Resource Block Research Articles

Cognitive radio technology with reduced PAPR and complexity for IoT-based OFDM networks

The principle target of an International Mobile Telecommunication (IMT) is to build a platform to advance the improvement of technology used in 5G and to inspire the support with the MNCs and organizations. The journey of 5th Generation initiated with 1G technology and has provided a massive improvement in today’s world. 5G technology on IMT provides the target to achieve ultra-high data rate, connectivity, reliability, lower latency, and lower data traffic. The usage of Orthogonal Frequency-Division Multiplexing (OFDM) system in 4G technology causes high Peak-to-Average Power Ratio (PAPR), complexity, and out-of-band radiation. To avoid these parameters, Filtered (F)-OFDM is exploited in 5G technology. The design becomes complex by the division of spectrum into non-contiguous spectrum fragments. So, resource block F-OFDM system using Internet of Things is proposed to convert non-contiguous spectrum into contiguous spectrum but the complexity remains the same. The proposed work of this paper is IoT-based RB-F-OFDM system along with inverse fast Walsh-Hadamard and Fourier transform to decrease the complexity and also, adaptive filter is exploited for retaining the actual (regular) grid to avoid signal information required for specifying a particular subband in the spectrum. Also, to enhance the quality of service, spectrum efficiency and cognitive radio system are used along with these techniques. Simulation result shows that proposed RB-Adaptive Filtered (AF)-OFDM provides low PAPR, high signal-to-interference ratio, and low bit error rate performance. The proposed RB-AF-OFDM system has the benefits of both RB-F-O FDM and adaptive F-OFDM so it further reduces the PAPR and complexity to the considerable value which is around 2.5.

Load Management, Power and Admission Control in Downlink Cellular OFDMA Networks

We present a resource management framework for load-coupled downlink cellular OFDMA networks considering the load factor of an individual base station (BS) per resource block (RB), i.e., the number of adjacent sub-carriers (SCs), as the variable of interest in the resource management problem. The load factor of a BS per RB, which corresponds to the fraction of active SCs in the BS per RB, is an indicator of the level of resource consumption, and it affects the interference caused to that RB reused in other BSs, and thereby, results in a load-coupled OFDMA system. We first propose two distributed schemes to minimize: (i) the total load factor of the BSs (which would in turn increase the number of supportable users in the system), and (ii) the total downlink transmit power level of the BSs. Then, we derive the necessary and sufficient conditions for checking the feasibility of given target-rate requirements (also referred to as demand vector) for users. Accordingly, an iterative and distributed scheme is proposed to check the feasibility of a given demand vector. Next, for a priority-based load-coupled network, we propose a priority-based gradual removal algorithm to support the maximal number of low-priority users while satisfying the demands of the high-priority users. To evaluate the performance of our proposed schemes for resource management and admission control in load-coupled OFDMA networks, the theoretical investigations are complemented with Monte Carlo simulations.

UAV-Sensing-Assisted Cellular Interference Coordination: A Cognitive Radio Approach

Aerial-ground interference mitigation has been deemed as the main challenge in realizing cellular-connected unmanned aerial vehicle (UAV) communications. Due to the line-of-sight (LoS)-dominant air-ground channels, the UAV generates/suffers much stronger interference to/from cellular base stations (BSs) over a much larger region in its uplink/downlink communication, as compared to the terrestrial users. As a result, conventional inter-cell interference coordination (ICIC) techniques catered for terrestrial networks become ineffective in mitigating the more severe UAV-induced interference. To deal with this new challenge, this letter introduces a cognitive radio based solution by treating the UAV and terrestrial users as secondary and primary users in the network, respectively. In particular, the LoS channels with terrestrial BSs/users endow the UAV with a powerful spectrum sensing capability for detecting the terrestrial signals over a much larger region than its serving BS. By exploiting this unique feature, we propose a new UAV-sensing-assisted ICIC design for both the UAV downlink and uplink communications. Specifically, the UAV senses its received interference and the transmissions of terrestrial users in the downlink and uplink, respectively, over the resource blocks (RBs) available at its serving BS to assist its RB allocation to the UAV for avoiding the interference with co-channel terrestrial communications. Numerical results demonstrate that the proposed UAV-assisted ICIC outperforms the conventional terrestrial ICIC by engaging the neighboring BSs for cooperation only.

Fractional Frequency Reuse in Multi-tier Networks: Performance Analysis and Optimization

Frequency Reuse (FR) is an efficient approach to improve the network performance in a multi-cell cellular network. In this paper, we investigate the performance of heterogeneous networks utilising two well-known frequency reuse algorithms, called Strict FR and Soft FR, with a reuse factor of $$\varDelta$$ ($$\varDelta > 1$$). Based on a two-phase operation of the FR algorithm, we develop a new approach to analyse cellular networks based on the Poisson Point Process (PPP) model which successfully demonstrates the impact of the network parameters (such as the number of allocated Resource Blocks (RBs) and bias factor) and FR parameters (such as Base Station (BS) transmit power) on the performance of a user and overall network. Compared to related works, we propose the following novel approaches: (i) we investigate flexible FR networks in which users have connections with the BSs which deliver the highest performance; (ii) the BS observes SINR on the data channel to classify each user into either a Cell-Edge User (CEU) or Cell-Center User (CCU); (iii) the initial state of the network is considered to establish the initial network interference when new users arrive and request connections to the BSs. In the case of a single-tier network, it is proved that our analytical approach is more accurate than previous works. In the case of a two-tier network, the analytical results indicate that compared to the 3GPP model, our proposed model not only reduces up to 40.79% and 3.8% power consumption of a BS on the data channel but also achieves 16.08% and 18.63% higher data rates in the case of Strict FR and Soft FR respectively. Furthermore, the paper presents an approach to find an optimal value of SINR thresholds and bias factor to achieve the maximum network performance.

MmWave-NOMA-Based Low-Latency and High-Reliable Communications for Enhancement of V2X Services

The enhanced vehicle-to-everything (eV2X) systems have stricter constraints of latency and reliability than before. To meet the low latency and high reliability (LLHR) demands, the investigation of semi-persistent scheduling (SPS) is of great significance. For the first time, we integrate millimeter wave (mmWave) and non-orthogonal multiple access (NOMA) into SPS strategy. The proposed strategy includes the beam division and user clustering process, the power allocation process, and the vehicular user equipment (VUE) – resource block (RB) matching process. Firstly, the joint-optimization problem in terms of user scheduling indicators and power allocation factors is formulated to minimize the SPS period. This problem is then proved to be non-deterministic polynomial hard (NP-hard) by being reduced into a vertex coloring problem. To solve it with polynomial complexity, we divide it into two subproblems and calculate them by iteration. For one subproblem, the LLHR power control algorithm is proposed to solve the non-convex reliability-optimization problem, which can provide an evaluation indicator for the user scheduling. For the other subproblem, the beam division and VUE clustering algorithm is designed to reduce the complexity of the VUE-RB matching. After that, the matching problem with peer effects is solved by the proposed union-based matching algorithm. The analytical results on stability, convergence and computational complexity are presented, and simulation results show that the proposed SPS strategy outperforms the existing schemes.

Joint Power and Resource Block Allocation for Mixed-Numerology-Based 5G Downlink Under Imperfect CSI

Fifth-generation (5G) of wireless networks are expected to accommodate different services with contrasting quality of service (QoS) requirements within a common physical infrastructure in an efficient way. In this article, we address the radio access network (RAN) slicing problem and focus on the three 5G primary services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC) and massive machine-type communications (mMTC). In particular, we formulate the joint allocation of power and resource blocks to the heterogeneous users in the downlink targeting the transmit power minimization and by considering mixed numerology-based frame structures. Most importantly, the proposed scheme does not only consider the heterogeneous QoS requirements of each service, but also the queue status of each user during the scheduling of resource blocks. In addition, imperfect Channel State Information (CSI) is considered by including an outage probabilistic constraint into the formulation. The resulting non-convex problem is converted to a more tractable problem by exploiting Big-M formulation, probabilistic to non-probabilistic transformation, binary relaxation and successive convex approximation (SCA). The proposed solution is evaluated for different mixed-numerology resource grids within the context of strict slice-isolation and slice-aware radio resource management schemes via extensive numerical simulations.

