Iterative clipping and filtering (ICF) is a straight-forward method for reducing the peak-to-average power ratio (PAPR) of signals in orthogonal frequency-division multiplexing (OFDM) system. Recently, convex optimization has been used to find the optimal filter coefficients that minimize the error vector magnitude (EVM) and meet the PAPR constraint. However, high-computation complexity may be incurred when solving the convex optimization problem. Therefore, we develop an efficient PAPR reduction method that uses the time-domain kernel matrix to generate the PAPR-reduction signal. Besides, we relax the assumption that the clipping noise is a series of uncorrelated parabolic pulses and apply the proposed method to more general cases. Based on the instantaneous observation of clipping noise, the proposed method constructs a simple time-domain kernel matrix and employs the curve fitting approach to optimize the corresponding scaling factors. The simulation results show that the proposed method can achieve very close performance to that using convex optimization in terms of both the PAPR reduction and EVM while the computational cost is reduced greatly. In addition, due to the decrease of iteration numbers and computational complexity, the proposed method is more efficient than some existing clipping and filtering methods.

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An Enhanced Iterative Clipping and Filtering Method Using Time-Domain Kernel Matrix for PAPR Reduction in OFDM Systems

One of the challenges in rollout of 5G is utilizing the radio communication which is not accessible by today’s radio system. To achieve a high data rate, it is necessary to make 5G networks compatible with advanced waveform. In this correspondence, we discussed advanced waveform technique non-orthogonal multiple access (NOMA) and filter bank multicarrier (FBMC) for 5G network. The design of advanced form technique compatible with advanced wireless communication is very important to fulfill the vision the 5G. Peak average to power ratio (PAPR) is viewed as a significant issue in actualizing NOMA and FBMC framework. PAPR reduction design for FBMC has been presented in the writing survey, yet PAPR decrease methods in NOMA are not investigated up until this point. In that regard are different downsides of minimization techniques introduced in the review. In the present investigation, we have inspected and analyzed the presence of the reduction systems and proposed a hybrid strategy (DSLM-CT) based on discrete selective mapping (DSLM) and circular transformation technique. Further, several parameters are discussed and analyzed. At long last, it is reasoned that the exhibition of the proposed hybrid technique is better than the existed minimization methods. Additionally, it is also observed that the implementation of proposed technique reduces the power consumption of solid state power amplifier.

A comprehensive study of PAPR reduction techniques: design of DSLM-CT joint reduction technique for advanced waveform

The orthogonal frequency division multiplexing (OFDM) and the non-orthogonal multiple access (NOMA) scheme are presented as promising techniques to meet the requirement of fifth-generation (5G) communication systems. Although much attention has recently been devoted to study these techniques, some scenarios have still been less explored. Considering that a fundamental part of any communication system is the use of power amplifiers, this paper presents an analytical evaluation of the bit error rate (BER) of NOMA-OFDM systems in the presence of a high power amplifier (HPA) with memory. Considering that the non-linear distortions generated by the HPA can be modeled using a polynomial model with memory, new theoretical expressions are developed to obtain the BER of the system. Specifically, exact BER expressions for a downlink NOMA-OFDM system with two users are presented and verified by Monte Carlo simulation results. The obtained numerical results demonstrate that the performance degradation of both users is highly dependent on the non-linear distortions, even when the successive interference cancellation (SIC) technique is performed perfectly.

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Bit Error Rate Analysis of NOMA-OFDM in 5G Systems With Non-Linear HPA With Memory

In this paper, a novel efficient continuous piecewise nonlinear companding scheme is proposed for reducing the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. In the proposed companding transform, signal samples with large amplitudes is clipped for peak power reduction, and the signal samples with medium amplitudes is nonlinear transformed with power compensation. While the signal samples with small amplitudes remain unchanged. The whole companding function is continuous and smooth in the range of positive numbers, which is beneficial for guaranteeing the bit error rate (BER) and power spectral density (PSD) performance. This scheme can achieve a significant reduction in PAPR. And at the same time, it cause little increment in BER and PSD performance. Simulation results indicate the superiority of the proposed scheme over existing companding schemes.

Novel PAPR reduction scheme based on continuous nonlinear piecewise companding transform for OFDM systems

Heterogeneous cloud radio access network (H-CRAN) has been deemed as a promising architecture for the fifth generation wireless networks. In H-CRAN, small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to boost the spectral efficiency. H-CRANs with dense RRHs suffer from severe inter-cell interference due to the random deployment of dense SRRHs, which limits its performance. In this paper, we propose an Edge-Aware RRH-Cooperation (EARC) scheme to mitigate the impact of both the cross-tier and co-tier interference. We partition the users into two groups, Cell Edge Device (CED) and Non-cell Edge Device (NED). These two groups, CED and NED, are mapped into two association modes, dual-association and single-association mode, respectively. In single-association mode, the NED associates with the RRH that provides the maximum received signal. On the other hand, in the dual-association mode, the CED associates with the two strongest RRHs where the two RRHs may belong to the same or different tier(s). Using tools from stochastic geometry, we quantify the performance gains of the proposed EARC scheme in terms of outage probability and ergodic rate. To further show the efficacy of the proposed scheme, we compare it to four schemes developed in the literature.

