ICI Cancellation in OFDM Systems by Frequency Offset Reduction

With the rapid growth of digital communication in recent years, the need for high speed data transmission is increased. Moreover, future wireless systems are expected to support a wide range of services which includes video, data and voice. OFDM is a promising candidate for achieving high data rates in mobile environment because of its multicarrier modulation technique and ability to convert a frequency selective fading channel into several nearly flat fading channels. Now OFDM is being widely used in wireless communications standards, such as IEEE 802.11a, the multimedia mobile access communication (MMAC), and the HIPERLAN/2. However, one of the main disadvantages of OFDM is its sensitivity against carrier frequency offset which causes inter carrier interference (ICI). A well-known problem of orthogonal frequency division multiplexing (OFDM), however, is its sensitivity to frequency offset between the transmitted and received signals, which may be caused by Doppler shift in the channel, or by the difference between the transmitter and receiver local oscillator frequencies. This carrier frequency offset causes loss of orthogonality between sub-carriers and the signals transmitted on each carrier are not independent of each other. The orthogonality of the carriers is no longer maintained, which results in inter-carrier interference (ICI). The undesired ICI degrades the performance of the system. Depending on the Doppler spread in the channel and the block length chosen for transmission, ICI can potentially cause a severe deterioration of quality of service (QOS) in OFDM systems. ICI mitigation techniques are essential in improving the performance of an OFDM system in an environment which induces frequency offset error in the transmitted signal. The comparisons of these schemes in terms of various parameters will be useful in determining the choice of ICI mitigation techniques for different applications and mobile environments. This project investigates an efficient ICI cancellation method termed ICI self-cancellation scheme for combating the impact of ICI on OFDM systems. The ICI self-cancellation scheme is a technique in which redundant data is transmitted onto adjacent sub-carriers such that the ICI between adjacent sub-carriers cancels out at the receiver. The main idea is one data symbol is modulated onto a group of adjacent subcarriers with a group of weighting coefficients. By doing so, the ICI signals generated within a group can be self-cancelled each other. At the receiver side, by linearly combining the received signals on these subcarriers with proposed coefficients, the residual ICI contained in the received signals can then be further reduced. Although the proposed scheme causes a reduction in bandwidth efficiency, it can be compensated, by using larger signal alphabet sizes in modulation. The average carrier-to-interference power ratio (CIR) is used as the ICI level indicator, and a theoretical CIR expression is derived for the proposed scheme. The proposed scheme provides significant CIR improvement, which has been studied theoretically and supported by simulations. Simulation results show that under the condition of the same bandwidth efficiency and larger frequency offsets, the proposed OFDM system using the ICI self-cancellation scheme per- forms much better than standard OFDM systems in AWGN channel with large Doppler frequencies. In addition, since no channel equalization is needed for reducing ICI, the proposed scheme is therefore beneficial in implementation issue without increasing system complexit

Fifty Years of Noise Modeling and Mitigation in Power-Line Communications.

Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and non-parametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Signal Detection for OFDM-Based Virtual MIMO Systems under Unknown Doubly Selective Channels, Multiple Interferences and Phase Noises

In this paper, the challenging problem of signal detection under severe communication environment that plagued by unknown doubly selective channels (DSCs), multiple narrowband interferences (NBIs) and phase noises (PNs) is investigated for orthogonal frequency division multiplexing based virtual multiple-input multiple-output (OFDM-V-MIMO) systems. Based on the Variational Bayesian Inference framework, a novel iterative algorithm for joint signal detection, DSC, NBI and PN estimations is proposed. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the bit-error-rate performance of the proposed signal detection algorithm is very close to that of the ideal case which assumes perfect channel state information, no PN, and known positions and powers of NBIs plus additive white Gaussian noise. Furthermore, simulation results show that the proposed signal detection algorithm outperforms other state-of-the-art methods.published_or_final_versio

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial