Orthogonal Frequency Division Multiple Access Research Articles

Secure Resource Allocations for Polarization-Enabled Multiple-Access Cooperative Cognitive Radio Networks With Energy Harvesting Capability

We address secure communications over energy-harvesting based orthogonal frequency-division multiple-access (OFDMA) cooperative cognitive radio networks, where one primary user (PU) cooperates with several size-limited secondary users (SUs) in terms of both information transmission and energy harvesting. To improve spectrum utilization and ensure that SU transmitters can harvest as much energy as possible, we let the size-limited SUs be equipped with orthogonally dual-polarized antennas (ODPAs). Based on these setting-ups, we propose the polarization-enabled two-phase cooperative framework, where SU transmitters first apply the power splitting technique to harvest energy from radio frequency (RF) signals radiated by the PU, and then use the harvested energy to concurrently transmit their own and the PU’s data over the same subcarriers. Under our proposed framework, we develop three secure resource allocation schemes for scenarios when SUs are untrusted users, which implies that each SU may overhear the PU’s and the other SUs’ confidential information. For these scenarios, which has hardly been studied, we jointly optimize the allocation of relays, subcarriers, power splitting ratios, and powers when SUs adopt the decode-and-forward (DF) strategy or the amplify-and-forward (AF) strategy to relay the PU’s information, with the objective to maximize the total secrecy rate of all SUs while guaranteeing the PU’s minimum secrecy rate. Finally, we validate and evaluate our proposed cooperative framework and secure resource allocation schemes through numerical analyses.

Interference mitigation in intentional jammers aided non-uniform heterogeneous cellular networks.

Coverage and capacity are optimized in fifth generation (5G) networks by small base station (SBS) distribution in the coverage realm of macro base station (MBS). However, system performance is significantly reduced by inter-cell interference (ICI) because of the orthogonal frequency division multiple access assumption. In addition to ICI, this work considers intentional jammers' interference (IJI) due to the presence of jammers. These Jammers try to inject undesirable energies into the legitimate communication band, which significantly degrade uplink (UL) signal-to-interference ratio (SIR). To reduce ICI and IJI, in this work, we employ SBS muting, where the SBSs near MBS are switched off. To further mitigate ICI and IJI, we use one of the effective interference management schemes a.k.a reverse frequency allocation (RFA). We presume that due to mitigation in ICI and IJI, the UL coverage performance of the proposed network model can be further improved.

Performance Analysis of QoS-Oriented OFDMA Protocol Based on IEEE 802.11ax for Cognitive Radio Network

To improve the quality of service (QoS) on the internet of medical things, a cognitive radio (CR) protocol based on orthogonal frequency division multiple access (OFDMA) is proposed, named CR-OFDMA. In this protocol, we divide a complete channel into multiple orthogonal subchannels and enhance the subchannel assignment scheme, which achieves QoS improvement under consideration of priority and fairness. Furthermore, we improve spectrum resource utilization by fully utilizing the remaining subchannels, feedback slots, and backoff slots. Then, a two-dimensional Markov model is established to describe the dynamic characteristics of the protocol operation, where the backoff stage and the backoff counter value constitute the system state. By establishing the traffic conservation equations for the system operation, the transmission probability and collision probability are calculated, and expressions of system throughput, channel utilization, and fairness index are derived. Finally, numerical results validate the advantages of CR-OFDMA.

SCMA-Enabled Multi-Cell Edge Computing Networks: Design and Optimization

Multi-access edge computing (MEC) is regarded as a promising approach for providing resource-constrained mobile devices with computing resources through task offloading. Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) scheme that can meet the demands of multi-cell MEC networks for high data transmission rates and massive connections. In this paper, we propose an optimization framework for SCMA-enabled multi-cell MEC networks. The joint resource allocation and computation offloading problem is formulated to minimize the system cost, which is defined as the weighted energy cost and latency. Due to the nonconvexity of the proposed optimization problem induced by the coupled optimization variables, we first propose an algorithm based on the block coordinate descent (BCD) method to iteratively optimize the transmit power and edge computing resources allocation by deriving closed-form solutions, and further develop an improved low-complexity simulated annealing (SA) algorithm to solve the computation offloading and multi-cell SCMA codebook allocation problem. To solve the problem of partial state observation and timely decision-making in long-term optimization environment, we put forward a multiagent deep deterministic policy gradient (MADDPG) algorithm with centralized training and distributed execution. Furthermore, we extend the framework to the partial offloading case and propose an algorithm based on alternating convex search for solving the task offloading ratio. Numerical results show that the proposed multi-cell SCMA-MEC scheme achieves lower energy consumption and system latency in comparison to the orthogonal frequency division multiple access (OFDMA) and power-domain (PD) NOMA techniques.

Performance analysis of coexistence of traditional communication system and emerging semantic communication system

As a new paradigm, semantic communication (SC) is attracting attention as the next-generation communication. In order to develop semantic communication systems, the research trend is to adopt deep learning networks with the best performance in each field of deep learning. Deep-learning based SC systems are task-dependent because the deep networks employed are specialized to perform specific tasks. Therefore, SC systems cannot be used for users using applications that do not work with the deep networks exploited by the SC systems. Such users still should be served by a traditional communication (TC) system. Hence, in the near future, communication networks will face the coexistence of both SC system and TC system, and thus a base station (BS) has to simultaneously transmit symbol streams encoded by an SC system and a TC system via orthogonal frequency-division multiple access. Although the current works verify that SC systems outperform a TC system in a point-to-point communication, it is necessary to analyze whether the introduction of SC systems can also improve the network performance provided by a BS. In this paper, we investigate the impact of introducing the new emerging semantic communication on network performance by modeling signal-to-noise ratio of SC users served by SC systems and analyzing sum-rate maximization with a minimum required SNR constraint for SC users. Via extensive numerical results, changes in network performance with and without an SC system are discussed with varying the distances from a BS, the number of SC users served by an SC system, and the degree of SC performance.

COMPARISON OF PERFORMANCES BETWEEN SC-FDMA AND OFDMA SYSTEMS UNDER DIFFERENT SUBCARRIER MAPPING SCHEMES

To improve the uplink performance of wireless network for long term evolution (LTE) single carrier frequency division multiple access (SC-FDMA) and orthogonal frequency division multiple access (OFDMA) are used. Two major concerns of these SC-FDMA and OFDMA are high peak-to-average power ratio (PAPR) and efficient subcarrier mapping scheme. This paper presents a performance comparison of SC-FDMA and OFDMA under different subcarrier mapping schemes which includes localized FDMA (LFDMA), distributed FDMA (DFDMA) and interleaved FDMA (IFDMA). Quadrature amplitude modulation (QAM) with different constellation sizes is used. The symbol error rate (SER) in SC-FDMA is minimized using appropriate mapping scheme. The PAPR is less in SC-FDMA compared to OFDMA. SC-FDMA with IFDMA shows better performance for the PAPR reduction compared to the OFDMA. Compared to OFDMA, using SC-FDMA with 8-QAM and IFDMA, PAPR is reduced by 78%, using SC-FDMA with 16-QAM and IFDMA, PAPR is reduced by 80%, using SC-FDMA with 64-QAM and IFDMA, PAPR is reduced by 68%.

Multi-users different rates visible light communication system for sixth-generation multimedia applications

The two most significant issues that communication engineers have been confronting since the development of fifth-generation wireless networks are bandwidth increase and energy-saving techniques in data transmission. We present an innovative solution for utilizing a multi-user different-rates visible light communication (VLC) system for sixth-generation applications. Such a solution takes advantage of orthogonal frequency division multiple access (OFDMA) with orthogonal codes as multiple access communication networks. For the achievement of energy-saving and wide beam width of the optical source, the light emitting diode is often used as a transmitter. Furthermore, one of the most popular orthogonal codes in use is the double length modified prime code, which has been utilized for enhancing communication network security and network capacity. Most importantly, the performance of the network is evaluated versus the number of users, taking into consideration the amount of noise resulting from the multiple access interference, shot noise, and thermal noise. The error vector magnitude has also been considered for performance analysis and results. Significantly, the obtained results show the possibility of accommodating 110 users at an error rate of no more than 10 − 9 and a data rate per user of 50 Gbps.

AlexNet Classifier and Support Vector Regressor for Scheduling and Power Control in Multimedia Heterogeneous Networks

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively.

An open-source implementation and validation of 5G NR configured grant for URLLC in ns-3 5G LENA: A scheduling case study in industry 4.0 scenarios

Factories are undergoing a digital transformation towards more cost-efficient, zero-defect manufacturing. The digitalized factories require communication networks capable of satisfying their strict latency and reliability demands. 5G and beyond networks are being designed to efficiently support services demanding Ultra-Reliable and Low Latency Communications (URLLC). At the MAC level, the use of dynamic scheduling for uplink transmissions entails a non-negligible latency introduced by the signaling messages exchanged to request and inform about the radio resources allocated for each packet transmission. To reduce the transmission latency, 5G defines Configured Grant (CG) for UL transmissions that pre-allocates radio resources to the User Equipments (UEs) and eliminates the need for requesting resources for each transmission. In this context, the availability of 5G NR simulation tools that accurately implement all 5G NR functionalities, and in particular, the technological enablers introduced in 5G NR to support URLLC, is key to analyze the capability of 5G and beyond networks to support time-critical services and research on new solutions. The availability and access to such tools are limited, and to the best of the authors' knowledge, there are currently no open-source 5G NR simulators that implement configured grant in 5G NR. To overcome this issue, this work presents the first implementation of configured grant in an open-source 5G NR simulator. In particular, configured grant has been implemented in the ns-3 5G-LENA system-level simulator, and it is publicly available. To accurately model the flexibility of 5G NR, we have also improved the implementation of Orthogonal Frequency Division Multiple Access (OFDMA) access mode in 5G-LENA according to 5G NR. To validate the implementation of CG and analyze the capability of 5G NR to support time-critical services, we analyze the latency performance that can be achieved using CG with different scheduling policies in Industry 4.0 scenarios. The results show that the latency values achieved with CG in 5G-LENA match with those reported by previous analytical studies. In addition, this study shows the importance of efficiently using radio resources to reduce the latency experienced and meet the requirements of critical services.

A 39-GHz CMOS Bidirectional Doherty Phased- Array Beamformer Using Shared-LUT DPD With Inter-Element Mismatch Compensation Technique for 5G Base Station

This article demonstrates a 39-GHz CMOS phased-array beamformer aiming for power-efficient and area-efficient fifth-generation (5G) dual-polarized multiple-in–multiple-out (DP-MIMO) applications. To address the digital pre-distortion (DPD) implementation issue in the massive beamformer elements with Doherty technique integrated, an inter-element mismatch compensation technique is introduced for improving the shared-lookup table (LUT) DPD performance over the process, voltage and temperature (PVT) variations. A bidirectional Doherty power amplifier (PA)-LNA is proposed to enhance the power back-off (PBO) efficiency regarding the high peak-to-average power ratio (PAPR) 5G signals, meanwhile cost down the system by the unbalanced neutralized bidirectional operation. The proposed phased-array beamformer chip is fabricated in 65-nm CMOS technology and packaged in a wafer-level chip-scale package (WLCSP). Each element occupies only a 0.82-mm2 chip area, including the on-chip low-dropout regulator (LDO). The measured stand-alone Doherty PA-LNA achieves 18.9-dBm saturated output power with 17.8% power-added efficiency (PAE) at 6-dB PBO in PA mode and obtains a 4.8-dB noise figure (NF) at 40 GHz in LNA mode. By utilizing the proposed mismatch compensation, the measured 64-quadrature amplitude modulation (QAM) orthogonal frequency-division multiple access (OFDMA)-mode error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR) with the shared-LUT DPD are improved from −22.4 to −25.0 dB and from −28.7 to −32.1 dBc, respectively. The 64-element module achieves a 55.2-dBm saturated effective isotropic radiated power (EIRP) and supports 21-Gb/s single-carrier (SC) mode data streaming. The measured 64-element transmitter (TX) EVM is −25.2 dB at 43.2 dBm with 3.5-GSymbol/s baud rate in 64 QAM, and the corresponding 64-to-4 elements TX-to-receiver (RX) EVM is −22.5 dB. The consumed power for each element is 402 mW at saturation output point in TX mode and 87 mW in RX mode.