Edge-Aware Remote Radio Heads Cooperation for Interference Mitigation in Heterogeneous C-RAN

Orthogonal frequency division multiplexing (OFDM) is an efficient multi-carrier modulation technique that underlies most of the current and probably future high-speed wireless communication systems. However, the OFDM waveform is characterized by a high peak-to-average power ratio (PAPR), especially when a large number of subcarriers are used. A high PAPR is a major waveform defect since it leads to non-linear distortion when passing through the transmitter’s power amplifier. Most of the PAPR reduction techniques found in the literature reduce the PAPR mainly at the cost of either excessive computational complexity or degrading the transmission bit error rate (BER). We propose a low-complexity technique for PAPR reduction based on linear scaling of a portion of signal coefficients by an optimal factor. This paper is backed up by the extensive analysis of various performance metrics, which leads to optimal choices of key parameters and hence maximum achievable gains. The analytic and simulated results show that the proposed technique is capable of reducing the PAPR effectively with negligible effect on BER in return for a slight reduction in data rate. For example, for 1024 subcarriers, the PAPR can be reduced from 13 dB to below 7.4 or 6.9 dB, in return for only 1% or 2% reduction in data rate, respectively. In addition, the achievable PAPR varies very slightly in response to increasing the number of subcarriers. This offers a highly competitive and flexible tradeoff compared with those provided by current techniques found in the literature. Therefore, this technique has a very good potential for practical application in current and future OFDM-based systems, especially those which employ a very large number of subcarriers, such as LTE, DVB-T2, and 5G systems.

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An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

In this paper, we consider jointly optimizing user association, resource allocation and power allocation in a two tier heterogeneous cloud radio access network (H-CRAN). Our objective is to utilize all the network resources in the most efficient way to maximize the network average throughput, while keeping some constraints such as the quality of service (QoS), interference protection to the devices associated with the Macro remote radio head (RRH), and fronthaul capacity. In our system, we propose using coordinated multi-point (CoMP) transmissions to utilize any excess resources to maximize the network performance. In contrast to the literature, in which CoMP is usually used only to support edge users. We divide our joint problem into three sub-problems: user association, radio resource allocation, and power allocation. We propose matching game based low complexity algorithms to tackle the first two subproblems. For the power allocation sub-problem, we propose a novel technique to convexity the non-convex original problem to obtain the optimal solution. Given the conducted simulations, our proposed algorithms proved to enhance the network average weighted sum rate significantly, compared to the state of the art algorithms in the literature.

Joint User Association and Resource Allocation in CoMP-Enabled Heterogeneous CRAN

The exponential growth of cellular data traffic over the years imposes a hard challenge on the next cellular generations. The cloud radio access network is an emerging cellular architecture that is expected to face that challenge effectively. The main difference between the CRAN architecture and the conventional cellular architecture is that the baseband units (BBUs) are aggregated at a centralized BBU pool, hence, enabling statistical multiplexing gains. However, to acquire the several advantages induced from the CRAN architecture, efficient optimization algorithms should be used to enhance the network performance. In this article, we give an overview of cloud radio access networks, highlighting the advantages of its architecture over conventional cellular networks. Then we illustrate the performance metrics that should be considered for optimization in CRAN, and the important parameters to optimize. We review some of the recent research works in CRAN performance optimization. We highlight the current challenges faced in CRAN performance optimization and discuss open issues.

Advances in CRAN Performance Optimization

The cloud radio access network (CRAN) is a promising architecture that can face the hard challenges imposed on next cellular generations. However, the high computational complexity of the optimization techniques currently proposed in the literature prevents from totally reaping the benefits of the CRAN architecture. Learning based techniques are expected to replace the conventional optimization techniques due to their high performance and very low online computational complexity. In this paper, we consider tackling the power allocation in CRAN via an unsupervised deep learning based approach. Different from the previous works, user association is considered in our optimization problem to reflect a real cellular scenario. Additionally, we propose a novel scheme that can enhance the deep learning based power allocation approaches, significantly. We provide intensive analysis to discuss the trade-offs faced when employing our deep learning based approach for power allocation. Simulation results prove that the proposed technique can obtain a very close to optimal performance with negligible computational complexity.

Unsupervised Deep Learning Approach for Near Optimal Power Allocation in CRAN

The cloud radio access network (CRAN) is considered a very promising architecture for the next generation cellular networks. However, in order to fully utilize the advantages of that architecture, optimization algorithms with superior performance and low complexity are needed. Such algorithms can improve the network performance significantly, in terms of enhancing spectral efficiency and reducing latency. Deep learning based techniques are considered very potential candidates due to their high performance and very low online computational complexity. In this paper, we implement a deep learning based power allocation technique for CRAN. Unlike the previous works, we consider user association in our optimization problem, which is essential to reflect a real cellular scenario. Simulation results prove that our technique can obtain a very close to optimal performance, with negligible computational complexity.

Mohamed Labana

Papers

An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

Joint User Association and Resource Allocation in CoMP-Enabled Heterogeneous CRAN

Advances in CRAN Performance Optimization

Unsupervised Deep Learning Approach for Near Optimal Power Allocation in CRAN

Unsupervised Deep Learning for Power Allocation in CRAN