach accomplishes the objectives.

This paper proposes a spectral efficient multiuser scheme for single-carrier transmission. In the proposed scheme, a combination (hybrid) of localized and distributed subcarrier mapping types is employed, where the signals of users with different subcarrier mapping types are superimposed on a common set of subcarriers. We first present mathematical formulations of multiuser detection and derive the equalization coefficients for the hybrid type. In fading environments, the proposed scheme allows users to employ any subcarrier mapping types (localized, distributed, hybrid types), which can support low and high speed users. To estimate the performance of the proposed scheme, we develop a density evolution technique and show that the predicted performances are consistent with simulated performances. Further, based on the performance estimation of the proposed scheme, we discuss channel-dependent resource allocation algorithms: opportunistic scheduling and adaptive modulation and coding (AMC) schemes. These resource allocation algorithms increase the spectral efficiency of the proposed scheme with guaranteed quality-of-service (QoS). Simulation results show that the proposed multiuser scheme achieves both multiuser and frequency diversity gains in fading environments.

Adaptive bit and power loading is a constraint optimization problem with generally two cases of practical interest, where the objectives are the achievable data rate maximization (RM) and system margin maximization (MM). In this paper, we propose a different optimization model for underwater acoustic (UWA) channels, which is achieved by two algorithms: one is the bandwidth-efficient bit loading algorithm; the other is the Lloyd algorithm based limited feedback procedure. It aims at minimizing the power consumption with constraints of the constant symbol data rate and desired bit-error-rate (BER). The optimization result is derived by the Lagrange multiplier method. The Lloyd algorithm is employed to quantize the CSI at the receiver and construct the codebook, which is adopted to achieve the limited feedback process. After selecting an initial bit loading vector upon the current CSI, the receiver will broadcast its index to the transmitter, then the transmitter will compute the bandwidth-efficient bit loading algorithm and allocate the corresponding power and bits to each subcarrier. Simulation results will reveal the benefits of adopting these two algorithms from different aspects.

In orthogonal frequency division multiplexing (OFDM) underwater acoustic (UWA) communications, some subcarriers may be subject to deep fading. If the channel state information (CSI) is available at the transmitter, adaptive transmission techniques can be applied to mitigate the deep fading effect and increase the system's overall performance. Therefore, it is more valuable to analyze the UWA channel with limited CSI feedback. In this paper, we adopt ambient noise in OFDM UWA communication systems for the first time and calculate its power for each subcarrier. We explain properties of the UWA channel and investigate its optimal power loading strategy with different levels of transmitter's CSI, which is combined with bit loading for a given target bit-error rate (BER). The optimum result is derived by maximum system capacity with constraints of the total transmission power and target BER. The Lloyd algorithm is employed to quantize the CSI at the receiver and construct the codebook, which is also known to the transmitter. Simulation results compare the performance difference between a few bits of feedback, perfect feedback and non-feedback, which show that a few bits of feedback can significantly improve the system performance.

In this paper, we propose two architectural arrangements to build localized and independent intelligent medical networks. These networks constantly monitor the specific needs of the patients and resources of the locality, with the involvement of medical service providers (MSP). The MSPs would serve the medical and informational needs of their client patients. A large number of these MSPs would serve a community or a region. Each MSP (i) will retain a portfolio of each of the client patient (j) and perform a daily check on the changes of status in the local or global information or service as it pertains to the medical history of the particular client patient and makes the patient aware of any detail as it may affect the course of action. If the patient goes through the MSP, then the incremental change is up dated in the patient database. In addition, the system performs the routine medical functions as a hospital staff would perform but retains the information over the life of the individual in and out of every ailment and its treatment. The details of the databases and their distribution or their aggregation, are presented in the two architectural arrangements.

A significant improvement in noise immunity can be achieved for digital transmission over band-limited channels by the use of multidimensional signal constellations. Conventional 16-point QAM signaling, such as that used in many digital transmission systems, is a two-dimensional modulation scheme where in each signaling interval a group ofNbits is used to determine the amplitudes of the in-phase and quadrature dimension or coordinate, i.e.,N/2bits are conveyed per dimension. In a2M-dimensional QAM system, a group ofMNbits is used to determine the in-phase and quadrature ampllitudes forMconsecutive, symbol intervals whereN/2bits are still conveyed by each dimension. It is the purpose of this paper to describe the basic theory and implementation for a particular 2 bits/dimension four-dimensional (two-symbol interval) encoding which readily lends itself to simple encoding and decoding. For this encoding, theory predicts a 1.2 dB gain in noise margin over conventional 16-point (two-dimensional) QAM signaling. Experimental results agreed with the theoretical predictions, and have demonstrated an order of magnitude reduction in block error rate. Extension to eight-dimensional signaling offers a theoretical gain of 2.4 dB over conventional 16-point QAM.

The linear canceller has been shown to be effective in mitigating intersymbol interference in the presence of slope distortion. However, even moderate amounts of bias can affect the stability and performance of the canceller. We infer the eigenvalues associated with the autocorrelation matrix of the jointly adaptive infinitely long linear canceller structure with the aid of a theorem on asymptotic convergence. As in the case of the fractionally spaced equalizer, these eigenvalues are spread over a wide range; hence, the linear canceller taps can grow large when a bias exists in the updating error signal. This occurs even though the canceller is only symbol-space d, in contrast to the phenomenon in an equalizer. One solution is to apply the tap-leakage algorithm to both jointly adapting filters constituting the canceller.

Adaptive filters, employing the transversal filter structure and the least mean square (LMS) adaptation algorithm, or its variations, have found wide application in data transmission equalization, echo cancellation, prediction, spectral estimation, on-line system identification, and antenna arrays. Recently, in response to requirements of fast start-up, or fast tracking of temporal variations, fast recursive least squares (FRLS) adaptation algorithms for both transversal and lattice filter structures have been proposed. These algorithms offer faster convergence than is possible with the LMS/ transversal adaptive filters, at the price of a five-to-tenfold increase in the number of multiplications, divisions, and additions. Here we discuss architectures and implementations of the LMS/transversal, fast-converging FRLS filter, and lattice filter algorithms which minimize the required hardware speed. We show how each of these algorithms can be partitioned so as to be realizable with an architecture based on multiple parallel processors.

The problem of designing nonrecursive digital filters to perform baseband and passband pulse shaping in digital systems amounts to determining a discrete sequence with preassigned zero crossings. This approach yields a filter frequency response which permits minimal stop band noise enhancement. Several methods exist for designing transmitter and receiver filters for digital systems. An interesting technique using a minimax criterion in the design procedure employs linear programming techniques in the frequency domain, in addition to ensuring periodic nulls in the time domain. We derive a bound which gives insight into the frequency resolution or frequency sampling grid density problem, and also provides an analytical basis for determining an estimate of the filter length. Problems encountered with efficient transmitter implementation are discussed along with methods found useful in overcoming these problems.

Finite Impulse Response filters (FIR) and other digital signal processing algorithms are implemented either with special purpose hardware or as programs on special purpose computers or signal processors. In either case the state variables and the coefficients are stored in binary format in registers or memory elements with finite lengths. Register length is an economic factor in hardware implementations and in software implementations it is generally forced upon the designer by the architecture of the available processor or computer.