Equalization Techniques for SC-FDMA Systems Under Radio Imbalances at Both Transmitter and Receiver

Orthogonal frequency division multiple access (OFDMA) is a multi-carrier, multiple access (MA) technique, which is widely adopted in contemporary wireless standards. Single carrier-frequency division multiple access (SC-FDMA) is a modified version of OFDMA which employs single carrier transmission by pre-coding the data symbols using discrete Fourier transform (DFT). However, these systems are highly vulnerable to the adverse effects arising in the channel, carrier frequency offset (CFO) and in-phase/quadrature phase (I/Q) imbalances. In the uplink communication scenario, the signal received at base station is the superposition of signals from all the active users. Even though the adverse effects caused by the communication channel and CFOs are addressed in the related literature extensively, the effect of I/Q-imbalances at the transmitter and the receiver is rarely considered. The effect of I/Q-imbalances will make the equalization process at the base station more complex. It is because the overall effective channel with radio impairments must be included in the system modelling. Hence the receiver processing should contain the equalization for effect of the channel, CFOs and I/Q imbalances. In this paper, we propose a novel technique based on the oblique projection (OP) technique for equalizing the channel, radio imbalances and synchronization errors caused by both transmitter (TX) and receiver (RX) for OFDMA/SC-FDMA uplink systems. We also propose an equalization technique with reduced computational complexity to overcome the adverse effects caused by I/Q imbalances and CFOs. The results of the simulation studies illustrate that the proposed techniques can compensate all the above-mentioned effects and they offer very good performance under both TX and RX I/Q imbalances.

Performance analysis of a heterogeneous network employing DAPSK based OFDMA-PON

Recent mobile broadband networks require heterogeneous networks supporting high capacity on demand. A hybrid Fiber-Wireless (Fi-Wi) access network integrating Differential Amplitude and Phase Shift Keying (DAPSK) based — Orthogonal Frequency Division Multiple Access (OFDMA) Passive Optical Network (PON) and 4G wireless network using Radio over Fiber (RoF) technique is proposed. The proposed heterogeneous network is simulated, and the performance is analyzed in terms of Bit Error Rate (BER), Error Vector Magnitude (EVM), Capacity, and Spectral efficiency. The proposed network is simulated considering a higher data rate of 10 and 25 Gbps, and the effect of system parameters like Laser Power, Fiber Length, and Number of users is analyzed. The results show that a higher network capacity of 720 Gbps with an average capacity of about 45 Gbps and a higher spectral efficiency of 4.85 bps/Hz is achieved for the multi-user heterogeneous network link with sixteen users. The minimum value of optical power sufficient to achieve the desired BER is found to be −6 dBm. The suitability of the proposed integrated architecture in supporting multiple services is analyzed by considering different services at each UE. The spectral efficiency of the multi-service link varies from 3 to 4 bps/Hz.

Sum-rate maximization for cognitive relay NOMA Systems with channel uncertainty

In this paper, we study the sum-rate maximization problem for relay cognitive network based on decoding and forwarding (DF), where the primary network is implemented with the orthogonal frequency division multiple access (OFDMA) technology and the secondary users are in the underlying accessing mode. In the system, multiple primary users are included and one of them plays a role of relay. The relay forwards information to secondary users using the non-orthogonal multiple access (NOMA) technology. We attempt to improve the transmission rate of both primary and secondary users. The block power control strategy is developed by addressing interference of primary users caused by the channel sharing with secondary users. To tackle the tricky issue caused by the channel uncertainty, the worst-case method is adopted when solving the proposed sum-rate maximization problem. Considering the nonlinear objective function and constraints, the successive convex approximation (SCA) method is introduced to determine a suboptimal solution. Also, the Lagrangian dual algorithm is utilized to obtain the closed-form solutions of the transmission power and subchannel allocation. Numerical results verify that the proposed scheme significantly improves the sum-rate under the uncertainty channel gains compared to benchmarks.

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.

