Orthogonal Frequency Division Multiple Access Systems Research Articles

Adaptive Carrier Frequency Offset Estimation in Interference Environments for OFDMA Uplink Systems

In orthogonal frequency division multiple access (OFDMA) systems, carrier frequency offsets (CFOs) influence the different users. In this paper, we proposed a CFO estimation algorithm with strong interference resistant capability for OFDMA systems. The first part of the proposed algorithm processes the received signals so as to deal with the CFO estimation problem. And the second part of the proposed algorithm is to use the proposed adaptive process to obtain the updated CFO estimation and track the parameters' time‐variations. Simulations demonstrate the effectiveness of this method and show that its performance is to the Cramer‐Rao Bounds (CRB). © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Deep Reinforcement Learning-Assisted Optimization for Resource Allocation in Downlink OFDMA Cooperative Systems.

This paper considers a downlink resource-allocation problem in distributed interference orthogonal frequency-division multiple access (OFDMA) systems under maximal power constraints. As the upcoming fifth-generation (5G) wireless networks are increasingly complex and heterogeneous, it is challenging for resource allocation tasks to optimize the system performance metrics and guarantee user service requests simultaneously. Because of the non-convex optimization problems, using existing approaches to find the optimal resource allocation is computationally expensive. Recently, model-free reinforcement learning (RL) techniques have become alternative approaches in wireless networks to solve non-convex and NP-hard optimization problems. In this paper, we study a deep Q-learning (DQL)-based approach to address the optimization of transmit power control for users in multi-cell interference networks. In particular, we have applied a DQL algorithm for resource allocation to maximize the overall system throughput subject to the maximum power and SINR constraints in a flat frequency channel. We first formulate the optimization problem as a non-cooperative game model, where the multiple BSs compete for spectral efficiencies by improving their achievable utility functions while ensuring the quality of service (QoS) requirements to the corresponding receivers. Then, we develop a DRL-based resource allocation model to maximize the system throughput while satisfying the power and spectral efficiency requirements. In this setting, we define the state-action spaces and the reward function to explore the possible actions and learning outcomes. The numerical simulations demonstrate that the proposed DQL-based scheme outperforms the traditional model-based solution.

Harmony Search-Based Optimization for Multi-RISs MU-MISO OFDMA Systems

In this letter, we aim to maximize the downlink sum-rate for multi-antenna orthogonal frequency division multiple access (OFDMA) systems with multiple reconfigurable intelligent surfaces (RISs). Due to the high non-convexity of the multiple RISs-aided systems, the sum-rate of the system is often dictated by the initial values of the optimization variables. We propose a novel harmony search-based alternating optimization (AO) algorithm to improve the AO algorithm by searching the high-quality initial value. In the numerical results, the proposed technique outperforms the AO algorithm with random initialization, making the proposed algorithm highly practical.

Energy-Efficient Resource Allocation for Short Packet Transmission in MISO Multicarrier NOMA

In this article, we investigate energy efficiency (EE) maximization of short packet transmission (SPT) in a downlink multiple-input single-output (MISO) multi-carrier non-orthogonal multiple access (MC-NOMA) communication system. We use the ratio of the effective throughput to the power as a performance metric to formulate a non-convex mixed-integer nonlinear programming (MINLP) problem. Then, a low-complexity three-step optimization framework is proposed for MINLP optimization. Under a given maximum transmit power and channel blocklength of each subcarrier, a block coordinate descent (BCD) based method is proposed to jointly optimize the power allocation coefficient, transmission rate, and transmit power within a single subcarrier. Then, we transform the subcarrier allocation problem into a weighted bipartite graph matching problem and obtain the optimal subcarrier allocation scheme using the Kuhn-Munkres (KM) algorithm. Finally, a dynamic programming (DP) method is proposed to obtain the optimal channel blocklength allocation scheme. The simulation results validate the effectiveness of the proposed three-step optimization framework and indicate that the MC-NOMA system achieves higher EE and higher effective throughput than the orthogonal frequency-division multiple access (OFDMA) system in SPT.

