Orthogonal Frequency Division Multiplexing (OFDM) is the important modulation of choice for fourthgeneration broadband multimedia wireless systems. This paper is focused on the problem of reducing the intercarrierinterference (ICI) and signal to noise ratio in the transmission over OFDM using various pulse shaping methods. Here we have performed a detailed performance comparison of various pulse shaping functions used in OFDM System with Carrier Frequency Offset. They appear to be suitable for transmission in OFDM systems with carrier frequency offset. The results obtained by analysis show that the performance improvement over conventional pulse shapes, are significant for reducing average intercarrier-interference (ICI) power and increased ratio of average signal power to average ICI power (SIR).
Electronic subsystems with present day's technology are quite complex and consists of many Printed Circuit Boards . Incidentally PCB's are the ones, which causes most of the Electromagnetic Interference @MI) problems.However each sub systems must maintain Electro Magnetic compatibility (EMC) with other sub systems in order to perform as per the specifications in its intended electromagnetic environment and to meet international regulations. Identifying the potential problems at an earlier stage of the design cycle minimizes the need for design changes by saving time and money. In this paper a Knowledge Based System called EMC Expert. for predicting EMI problems and offering solutions at the design stage of the Printed Circuit Board, has been presented. EMC Expert has been implemented on a HP9000/735 workstation using CCC under X-Windows/MOTIF and is being used to analyze Printed Circuit Boards designed by DRDUCAD centre,
Automatic modulation classification (AMC) is becoming a promising technique for future adaptive wireless transceiver systems. The existing blind modulation classification (BMC) methods for orthogonal frequency division multiplexing (OFDM) fail to achieve the required performance by using statistical-based methods. Thus, the modulation classification research community is trying to adopt the deep learning (DL) method to improve the modulation classification accuracy. However, most of the existing DL methods for AMC of OFDM that involve the extraction of statistical features from the signal do not work for adaptive transceiver systems where the signal parameters are changed dynamically. In this paper, we design and implement AMC for adaptive OFDM systems by using a convolutional neural network (CNN) with residual learning. The proposed AMC can identify the modulation format of the received OFDM signal with different number of subcarriers, randomized carrier frequency offset (CFO), symbol timing offset (STO), phase offset, and unknown channel state information. We use residual learning to mitigate the effect of varying CFO, STO, and AWGN noise on the received OFDM signal. A larger pool of modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK, π/4-QPSK, minimum shift keying, 8-PSK, 16-quadrature amplitude modulation (QAM), and 64-QAM are being considered for the proposed AMC for OFDM system in a dynamic environment. The performance and complexity of the proposed AMC are compared with the existing statistical feature-based and DL-based approaches. The proposed AMC for the OFDM system is also verified on the real-time data set generated from the universal software radio peripheral testbed setup.INDEX TERMS Automatic modulation classification, CNN, deep learning, OFDM, residual learning. I. INTRODUCTIONFuture generation of communication systems are expected to operate in a variety of environments serving varying requirements in terms of data rate, coverage, number of connected devices, etc. This requires the next-generation communication systems to have added intelligence to interact with theirThe associate editor coordinating the review of this manuscript and approving it for publication was Yafei Hou . environment and adapt their parameters for delivering optimal performance even in the physical layer [1], [2]. However, synchronization of these parameters across the transceiver is a highly challenging task for an adaptive or automated communication system which will be a key player for beyond fifth-generation (5G) wireless communication [3]. Explicit knowledge of these parameters between a transmitter and a receiver often results in inefficient use of available resources. For example, a transmitter that adapts its modulation scheme
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