Towards a Fair Allocation and Effective Utilization of Resource Units in Multi-User WLANs-based OFDMA technology

To promote the MU (Multi-User) communications in High-Efficiency WLANs (Wireless Local Area Networks), the new IEEE 802.11ax standard introduced the concept of RU (Resource Unit) by means of the OFDMA (Orthogonal Frequency Division Multiple Access) transmission technique. Although the OFDMA MU communications enable multiple users to be served at the same time in both DL (Down-Link) and UL (Up-Link) directions, several challenges remain to be met in order to design a MAC (Medium Access Control) protocol that fairly allocates and effectively utilizes the RUs. The challenges addressed are: (1) define when and how using 802.11ax control frames, (2) establish a fair sending policy of UL needs, (3) adopt a strategy to select a channel structure, and (4) avoid loss in spectral efficiency and timeout BA (Block Acknowledgment). In this paper, we proposed the FAEU-RUs protocol (Fair Allocation and Effective Utilization of RUs) by meeting each of the challenges of OFMDA MU communications. The performance evaluation demonstrates that the FAEU-RUs protocol significantly enhances the overall performance of the network but also the individual performance of each type of traffic (DL and UL) and each category of station.

Efficient Space-Time Signal Processing Scheme of Frequency Synchronization and Positioning for Sensor Networks.

The orthogonal frequency division multiple access (OFDMA) technique has been widely employed in sensor networks as the data modulation scheme. This study presents a one-dimensional (1D) space-time signal processing scheme for the joint estimation of direction of arrival (DOA) and carrier frequency offsets (CFOs) in OFDMA uplink systems. The proposed approach, initiated by a one-dimensional ESPRIT algorithm, involves estimating the DOAs of the received signal to identify subscriber positions. Spatial beamformers are then used to suppress multiple access interference and separate each subscriber's signal from the received signal. The outputs of the spatial beamformer are decimated to estimate the CFO of each subscriber. Compared with conventional two-dimensional parameter estimation algorithms, the proposed one-dimensional algorithm has a higher estimation accuracy and significantly lower computational complexity.

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.

Second-Order Statistics for IRS-Assisted Multiuser Vehicular Network With Co-Channel Interference

In this paper, we consider a downlink multi-user vehicular communication network empowered by an intelligent reflecting surface (IRS) with the base station (BS) employing the orthogonal frequency division multiple access (OFDMA) scheme. The performance of the considered system is evaluated in terms of second-order statistics (SOS) in the presence of multiple co-channel interferers with varying powers and speeds. In particular, we develop the analytical expressions of level crossing rate (LCR) and average outage duration (AOD) of the received signal envelope at a desired vehicle by considering a generalized fading model as - distribution. The impact of various system and channel parameters such as number of reflecting elements, amplitude of reflection coefficients, number of interferers, Doppler frequencies of desired and interfering vehicles, and different channel fading environments has been shown on the LCR and AOD performances. Moreover, we derive an expression for asymptotic LCR under high transmit power conditions. It has been shown that the asymptotic LCR is independent of the threshold and transmit power. In addition, we consider a stop and wait automatic repeat request (SW-ARQ) protocol for reliable data packet transmission and derive the expressions for packet error rate (PER) and throughput by utilizing the finite-state Markov channel (FSMC) model. The optimal packet length is also determined which maximizes the throughput under SW-ARQ protocol. The impact of various system parameters is also shown on the PER and throughput performances.

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.

One Bit Aggregation for Federated Edge Learning With Reconfigurable Intelligent Surface: Analysis and Optimization

As one of the most popular and attractive frameworks for model training, federated edge learning (FEEL) presents a new paradigm, which avoids direct data transmission by collaboratively training a global learning model across multiple distributed edge devices, thus overcoming the disadvantage of centralized machine learning in resource limitations, delay constraints, and privacy issues. However, due to the heavy cost of communicating gradient among edge devices, sharing the parameters of a large-scale neural network can still be time-intensive. To alleviate this bottleneck, an efficient scheme, called SignSGD has been recently proposed, where the one-bit gradient quantization with majority vote is featured at edge devices. Nevertheless, the performance of one-bit aggregation will inevitably deteriorate due to the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a novel reconfigurable intelligent surface (RIS)-aided one-bit communication optimization scheme under orthogonal frequency division multiple access (OFDMA) to relieve the negative influence of communication error on the SignSGD-based FEEL. Specifically, a learning convergence analysis is firstly presented to quantitatively characterize the impact of wireless communication error measured by the union bound on pairwise bit error rate (BER) on the performance of SignSGD-based FEEL. Immediately, a unified communication-learning optimization problem is further formulated to jointly optimize the sub-band assignment strategy, the power allocation vector, and the RIS configuration matrix. Numerical experiments show that the proposed design achieves substantial performance improvement compared with the state-of-the-art approaches.