Joint Subchannel and Power Allocation for Energy Efficiency Optimization in NOMA Heterogeneous Networks With Energy Harvesting

Nonorthogonal multiple access (NOMA) in heterogeneous networks (HetNets) can effectively improve data rate and spectral utilization by allowing multiple users to share the same channel. However, in trying to meet various requirements of multiuser communication, maintaining a low energy becomes a serious issue in NOMA HetNets. In this article, we study a joint subchannel and power allocation scheme in order to improve the energy efficiency of small cells with energy harvesting unit in NOMA HetNets. In the scheme, we propose a modified Gale–Shapley algorithm based on the interference hypergraph for subchannel allocation that can dynamically adjust the number of small cells on the same subchannel. Furthermore, we optimize the power allocation for small cell users by the Lagrangian dual method. Numerical simulation results are presented to demonstrate that the proposed scheme achieves superior performance compared with the existing well-known methods and traditional orthogonal frequency-division multiple access system.

Enhanced Bundle-based Particle Collision Algorithm for Adaptive Resource Optimization Allocation in OFDMA Systems

The necessity for an efficient algorithm for resource allocation is highly urgent because of the increased demand for utilizing the available spectrum of wireless communication systems. This paper proposes an Enhanced Bundle-based Particle Collision Algorithm (EB-PCA) to get the optimal or near optimal values. It applied to the Orthogonal Frequency Division Multiple Access (OFDMA) to evaluate allocations for the power and subcarrier. The analyses take into consideration the power, subcarrier allocations constrain, channel and noise distributions, as well as the distance between the user’s equipment and the base station. Four main cases are simulated and analyzed under specific operation scenarios to meet the standard specifications of different advanced communication systems. The sum rate results are compared to that achieved by employing another existing algorithm, Bat Pack Algorithm (BPA). The achieved results show that the proposed EB-PAC for the OFDMA system is an efficient algorithm in terms of estimating the optimal or near optimal values for both subcarrier and power allocation.

Channel State Information Prediction for Adaptive Underwater Acoustic Downlink OFDMA System: Deep Neural Networks Based Approach

In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs.

Dynamic power optimization of pilot and data for downlink OFDMA systems

In this paper, we investigate the power allocation problem in downlink orthogonal frequency-division multiple access (OFDMA) networks. Different from previous researches on power allocation, we take into account various practical factors, such as the stochastic traffic arrival, the time-varying channel, the queue stability requirements of all users, the channel estimation cost and the corresponding effect of imperfect channel state information (CSI) on data transmission rate. The power allocation problem is formulated as maximizing the time-averaged data transmission rate by optimizing pilot and data power allocation subject to the queue stability and the maximum transmit power constraints. The data transmission rate is defined in terms of the pilot transmit power, the data transmit power and the channel estimation error, which is non-concave. To solve the non-concave and stochastic optimization problem, a dynamic pilot and data power allocation (DPDPA) algorithm is proposed with the aids of approximate transformation, Lyapunov optimization and Lagrange dual formulation. Moreover, we derive the bounds of performances, in terms of the time-averaged data transmission rate and queue length.

Capacity Analysis of NOMA-Enabled Underwater VLC Networks

Visible light communication (VLC) has recently emerged as an enabling technology for high capacity underwater wireless sensor networks. Non-orthogonal multiple access (NOMA) has been also proven capable of handling a massive number of sensor nodes while increasing the sum capacity. In this paper, we consider a VLC-based underwater sensor network where a clusterhead communicates with several underwater sensor nodes based on NOMA. We derive a closed-form expression for the NOMA system capacity over underwater turbulence channels modeled by lognormal distribution. NOMA sum capacity in the absence of underwater optical turbulence is also considered as a benchmark. Our results reveal that the overall capacity of NOMA-enabled Underwater VLC networks is significantly affected by the propagation distance in underwater environments. As a result, effective wireless transmission at high and moderate spectral efficiency levels can be practically achieved in underwater environments only in the context of local area networks. Moreover, we compare the achievable capacity of NOMA system with its counterpart, i.e., orthogonal frequency division multiple access (OFDMA). Our results reveal that NOMA system is not only characterized by achieving higher sum capacity than the sum capacity of its counterpart, OFDMA system. But also, the distances between sensor nodes and the clusterhead for achieving the highest sum capacity in these two multiple access systems are different.

