Frequency Division Multiple Access Systems Research Articles

Joint CFO and Channel Estimation for RIS-Aided Multi-User Massive MIMO Systems

Accurate channel estimation is essential to achieve the performance gains promised by the use of reconfigurable intelligent surfaces (RISs) in wireless communications. In the uplink of multi-user orthogonal frequency division multiple access (OFDMA) systems, synchronization errors such as carrier frequency offsets (CFOs) can significantly degrade the channel estimation performance. This becomes more critical in RIS-aided communications, as the RIS phases are adjusted based on the channel estimates and even a small channel estimation error leads to a significant performance loss. Motivated by this, we propose a joint CFO and channel estimation method for RIS-aided multi-user massive multiple-input multiple-output (MIMO) systems. Our proposed pilot structure makes it possible to accurately estimate the CFOs without multi-user interference (MUI), using the same pilot resources for both CFO estimation and channel estimation. For optimization of the RIS phase shifts at the data transmission stage, we propose a projected gradient method (PGM) which achieves the same performance as a more computationally demanding grid search technique while having a significantly lower computational complexity. Simulation results demonstrate that the proposed method provides an improvement in the normalized mean-square error (NMSE) of channel es-timation as well as in the bit error rate (BER) performance. Moreover, analyzing the computational complexity and the pilot resource efficiency of the proposed method, we demonstrate that the proposed estimation approach requires low computational load and no extra cost in pilot overhead.

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.

Performance analysis of wireless compressed-image transmission over DST-based OFDMA systems

Multimedia data, like images, consumes significant bandwidth when transmitted over wireless systems. Therefore, compressing transmitted images becomes crucial to reduce the required bandwidth and improve energy efficiency. This work aims to analyze the performance of transmitting wireless compressed images over a recent Discrete Sine Transform (DST)-Based Orthogonal Frequency Division Multiple Access (DST-OFDMA) system. It investigates the effectiveness of several image compression methods by determining the minimum Signal-to-Noise Ratio (SNR) required for each method to achieve error-free image recovery at the receiver. This work considers different modulation schemes including 16QAM and QPSK, as well as different subcarrier mapping schemes (localized and interleaved) over vehicular A, SUI3, and uniform channels. Nine standard compression methods are used for analyzing the performance of the DST-OFDMA system and compared it with that of the conventional Discrete Fourier Transform (DFT)-based OFDMA (DFT-OFDMA) system. The results show that the performance of DST-OFDMA outperforms that of DFT-OFDMA, especially when QPSK modulation is used. Simulation results demonstrate that the interleaved DST-OFDMA (DST-IOFDMA) system, employing the SPIHT_3D compression method and QPSK modulation (over the SUI3 channel model), achieves the lowest SNR value required for compressed image recovery, approximately 18 dB. This indicates that the SPIHT_3D compression method exhibits lower power consumption compared to other methods as well as high bandwidth efficiency.

On companding techniques for PAPR reduction in DCT SC-FDMA system in the presence of CFOs

In the single carrier frequency division multiple access (SC-FDMA) system, the use of discrete cosine transform (DCT) instead of discrete Fourier transform (DFT) has shown improved bit error rate (BER) performance. Nevertheless, peak-to-average power ratio (PAPR) in the DCT-based SC-FDMA system is higher than that in the DFT-based system. This paper investigates the performance of various companding schemes to reduce PAPR in DCT-based SC-FDMA systems by considering carrier frequency offsets (CFOs). Simulation results, considering parameters like BER, PAPR, and power spectral density (PSD), are provided to find the best companding technique. Furthermore, the results have been validated in a real-time indoor channel using a wireless open-access research platform (WARP) hardware.

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.

Channel temporal correlation-based optimization method for imperfect underwater acoustic channel state information

The rapid time variations and large channel estimation errors in underwater acoustic (UWA) channels mean that transmitters for adaptive resource allocation quickly become outdated and provide inaccurate channel state information (CSI). This results in poor resource allocation efficiency. To address this issue, this paper proposes an optimization approach for imperfect CSI based on a Gauss–Markov model and the per-subcarrier channel temporal correlation (PSCTC) factor. The proposed scheme is applicable to downlink UWA orthogonal frequency division multiple access systems. The proposed PSCTC factors are measured, and their long-term stability is verified using data recorded in real-world sea tests. Simulation and experimental results show that the optimized CSI effectively mitigates the effects of the temporal variability of UWA channels. It demonstrates that the resource allocation scheme using optimized CSI achieves a higher effective throughput and a lower bit error rate than both imperfect CSI and the CSI predicted by the recursive least-squares (RLS) algorithm.