Multiple Delay Estimation for Collision Resolution in Non-Orthogonal Random Access

In machine-type communications (MTC), contention-based random access is employed to support a number of MTC devices with a limited number of resource blocks (RBs). Since multiple active devices may transmit signals in the same RB or channel, the collision caused by the presence of multiple signals is inevitable and the detection of collision becomes important. Furthermore, if the arrival time and the number of multiple signals can be estimated, successive interference cancellation (SIC) can be employed in time domain to improve the throughput. In this paper, we focus on the estimation of round-trip delays (RTD) of multiple signals in non-orthogonal random access (NORA) based on the maximum likelihood (ML) criterion. Since the computational complexity of the ML approach is high, we propose a low-complexity approach based on variational inference, which is widely used in machine learning. We also show that the number of signals can be reliably estimated from the estimated RTDs.

A Survey of Rate-Optimal Power Domain NOMA With Enabling Technologies of Future Wireless Networks

The ambitious high data-rate applications in the envisioned future B5G networks require new solutions, including the advent of more advanced architectures than the ones already used in 5G networks, and the coalition of different communications schemes and technologies to enable these applications requirements. Among the candidate schemes for future wireless networks are NOMA schemes that allow serving more than one user in the same resource block by multiplexing users in other domains than frequency or time. In this way, NOMA schemes tend to offer several advantages over OMA schemes such as improved user fairness and spectral efficiency, higher cell-edge throughput, massive connectivity support, and low transmission latency. With these merits, NOMA-enabled transmission schemes are being increasingly looked at as promising multiple access schemes for future wireless networks. When the power domain is used to multiplex the users, it is referred to as PD-NOMA. In this paper, we survey the integration of PD-NOMA with the enabling communications schemes and technologies that are expected to meet the various requirements of B5G networks. In particular, this paper surveys the different rate optimization scenarios studied in the literature when PD-NOMA is combined with one or more of the candidate schemes and technologies for B5G networks including MISO, MIMO, mMIMO, advanced antenna architectures, mmWave and THz, CoMP, cooperative communications, cognitive radio, VLC, UAV and others. The considered system models, the optimization methods utilized to maximize the achievable rates, and the main lessons learnt on the optimization and the performance of these NOMA-enabled schemes and technologies are discussed in detail along with the future research directions for these combined schemes. Moreover, the role of machine learning in optimizing these NOMA-enabled technologies is addressed.

Towards Energy Efficient Load Balancing for Sustainable Green Wireless Networks Under Optimal Power Supply

The enormous growth in the cellular networks and ubiquitous wireless services has incurred momentous energy consumption, greenhouse gas (GHG) emissions and thereby, imposed a great challenge to the development of energy-efficient sustainable cellular networks. With the augmentation of harvesting renewable energy, cellular base stations (BSs) are progressively being powered by renewable energy sources (RES) to reduce the energy crisis, carbon contents, and its dependency on conventional grid supply. Thus, the combined utilization of renewable energy sources with the electrical grid system is proving to be a more realistic option for developing an energy-efficient as well as an eco-sustainable system in the context of green mobile communications. The ultimate objective of this work is to develop a traffic-aware grid-connected solar photovoltaic (PV) optimal power supply system endeavoring the remote radio head (RRH) enabled heterogeneous networks (HetNets) aiming to minimize grid energy consumption and carbon footprint while ensuring long-term energy sustainability and energy efficiency (EE). Moreover, the load balancing technique is implemented among collocated BSs for better resource blocks (RBs) utilization and thereafter, the performance of the system is compared with an existing cell zooming enabled cellular architecture for benchmarking. Besides, the techno-economic feasibility of the envisaged system has been extensively analyzed using HOMER optimization software considering the dynamic nature of solar generation profile and traffic arrival rate. Furthermore, a thorough investigation is conducted with the help of Monte-Carlo simulations to assess the wireless network performance in terms of throughput, spectral efficiency (SE), and energy efficiency as well under a wide range of design scenarios. The numerical outcomes demonstrate that the proposed grid-tied solar PV/battery system can achieve a significant reduction of grid power consumption yielding up to 54.8% and ensure prominent energy sustainability with the effective modeling of renewable energy harvesting.