Energy-efficient cross-layer resource allocation scheme for OFDMA systems

Recently, the interest of the telecommunication operators and Internet service providers (ISPs) in energy efficiency (EE) for wireless networks has remarkably increased. The boost in energy prices, the continuous increase of the user numbers, the pervasion of wideband access, and the diffusion of offered services have motivated this interest. The challenge is to minimize the transmitted energy without compromising the quality of service (QoS) and the network performance. In this paper, a new efficient Orthogonal Frequency Division Multiple Access (OFDMA) resource allocation for Long Term Evolution (LTE)-advanced systems based on cross-layer optimization between Physical (PHY) and Medium Access Control (MAC) layers is proposed. A new solution based on Rate Adaptive (RA) principle is presented to achieve better EE resource allocation. The purpose behind the new model is to increase the capacity of the system as much as possible while trying to decrease the total transmit power. Hence, the EE which is equal to the capacity to total transmit power ratio is increased. As reducing the transmit power for the transmission of a given capacity reduces the power consumption, the greenhouse gas (GHG) emission will be then reduced. On the other hand, the service differentiation is highly considered to provide high QoS support. The users are differentiated according to different service classes and different data rates. The proposed cross-layer-based solution has then to allocate the system resources in an optimal way while satisfying the users having high QoS requirements.

A low complexity iterative equalization technique for OFDMA and SC-FDMA uplink systems with Tx-Rx IQ imbalance and CFO under doubly selective channels

The next-generation wireless communication systems must address the compensation of in-phase and quadrature phase imbalance (IQI), carrier frequency offset (CFO), and doubly selective channel (DSC) to provide a high quality of service for high mobility users with low-cost radio . None of the existing works consider all these RF impairments under DSC for multi-user scenario. To address this gap, in this paper, we propose an iterative equalization technique to compensate the multi-user interference (MUI) and self inter-carrier interference (S-ICI) induced by the joint effect of transmitter IQI, receiver IQI, CFO and DSC for single carrier frequency division multiple access (SC-FDMA) and orthogonal frequency division multiple access (OFDMA) uplink systems. In each iteration of the proposed equalization technique, the extrinsic information from the decoder is used to cancel the MUI and S-ICI from the received signal. After interference cancellation, each mirror subcarriers pairs are equalized simultaneously. Additionally, unlike existing works, our proposed technique does not put any restriction on the carrier assignment scheme (CAS) used with OFDMA/SC-FDMA systems. Further, to validate the performance of the proposed compensation scheme analytically, we derive an upper bound on the bit error rate (BER) of coded OFDMA and SC-FDMA system without MUI and S-ICI. It is established that the system BER with the proposed technique satisfies the derived upper bound as the iteration converges, demonstrating the effective cancellation of MUI and S-ICI due to all the impairments.

Experienced Deep Reinforcement Learning With Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication

In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC-6G) in the downlink of a wireless network. The goal is to guarantee high end-to-end reliability and low end-to-end latency, under explicit data rate constraints, for each wireless user without any models of or assumptions on the users' traffic. In particular, in order to enable the deep-RL framework to account for extreme network conditions and operate in highly reliable systems, a new approach based on generative adversarial networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL framework using a mix of real and synthetic data, thus creating an experienced deep-RL framework that has been exposed to a broad range of network conditions. The proposed deep-RL framework is particularly applied to a multi-user orthogonal frequency division multiple access (OFDMA) resource allocation system. Formally, this URLLC-6G resource allocation problem in OFDMA systems is posed as a power minimization problem under reliability, latency, and rate constraints. To solve this problem using experienced deep-RL, first, the rate of each user is determined. Then, these rates are mapped to the resource block and power allocation vectors of the studied wireless system. Finally, the end-to-end reliability and latency of each user are used as feedback to the deep-RL framework. It is then shown that at the fixed-point of the deep-RL algorithm, the reliability and latency of the users are near-optimal. Moreover, for the proposed GAN approach, a theoretical limit for the generator output is analytically derived. Simulation results show how the proposed approach can achieve near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. The results also show that the proposed experienced deep-RL framework is able to remove the transient training time that makes conventional deep-RL methods unsuitable for URLLC-6G. Moreover, during extreme conditions, it is shown that the proposed, experienced deep-RL agent can recover instantly while a conventional deep-RL agent takes several epochs to adapt to new extreme conditions.

An Efficient Carrier Frequency Offset Tracking for OFDMA Systems Using Normalized Least-Mean-Square Algorithm

In orthogonal frequency-division multiple access (OFDMA), carrier frequency offset (CFO) destroys the orthogonality between subcarriers due to Doppler effect of the channel and also causes inter-carrier interference (ICI) as well as multiple access interference (MAI). In this paper, CFO tracking algorithm for OFDMA system is presented to overcome the effect of ICI as well as MAI. The proposed algorithm employs normalized least-mean-square (NLMS) algorithm which tracks the residual carrier frequency offset in OFDMA system. The proposed NLMS-based tracking algorithm uses average of all residual carrier frequency offset (RCFO) for improving the mean square error (MSE) performance. The proposed NLMS-based frequency tracking along with frequency offset compensator improves the error performance by reducing the effect of ICI and MAI. Simulation results indicate that the proposed algorithm achieves better tracking performance in the presence of frequency offset. From the results, it is observed that the MSE of the proposed estimator is improved about 3.8 dB at SNR = 20 dB with the frequency offset.

Low-PMEPR Preamble Sequence Design for Dynamic Spectrum Allocation in OFDMA Systems

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks.

Optimal Multi-View Video Transmission in OFDMA Systems

In this letter, we study the transmission of a multi-view video (MVV) to multiple users in an Orthogonal Frequency Division Multiple Access (OFDMA) system. To maximally improve transmission efficiency, we exploit both natural multicast opportunities and view synthesis-enabled multicast opportunities. First, we establish a communication model for transmission of a MVV to multiple users in an OFDMA system. Then, we formulate the minimization problem of the average weighted sum energy consumption for view transmission and synthesis with respect to view selection and transmission power and subcarrier allocation. The optimization problem is a challenging mixed discrete-continuous optimization problem with huge numbers of variables and constraints. A low-complexity algorithm is proposed to obtain a suboptimal solution. Finally, numerical results further demonstrate the value of view synthesis-enabled multicast opportunities for MVV transmission in OFDMA systems.

Secure Transmission for SWIPT IoT Systems With Full-Duplex IoT Devices

This article investigates physical layer security of a downlink multiuser orthogonal frequency division multiplexing (OFDM) Internet of Things (IoT) system with an access point communicating with multiple legitimate IoT devices in the presence of multiple eavesdroppers. For coordinating multiuser communication, the orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) are considered. The IoT devices are assumed to support simultaneous wireless information and power transfer (SWIPT) and can use the harvested energy to jam the eavesdroppers based on full-duplex. The resource allocation problems of maximizing the sum secrecy rate for the OFDMA and TDMA systems are investigated. We first consider the scenario that perfect channel state information (CSI) is available and derive suboptimal algorithms based on alternating optimization. Then we consider the scenario that CSI is imperfect and propose heuristic algorithms. The secrecy performances of the OFDMA and TDMA systems with jamming from the SWIPT IoT devices are compared using extensive simulations. It is shown that the secrecy performance of the OFDMA system is superior over the TDMA system. It is also shown that the algorithm with imperfect CSI is inferior over the algorithm with perfect CSI, but it can outperform the benchmark algorithm with perfect CSI and without jamming.