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.

A Powerful Joint Modulation and STBC Identification Algorithm for Multiuser Uplink SC-FDMA Transmissions

Owing to the rapid development and broad adoption of multiple antenna communication systems over the past few years, space-time block coding (STBC) identification has emerged as a crucial responsibility for smart radios. The majority of previous analysis of STBC identification assumed that the utilized modulation schemes for single-user and uncoded broadcasts were known. This paper investigates the challenge of joint STBC and modulation identification for uplink transmissions with numerous users in single-carrier frequency division multiple access (SC-FDMA) systems using coded transmissions. Multi-user channel estimation brings us one step closer to implementing the proposed design in real-world systems. We additionally employ the channel decoder’s deliverables, which are common in many real-world systems, to enhance the identifying and estimating procedures. Mathematical findings prove that a recursive approach can be utilized to tackle the maximum likelihood (ML) problem of simultaneous STBC and modulation identification with channel estimation. Distinguishing the superimposed signals that originate at the base-station (BS) is accomplished with the use of the space-alternating generalized expectation-maximization (SAGE) algorithm. After that, an expectation-maximization (EM) engine is deployed to make the necessary adjustments to the parameters being considered for each user. The success of the above-mentioned architecture for usage in practical applications is demonstrated by the simulation results obtained under various conditions.

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.

Intersymbol interference resilient interleaving architecture for multi‐stream interleaved frequency division multiple access system

SummaryA multi‐stream cyclic interleaving architecture for an interleaved frequency division multiple access (IFDMA) system is proposed. The proposed scheme exploits the full benefits of maximal length sequence ( ‐sequence), in contrast to single data stream transmission scheme proposed in earlier works, thereby allowing simultaneous transmission of multiple data streams by each user. The cyclic spreading allows the separation of multiple data streams for each user in addition to intersymbol interference (ISI) minimization. The assignment of orthogonal subcarriers to each user compensates multiuser interference (MUI). The increase in the number of data stream transmission improves the data rate of the system. The proposed scheme employs maximal ratio combining (MRC) to maximize the instantaneous signal‐to‐noise ratio (SNR) of the multipath signal. The performances of the proposed interleaving scheme are compared with the minimum mean square error frequency domain equalization (MMSE‐FDE)‐based conventional interleaving scheme. Despite the simultaneous transmission of multiple data streams, the proposed scheme retains the performance of single data stream transmission.

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.

Performance evaluation of wireless compressed‐image transmission over discrete Fourier transform‐based orthogonal frequency division multiple access system

Orthogonal frequency division multiple access (OFDMA) has been attracting much attention in the recent generation of wireless communications to meet the required demands arising from the explosive growth of Internet, multimedia and broadband services. Currently, images transmission is one of the main parts of wireless communication. However, an image file transmission requires sending a large amount of data which consumes a huge bandwidth so that the image must be compressed to reduce the amount of information to be transmitted. The aim of this paper is to evaluate the wireless transmission of the compressed image over discrete Fourier transform (DFT)-based OFDMA DFT-OFDMA system for different compression methods, different modulation schemes and different subcarriers mapping schemes over three wireless channels including SUI3, vehicular A and uniform. The required minimum signal-to-noise ratio (SNR) to recover the transmitted compressed image is determined to evaluate the system performance. For the sake of diversity, nine compression techniques are used in this work. The MATLAB simulator is used to determine the required minimum SNR to recover the transmitted compressed image for each compression method. The results show that the performance of the set partitioning in hierarchical trees three-dimensional for true colour images (SPIHT-3D) method is nearly better than that of other compression methods, especially when the QPSK and interleaved system are considered.

