Outage probability and ergodic capacity analysis of satellite–terrestrial NOMA system with mixed RF/mmWave relaying

This paper investigates the performance of hybrid radio frequency/millimeter wave (RF/mmWave) satellite–terrestrial relay network with non-orthogonal multiple access (NOMA) scheme. In particular, the satellite network operates at the Ka-band while the mmWave band is adopted at decode-and-forward (DF) relay–destination link. The blockage effect in mmWave communication is considered to deduce the ordered probability density function (PDF) of the user equipment (UE) distance. However, the randomness of the UEs’ location and the random beamforming design cause the difficulty of the performance analysis. Herein, we provide a closed-form approximation for the outage probability. Moreover, the system diversity order is derived from the asymptotic expressions of the outage probability at high signal-to-noise ratio (SNR) slopes. Besides, approximate ergodic capacity performances of the multi-UEs are evaluated with numerical integration. Simulations are applied to demonstrate the accuracy of the theoretical analysis and show that the performance of the ordered selection NOMA scheme outperforms the orthogonal multiple access scheme and random selection scheme.     

Hybrid Beamforming Design for Self-Interference Cancellation in Full-Duplex Millimeter-Wave MIMO Systems with Dynamic Subarrays

Full-duplex (FD) millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication is a promising solution for the extremely high-throughput requirements in future cellular systems. The hybrid beamforming structure is preferable for its low hardware complexity and low power consumption with acceptable performance. In this paper, we introduce the hardware efficient dynamic subarrays to the FD mmWave MIMO systems and propose an effective hybrid beamforming design to cancel the self-interference (SI) in the considered system. First, assuming no SI, we obtain the optimal fully digital beamformers and combiners via the singular value decomposition of the uplink and downlink channels and the water-filling power allocation. Then, based on the obtained fully digital solutions, we get the dynamic analog solutions and digital solutions using the Kuhn–Munkres algorithm-aided dynamic hybrid beamforming design. Finally, we resort to the null space projection method to cancel the SI by projecting the obtained digital beamformer at the base station onto the null space of the equivalent SI channel. We further analyze the computational complexity of the proposed method. Numerical results demonstrate the superiority of the FD mmWave MIMO systems with the dynamic subarrays using the proposed method compared to the systems with the fixed subarrays and the half-duplex mmWave communications. When the number of RF chains is 6 and the signal-to-noise ratio is 10 dB, the proposed design outperforms the FD mmWave MIMO systems with fixed subarrays and the half-duplex mmWave communications, respectively, by 22.4% and 47.9% in spectral efficiency and 19.9% and 101% in energy efficiency.     

3D beamforming in Intelligent Reflecting Surface (IRS)-assisted multi-user cognitive radio networks

In this paper, we consider a multi-user cognitive radio network (CRN) equipped with an intelligent reflecting surface (IRS). We examine the network performance by evaluating the fairness of the secondary system, which is satisfying the minimum required signal to interference and noise ratio (SINR) for each secondary user (SU). The minimum SINR of the SUs is maximized by joint optimization of the beamforming vector and three-dimensional beamforming (3DBF) angles at the secondary base station (SBS) and also the phase shifts of the IRS elements. This optimization problem is highly non-convex. To solve this problem, we utilize Dinkelbach’s algorithm along with an alternating optimization (AO) approach to achieve some sub-problems. Accordingly, by further applying a semi-definite relaxation method, we convert these sub-problems to equivalent convex forms and find a solution. Furthermore, analytically we propose an algorithm for optimizing 3DBF angles at the SBS. Through numerical results, the improvement of the sum SINR of the secondary system using the proposed method is illustrated. Moreover, it is shown that as the number of reflecting elements of IRS increases, the sum SINR significantly augments while satisfying fairness. Also, the convergence of the proposed algorithm is verified utilizing numerical results.     

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.     

An effective approach for initial access in 5G-millimeter wave-based Vehicle to Everything (V2X) communication using Improved Genetic Algorithm

The process of establishing a directional communication link between the vehicle (VE) on the road and a roadside unit (RSU) is known as initial access process in 5G-millimeter wave(mmWave)-Vehicle to Everything (V2X) communications. Initial access is a tricky problem because substantial interruption or delay can be experienced where the RSU and the VE tries to discover the suitable beam alignment for establishing a direct communication link. Thus, it is very important to resolve this initial access problem in an effective way. Moreover, with the popularity of 5G-mmWave based V2X communication many researchers are trying to address this problem. Therefore, in this paper, we will be presenting a novel beam refinement technique that uses Improved Genetic Algorithm which is quite useful when the system is comprised of large number of antenna arrays. In this work we have considered that RSU and VE are equipped with multiple input multiple output (MIMO) antenna system. Proposed improved genetic algorithm includes some key improvements in terms of selection procedure, elitism, crossover and mutation operations. The effectiveness of the proposed work is investigated in terms of Capacity achieved vs number of transmit and receive antennas at RSU and VE, codebook size, outage probability and total transmitted power. Moreover, a detail analysis has been performed with previous state of the art works in terms of key performance metrics like: capacity achieved by 5G-V2X system, outage probability, and total transmitted power utilization. The proposed work has shown improved beam refinement by solving the initial access problem effectively.     