IM-OFDMA: A Novel Spectral Efficient Uplink Multiple Access Based on Index Modulation

This paper proposes a novel uplink multiple access scheme to enhance the overall spectral efficiency of wireless systems. The core idea is to exploit index modulation (IM) concept in orthogonal frequency division multiple access (OFDMA) systems. In the proposed scheme, entitled IM-OFDMA, part of the transmitted bits of each user is conventionally modulated, while the rest of the bits determine the index of the active subcarrier that will be used to communicate the modulated symbol. Unlike OFDMA, IM-OFDMA does not require any scheduling algorithm, which results in data collision between the user's received messages. Yet, it is revealed that IM-OFDMA outperforms OFDMA and state of the art uplink non-orthogonal multiple access (NOMA) over a wide range of system and channel parameters. Besides, it is reported that IM-OFDMA can serve up to 3 times the number of served users in OFDMA system while maintaining similar error performance per user. The average bit error rate of IM-OFDMA scheme is derived in a closed form expression that is substantiated through Monte Carlo simulation results.

Energy-Efficient Resource Allocation for OFDMA Heterogeneous Networks

We proposed several energy-efficient resource allocation algorithms for the downlink of an orthogonal frequency-division-multiple-access (OFDMA) based femtocell heterogeneous networks (HetNets). Heterogeneous QoS and fairness in rate are investigated in the proposed resource allocation problem. A dense deployment of femtocells in the coverage area of a central macrocell is considered and energy usage of both femtocell and macrocell users are optimized simultaneously. We aim to maximize the weighted sum of the individual energy efficiencies (WSEEMax) and the network energy efficiency (NEEMax) while satisfying the following: (1) minimum throughput for delay-sensitive (DS) users, (2) fairness constraint for delay-tolerant (DT) users, (3) required constraints of OFDMA systems. The problem is formulated in three different forms: mixed 0–1 integer programming formulation, time-sharing formulation and sparsity-inducing formulation. The proposed resource block (RB) and power optimization problems are combinatorial and highly non-convex due to the fractional form of the objective function, the integer constraint of OFDMA RBs and non-affine fairness. We adopt the successive convex approximation (SCA) approach and transform the problems into a sequence of convex subproblems. With the proposed algorithms, we show that the overall joint RB and power allocation schemes converge to suboptimal solutions. Numerical examples confirm the merits of the proposed algorithms.

Low-complexity joint channel estimation and symbol detection for OFDMA systems

In this paper, we propose a joint channel estimation and symbol detection (JCESD) algorithm relying on message-passing algorithms (MPA) for orthogonal frequency division multiple access (OFDMA) systems. The channel estimation and symbol detection leverage the framework of expectation propagation (EP) and belief propagation (BP) with the aid of Gaussian approximation, respectively. Furthermore, to reduce the computation complexity involved in channel estimation, the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula. Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0.1 dB.

Joint rate adaptation and resource allocation for real-time H.265/HEVC video transmission over uplink OFDMA systems

We consider multiuser video communication over uplink orthogonal frequency-division multiple access (OFDMA) systems. A cross-layer algorithm of joint bit allocation, packet scheduling and wireless resource assignment are proposed to minimize the end-to-end expected video distortion. Video rate adaptation is performed under the wireless resource constraints. The target number of encoding bits for each video packet is obtained to minimize the estimated distortion based on the online content-based rate-distortion function. Due to the inaccuracy of the rate control algorithm in H.265/HEVC encoding, the actual number of bits may differ from the target. Accordingly, the actual encoder distortion may deviate from the estimated distortion. Then, we propose an iterative algorithm to re-assign wireless resources based on the actual number of encoded bits to obtain the final resource allocation policy and packet scheduling decision. Numerical simulation results show that our proposed approach significantly outperforms the baseline algorithms in terms of received video quality.