STBC Identification for Multi-User Uplink SC-FDMA Asynchronous Transmissions Exploiting Iterative Soft Information Feedback of Error Correcting Codes

With the advancement and widespread implementation of multiple-input multiple-output (MIMO) wireless communication systems over the last decade, space-time block coding (STBC) identification has become a critical task for intelligent radios. Previous examinations of STBC identification were focused on single-user transmissions over single-carrier and multi-carrier systems in combination with uncoded broadcasts. Practical systems, on the other hand, contain many users and employ error-correcting codes. For the first time in literature, this work explores the problem of STBC identification for multi-user uplink transmissions in single-carrier frequency division multiple access (SC-FDMA) systems. We take another step closer to real systems by addressing asynchronous transmissions and by conducting multi-user channel estimation. We also exploit the outputs of the channel decoder, which is usually used in many practical systems, to improve the identification and estimation processes. The mathematical analysis demonstrates that the maximum-likelihood (ML) solution of STBC identification, channel estimation, and synchronization can be executed by an iterative approach. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to separate the overlaid signals arriving at the base-station (BS). The parameters under consideration for each user are then updated using an expectation-maximization (EM) processor. Simulation results show that the proposed architecture outperforms other identification methods published in the literature while maintaining a reasonable level of processing time.

LDPC coded hybrid discrete cosine transform and Fejér–Korovkin wavelet transform-based SC-FDMA for image communication

A low-density parity-check (LDPC) coded single-carrier frequency-division multiple access (SC-FDMA) system is proposed for effective image transmission. The employment of the Fejér–Korovkin wavelet transform (FKWT) in combination with a discrete cosine transform (DCT) has been conceived in the proposed system. SC-FDMA is capable of mitigating the channel-induced dispersion at a low peak-to-average power ratio (PAPR). The performance of the proposed scheme is compared to that of the traditional wavelet transform-based SC-FDMA system in terms of different performance metrics. The scheme provides a remarkable performance gain compared to its LDPC coded wavelet transform-based SC-FDMA counterpart. Additionally, FKWT provides improved performance compared to the commonly used Haar wavelet transform-based system.

A game-based deep reinforcement learning approach for energy-efficient computation in MEC systems

Many previous energy-efficient computation optimization works for mobile edge computing (MEC) systems have been based on the assumption of synchronous offloading, where all mobile devices have the same data arrival time or calculation deadline in orthogonal frequency division multiple access (OFDMA) or time division multiple access (TDMA) systems. However, the actual offloading situations are more complex than synchronous offloading following the first-come, first-served rule. In this paper, we study a polling callback energy-saving offloading strategy, that is, the arrival time of data transmission and task processing time are asynchronous. Under the constraints of task processing time, the time-sharing MEC data transmission problem is modeled as the total energy consumption minimization model. Using the semi-closed form optimization technology, energy consumption optimization is transformed into two subproblems: computation (data partition) and transmission (time division). To reduce the computational complexity of offloading computation under time-varying channel conditions, we propose a game-learning algorithm, that combines DDQN and distributed LMST with intermediate state transition (named DDQNL-IST). DDQNL-IST combines distributed LSTM and double-Q learning as part of the approximator to improve the ability of processing and predicting time intervals and delays in time series. The proposed DDQNL-IST algorithm ensures rationality and convergence. Finally, the simulation results show that our proposed algorithm performs better than the DDQN, DQN and BCD-based optimal methods.

Priority-Based Downlink Wireless Resource Provisioning for Radio Access Network Slicing

This paper examines network slicing within the radio access network which employs an orthogonal frequency division multiple access system for downlink communications. Its radio resources are allocated among slices configured for specific 5G use cases as well as other non-5G services. This work sets out to achieve optimal resource provisioning and power allocation within the context of priority slicing where slices are assigned priority levels by the base station. A mixed-integer non-linear programming problem is formulated to maximize the throughput of a best effort slice while satisfying the constraints of the high priority 5G slices. The problem is simplified and relaxed into a convex optimization problem. Three scenarios under various conditions are presented to serve as benchmarks. The impact of the chosen schedulers on the allocations, intra-slice, and inter-slice contentions is also examined. The results highlight that the wireless network can satisfy the quality-of-service requirements of opposing levels of priorities of traffic while simultaneously mitigating both inter-slice and intra-slice contentions under conditions mirroring that of practical wireless network deployments. Furthermore, it is proven that the convex approximation problem serves as a reasonable approximation of the original MINLP problem.

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.