Scheduling Algorithm supporting V2X Communications based on NOMA access

5G promises very high throughput and low latency. It will be able to support the exchange of large amounts of data with a wide variety of communications, including Vehicle-to-Everything (V2X) services. Conventional resource allocation techniques based on orthogonal multiple access (OMA) appear to be unsuitable for a dense network due to limited resources available. This work presents a new planning algorithm called SAVCN (Scheduling Algorithm for V2X Communication based on NOMA), for the 5G network. The main feature of NOMA is the same resource can be shared by multiple users. The purpose of our algorithm is to improve network performance in terms of throughput, equity, the number of V2X users served and error rates. In fact, SAVCN efficiently allocates available resource blocks (Rbs) to maximize system throughput by taking into account a well-defined criterion on the minimum distance between transmitters and receivers. The simulation results indicate promising performance for SAVCN.

Physical Layer Detection of Malicious Relays in LTE-A Network Using Unsupervised Learning

For Long Term Evolution Advanced (LTE-A) network, although there exist many studies that focus on improving the performance with relays, security issues are often neglected. Due to the broadcast nature of wireless channels, relay nodes in LTE-A network may act maliciously, affect communication, reduce quality, and cause delays. Recently, physical (PHY) layer security has attracted researchers to provide secure communication and data privacy. In this study, we propose using unsupervised learning approach at the destination node to detect malicious relay attacks in cooperative LTE-A network based on received source signal in the PHY layer. Outlier detection algorithms such as one class support vector machine (OCSVM), local outlier factor (LOF) and isolation forest ( $i$ Forest) are applied to detect various malicious relay behaviors such as garbling, regenerative, and false data injection type attacks. As input to these algorithms, feature vectors are constructed by using amplitude, phase, and relative phase information of modulated baseband symbols. The performance of the outlier detectors are evaluated with respect to precision, accuracy, and under the area curve (AUC) measures under changing signal-to-noise ratio (SNR) levels, different modulation types, allocated number of resource blocks (RBs), and varying data size. The results demonstrate the effectiveness of our proposed outlier detection approach for detecting malicious relays in the LTE-A network. Accuracy and precision of the algorithms are observed to be above 90% for 10 dB and larger SNR levels for the relay attack scenarios considered here. AUC values for all algorithms for all SNR levels is also above 0.9 for the attack detection cases, and the performance of the LOF algorithm with 0.95 and above AUC values is superior to other algorithms. The results verify the contribution of this study, which is the demonstration of the effectiveness of one class outlier detection approaches for detecting malicious relays in the LTE-A network.

Analysis of SIR and Rate Meta Distributions for 3D Heterogenous Ultra-Dense Networks With Joint Offloading and Resource Partitioning

In heterogeneous ultra-dense networks (HUDNs), load balancing is always considered by offloading users from the macro base station (MBS) tier to the small base station (SBS) tier, to alleviate the data traffic of the MBS tier, while it causes a serious problem that the offloaded users will suffer strong interference from the MBS tier, and hence the signal-to-interference ratio (SIR) is in low region for the offloaded users. In order to improve the performance of these offloaded users, an interference mitigation scheme based on resource partitioning is employed in the literature. Specifically, a fraction of resource blocks (RBs) is reserved for the offloaded users, and then the SIRs of these users will be enhanced because there is only interference from the SBS tier. In summary, the combination of offloading and resource partitioning is called joint offloading and resource partitioning scheme, which has been investigated in the HUDNs built on the 2D space, while it has rarely been deliberated in 3D HUDNs that are used to model the scenario of high-rise buildings. Moreover, a two-tier 3D HUDN is proposed in this paper, and the MBS and SBS tiers are modeled as two independent Poisson point processes. Then, the SIR and rate meta distributions, which are used to characterize the fraction of users that achieve a desired link reliability given SIR or rate thresholds, are formulated for the proposed 3D HUDN. Lastly, numerical results show that with the usage of the joint offloading and resource partitioning scheme, the performance of the proposed 3D HUND can be significantly improved.

