At cellular wireless communication systems, channel estimation (CE) is one of the key techniques that are used in Orthogonal Frequency Division Multiplexing modulation (OFDM). The most common methods are Decision‐Directed Channel Estimation, Pilot-Assisted Channel Estimation (PACE) and blind channel estimation. Among them, PACE is commonly used and has a steadier performance. Applying deep learning (DL) methods in CE is getting increasing interest of researchers during the past 3 years. The main objective of this paper is to assess the efficiency of DL-based CE compared to the conventional PACE techniques, such as least-square (LS) and minimum mean-square error (MMSE) estimators. A simulation environment to evaluate OFDM performance at different channel models has been used. A DL process that estimates the channel from training data is also employed to get the estimated impulse response of the channel. Two channel models have been used in the comparison: Tapped Delay Line and Clustered Delay Line channel models. The performance is evaluated under different parameters including number of pilots (64 pilots or 8 pilots), number of subcarriers (64), the length of cyclic prefix (16 or 0 samples) and carrier frequency (4 GHz) through computer simulation using MATLAB. From the simulation results, the trained DL estimator provides better results in estimating the channel and detecting the transmitted symbols compared to LS and MMSE estimators although, the complexity of the proposed LSTM estimator exceeds the equivalent LS estimator. Furthermore, the DL estimator also demonstrates its effectiveness with various pilot densities and with different cyclic prefix periods.
The internet of things (IoT) has provided a promising opportunity to build powerful systems and applications. Security is the main concern in IoT applications due to the privacy of exchanged data using limited resources of IoT devices (sensors/actuators). In this paper, we present a classification of IoT modes of operation based on the distribution of IoT devices, connectivity to the internet, and the typical field of application. It has been found that the majority of IoT services can be classified into one of four IoT modes: gateway, device to device, collaborative, and centralized. The management of either public or symmetric keys is essential for providing security. In the present paper, we survey different key management protocols concerning IoT, which we further allocate in a map table. The map table is a link between modes of operation and the associated security key management elements. The main target of this mapping table is to help designers select the optimum security technique that provides the best balance between the required security level and IoT system mode constraints.
In this paper, design, simulation and implementation of Asymmetric Digital Subscriber Line (ADSL) modem is presented which can be applied to different telephone networks. Lhe ADSL modem is modeled and simulated under the MATLAB version 7.3 (R2006b) environments by which the simulation is achieved for both the downstream and upstream directions of the modem. The ADSL modem with a transmission throughput between 640 kbps and 6 Mbps operating over most of existing telephone subscriber loops has been implemented on TI TMS320C6713 DSP for consumer multimedia applications. Except the Analog Front End, all the basic building blocks of the ADSL modem functionalities are implemented with DSP platform. We use Real-Time Data Exchange (RTDX) as a way to debug and test our DSP designs.
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