Novel Bee Colony Optimization with Update Quantities for OFDMA Resource Allocation

In recent years, large usage of wireless networks puts forward challenge to the utilization of spectrum resources, and it is significant to improve the spectrum utilization and the system sum data rates in the premise of fairness. However, the existing algorithms have drawbacks in efficiency to maximize the sum data rates of orthogonal frequency division multiple access (OFDMA) systems in the premise of fairness threshold. To address the issue, a novel artificial bee colony algorithm with update quantities of nectar sources is proposed for OFDMA resource allocation in this paper. Firstly, the population of nectar sources is divided into several groups, and a different update quantity of nectar sources is set for each group. Secondly, based on the update quantities of nectar sources set for these groups, nectar sources are initialized by a greedy subcarrier allocation method. Thirdly, neighborhood searches and updates are performed on dimensions of nectar sources corresponding to the preset update quantities. The proposed algorithm can not only make the initialized nectar sources maintain high levels of fairness through the greedy subcarrier allocation but also use the preset update quantities to reduce dimensions of the nectar sources to be optimized by the artificial bee colony algorithm, thereby making full use of both the local optimization of the greedy method and the global optimization of the artificial bee colony algorithm. The simulation results show that, just in the equal-power subcarrier allocation stage, the proposed algorithm can achieve the required fairness threshold and effectively improve the system sum data rates.

Optimum DCT type-I based transceiver model and effective channel estimation for uplink NB-IoT system

Single-tone and multi-tone transmissions in the narrowband Internet of Things (NB-IoT) uplink system can be accomplished by employing different orthogonal basis. In this paper, we develop an uplink NB-IoT baseband system model and derive the corresponding analytical signal model based on the discrete cosine transform type-I (DCT-I) domain. Basically, the circular convolution and channel matrix diagonalization issues for designing DCT-based systems are addressed without exploiting any redundancy (a prefix and a suffix sequences, and zero-padding (ZP)) into each transmitted data symbol blocks. In our proposed DCT-I based transceiver, we employ a standard cyclic prefix (CP) and apply discrete Fourier transform (DFT) and inverse DFT (IDFT) operations directly to make the channel matrix circulant and diagonal, resulting in the use of one-tap frequency-domain channel equalizer without incorporating extra front-end prefilter at the receiver. Furthermore, we propose novel least squares (LS) and linear minimum mean square error (LMMSE) channel estimators by considering the energy concentration and spectral compaction properties of DCT-I for the uplink NB-IoT system. The expressions for the DCT-I domain LS and LMMSE estimators are derived for channel frequency response estimation and mean square error computation. Finally, the viability of the proposed CP-DCT-I-NB-IoT uplink system, as well as the channel estimators, are tested compared with the standardized CP-DFT-NB-IoT and ZP-DCT type-2 even based single-carrier frequency-division multiple access (SC-FDMA) systems through rigorous computer simulations.

Millimeter Wave Massive MIMO Downlink Per-Group Communications With Hybrid Linear Precoding

We address the problem of analyzing and classifying into groups the downlink channel environment in a dense millimeter-wavelength cell, accounting for path loss, multipath fading, and User Equipment (UE) blocking, by employing a hybrid propagation and multipath fading model, thus using accurate inter-group interference modeling. The base station (BS) employs a large Uniform Planar Array (UPA) to facilitate massive Multiple-Input, Multiple-Output (MIMO) communications with high efficiency. UEs are equipped with a single antenna and are distributed uniformly within the cell. The key problem is analyzing and defining groups toward efficient precoding. Because balanced throughput is desired between groups, up to four separate frequency sub-carriers are employed in an Orthogonal Frequency Division Multiple Access (OFDMA) system. We show that by employing three or four OFDMA subcarriers, depending on the number of UEs in the cell and the cell range, the UEs can be efficiently separated into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-throughput</i> groups, with each group employing Virtual Channel Model Beams (VCMB) based inner precoding, followed by efficient Multi-User Multiple-Input Multiple-Output (MU-MIMO) outer precoders. For each group, we study two different sub-grouping methods offering different advantages. We show that the improvement offered by Zero-Forcing Per-Group Precoding (ZF-PGP) over Matched Filter Precoding (MFP) and Zero-Forcing Precoding (ZFP) is very high when finite-alphabet inputs are used. We also show ZF-PGP with finite-alphabet inputs significantly outperforms MFP with Gaussian inputs in certain realistic setups. Finally, a new technique for power allocation among different UEs is proposed, called Optimized Per-Group Power Allocation (OPGPA), which achieves much higher power efficiency jointly with equal throughput among all UEs in each group.