Joint Resource Allocation, User Association, and Power Control for 5G LTE-Based Heterogeneous Networks

The aim of 5G wireless networks to provide Mbps and Gbps data rates to end users is expected to be fulfilled by the advanced technologies such as multi-input multi-output (MIMO), carrier aggregation (CA), inter/intra-cell communication, and adaptive modulation and coding techniques, which would be all realized in the Long Term Evolution-Advanced (LTE-A) heterogeneous network constituted by macrocells (MCs) and small cells (SCs) adopting these 5G advanced techniques. Given the potential of significantly increasing the network performance, the resource allocation (RA) problem involved becomes harder than ever especially when MIMO and CA are included in the RA problem involving multiple types of resources to be concurrently determined for the global optimization. Facing this challenge, we develop a framework to jointly optimize energy efficiency (EE), spectrum efficiency (SE), and queue length for downlink transmissions with an overall and comprehensive consideration of dynamically allocating resource blocks (RBs), component carriers (CCs), modulation and coding schemes (MCSs), and deciding user association (UA) with a power control (PC) mechanism on discrete power levels (PLs) in the heterogeneous LTE-based MIMO wireless networks. Specially, for the complex joint RA, UA, and PC problem, we conduct a mixed integer programming model to accommodate the stochastic optimization problem involved with the drift-plus-penalty (DPP) approach for Lyapunov opportunistic optimization. In particular, although it involves a nondeterministic polynomial time (NP) problem, we can still show a reduced problem to be solved easily through linear relaxation when its coefficient matrix is totally unimodular (TUM), and to be solved efficiently as well even when the TUM property is not guaranteed. Based on the reduction, we further develop a distributed or semi-distributed algorithm operated on two levels to approach the optimal results with lower complexity if the UA requirement can be relaxed. Finally, apart from exhibiting its performance on the weighting parameters, the numerical experiments also show our approach to make a good tradeoff among SE, EE, and queue length, and outperform the greedy-based state-of-the-art algorithms.

A QoE-Aware Mode Selection Framework for Video Streaming in D2D Networks

Device-to-Device (D2D) communication is a novel approach that has been proposed to enhance the performance of traditional cellular networks. This work focuses on video streaming using D2D communications underlaying a Long Term Evolution (LTE) network where resources are allocated to each D2D pair in three modes of operation; cellular, dedicated and reuse. The objective is to maximize the sum-rate of the D2D pairs while maintaining continuous video playback with an acceptable quality level. Unlike previous works, which assign one channel to each user or divide the available resources equally between them or assume constant bit rate streaming (CBR), we estimate the number of Resource Blocks (RBs) needed for all users according to their rate and Signal-to-Noise Ratio (SNR) requirements and consider Scalable Video Coding (SVC). A complete Quality of Experience (QoE)-aware framework for D2D-based video streaming that includes resource allocation, power control, mode selection and SVC layer selection is then proposed while taking into consideration buffer occupancies and the video peak signal-to-noise ratio (PSNR). The efficacy of the proposed scheme is evaluated through extensive simulations and compared to the scenario where only one mode of operation is assigned to all D2D pairs. Furthermore, the effect of the number of D2D users as well as their mobility is also investigated. Simulation results show that the proposed framework improves the throughput of the D2D pairs when compared to a single mode of operation while at the same time providing better video quality and continuity.

Ieee Access Special Section Editorial: Advances in Signal Processing for Non-Orthogonal Multiple Access

Orthogonal multiple access (OMA) technologies have prevailed from the first-generation (1G) to the fourth-generation (4G) of modern mobile communications, primarily because of their low complexity. The key idea of OMA is to ensure that the communication resources allocated to different users are orthogonal in at least one radio resource dimension. As a result, the number of active users allowed to access the OMA system is strictly limited by the number of available orthogonal resources, which becomes less effective for supporting massive connectivity and achieving user fairness. In contrast to OMA, non-orthogonal multiple access (NOMA) simultaneously accommodates a multitude of users using the same radio resource block via superposition signaling and employs various transmit or receive signal processing techniques to mitigate the resulting interference. However, the success of NOMA technologies relies heavily on the implementation of advanced signal processing techniques for transceivers, which may introduce large processing delays and increase computational complexity. Thanks to recent progress in hardware and theory in signal processing and machine learning, high signal processing complexity has become more affordable and processing latency can be significantly reduced, which enables the development of NOMA. Thus, sophisticated signal processing algorithms for multi-user detection, scheduling, and interference management are indispensable for the successful implementation of NOMA in the next-generation wireless systems.

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naive Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.

Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected, so the problem is modeled as a stochastic non-cooperative game. Hence, each agent becomes a player and they make decisions together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm based on game theory is established. Secondly, in order to accelerate the convergence rate of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats each group as an agent, and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users. The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system. In particular, FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.

Transmission Scheduling for Remote Estimation with Multi-packet Reception under Multi-Sensor Interference

In networked control systems, due to competing demands on bandwidth and energy constraints, sensor scheduling is an important problem for remote estimation and control tasks. Traditionally, a single sensor is scheduled in each resource block to avoid interference or collisions so that the probability of packet loss is reduced. However, receiving multiple packets from different sources under interference is routinely achieved in wireless networks using multi-packet reception techniques. In this work, we explore the problem of sensor scheduling for remote estimation when the estimator is able to simultaneously receive multiple packets. We use the typical signal-to-interference-and-noise-ratio (SINR) based capture model to compute the packet arrival probabilities. Then an optimal scheduling policy is determined by minimizing expected estimation error covariance subject to a constraint on the average number of total transmissions. In the case of two sensors, for a scalar system and for a decoupled two-dimensional system, we show that allowing multiple simultaneous transmissions can improve the quality of the estimation achieving lower energy consumptions and we provide structural results on the optimal policies. Numerical results illustrate the benefits of multi-packet reception in remote estimation.

Minimizing Age of Information in Energy Harvesting Wireless Sensor Networks

We consider the uplink of an energy harvesting (EH) wireless sensor network (WSN) where N single-antenna sensors communicate with a common fusion center (FC) with the aim of cooperatively minimizing the overall average age of information (AoI). Specifically, we propose new resource allocation algorithms to minimize the average AoI in an EH-WSNs employing common multiple-access schemes, in particular time-division multiple access (TDMA) and frequency-division multiple access (FDMA). To this end, we take advantage of the convexity of the derived AoI, enabling an optimal resource block assignment, implemented as a greedy algorithm for TDMA systems and in the form of an alternating direction method of multipliers (ADMM) scheme for FDMA systems. The optimality of the greedy resource allocation scheme derived for the TDMA case is obtained by design, whereas that of the ADMM-based method derived for the FDMA case is demonstrated numerically. Simulation results indicate that the choice between TDMA or FDMA depends on the available resources, size of the data packet, and the time of packet observation in the system.

Secure Transmission Designs for NOMA Systems Against Internal and External Eavesdropping

The key idea of non-orthogonal multiple access (NOMA) is to serve multiple users in the same resource block to improve the spectral efficiency. Whereas due to the resource sharing, a security flaw of NOMA emerges in the presence of internal untrusted users, especially untrusted near users who are closer to the base station and can easily access the confidential information for paired far users. To mitigate the flaw, in this paper, we investigate the reliable and secure transmission of NOMA systems with untrusted near users, and propose joint beamforming and power allocation (JBP) scheme for the scenario. Meanwhile, from security point of view, we extend to a worse-case scenario where both untrusted near users and external eavesdroppers exist, and propose joint artificial noise aided beamforming and power allocation (JANBP) scheme to achieve a reliable and secure transmission for the scenario. The exact and asymptotic closed-form expressions of secrecy outage probability (SOP) for the two scenarios are derived to evaluate the secrecy performance achieved by the proposed schemes, respectively. The analysis and simulation results show the superiority of the proposed JBP and JANBP schemes in terms of combating internal and external eavesdropping, and also indicate the two schemes can achieve the same SOP at high SNR.