Relay selection and nonlinear energy harvesting in full-duplex multi-relay cooperative network

Energy harvesting (EH) is a promising technique to extend the lifetime energy-constrained devices in wireless networks. This paper analyses the significance of relay selection and non-linear EH in evaluating the performance of Full-duplex (FD) decode-and-forward (DF) multi-relay cooperative network. The relay selection is considered under three scenarios based on channel state information (CSI) availability. We compare the EH performance of a hybrid power-time-splitting (PTS)-based non-linear energy harvester with time-spitting (TS) and power-splitting (PS) EH at the relay node. We derive the expression of outage probability (OP) of FD DF multi-relay cooperative network over independent and identically distributed Rayleigh fading channels. Numerical results show that the outage performance of the system is significantly enhanced by deploying an ideal relay selection scheme. This paper also gives insights about the combined effect of saturation threshold of non-linear EH and residual self-interference (RSI) on the system performance. Unlike linear EH systems, the non-linear EH causes the outage floor even when the RSI is significantly small. The response of the system is also analysed for the impact of TS and PS ratios. Furthermore, we also investigate the system’s performance in terms of other system parameters like the number of relays and data transfer rate. Monte Carlo simulations are used to verify the analytical expressions.     

Beamspace channel estimation in wideband lens antenna array-based mmWave mMIMO-OFDM systems under beam squint

Sparse Bayesian learning (SBL) and particularly relevant vector machines (RVMs) have drawn much attention to improving the performance of existing machine learning models. The methodology depends on a parameterized prior that enforces models with weight sparsity, where only a few are non-zeros. Wideband mmWave massive multiple-input multiple-output (mMIMO) systems with lens antenna array (LAA), expect to play a key role in future fifth-generation (5G) wireless systems. To provide the beamforming gain required to overcome path loss, we consider a lens antenna array (LAA)-based beamspace mMIMO system. However, the spatial-wideband influence causes the beam squint effect to emerge, making the beamspace channel path components exhibit a unique frequency-dependent sparse structure, and thus nullifies the frequency domain common support assumption. In this paper, we first propose a channel estimation (CE) algorithm, namely a reduced-antenna selection progressive support-detection (RAS-PSD), for the wideband mmWave mMIMO-OFDM systems with LAA, which considers the beam squint effect. Secondly, by exploring Bayesian learning (BL), a Gaussian Process hyperparameter optimization-based CE (GP-HOCE) algorithm is proposed for the considered system, where both its own hyperparameter and the hyperparameters of its adjacent neighbors governs the sparsity of each coefficient. The simulation results show that the wideband beamspace channel coefficients can be estimated more efficiently than those of the existing state-of-the-art algorithms in terms of normalized mean square error (NMSE) of CE for wideband mmWave mMIMO-OFDM systems.     

Security concerns on machine learning solutions for 6G networks in mmWave beam prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack.     

Deep learning assisted time-varying channel estimation in multi-user mmWave hybrid MIMO systems

Hybrid analog/digital multiple input multiple output (MIMO) system is proposed to mitigate the challenges of millimeter wave (mmWave) communication. This architecture enables utilizing the large array gain with reasonable power consumption. However, new methods are required for the channel estimation problem of hybrid architecture-based systems due to the fewer number of radio frequency (RF) chains than antenna elements. Leveraging the sparse nature of the mmWave channels, compressed sensing (CS)-based channel estimation methods are proposed. Recently, machine learning (ML)-aided methods have been investigated to improve the channel estimation performance. Additionally, the Doppler effect should be considered for the high mobility scenarios, and we deal with the time-varying channel model. Therefore, in this article, we consider the scenario of time-varying channels for a multi-user mmWave hybrid MIMO system. By proposing a Deep Neural Network (DNN) and defining the inputs and outputs, we introduce a novel algorithm called Deep Learning Assisted Angle Estimation (DLA-AE) for improving the estimation of the Angles of Departure/Arrival (AoDs/AoAs) of the channel paths. In addition, we suggest Linear Phase Interpolation (LPI) to acquire the path gains for the data transmission instants. Simulation results show that utilizing the proposed DLA-AE and LPI methods enhance the time-varying channel estimation accuracy with low computational complexity.     

On spectrum sensing in cognitive radio CDMA networks with beamforming

In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%).     

SINR- and MI-Based Double-Robust Waveform Design

Owing to cognitive radar breaking the open-loop receiving–transmitting mode of traditional radar, adaptive waveform design for cognitive radar has become a central issue in radar system research. In this paper, the method of radar transmitted waveform design in the presence of clutter is studied. Since exact characterizations of the target and clutter spectra are uncommon in practice, a single-robust transmitted waveform design method is introduced to solve the problem of the imprecise target spectrum or the imprecise clutter spectrum. Furthermore, considering that radar cannot simultaneously obtain precise target and clutter spectra, a novel double-robust transmitted waveform design method is proposed. In this method, the signal-to-interference-plus-noise ratio and mutual information are used as the objective functions, and the optimization models for the double-robust waveform are established under the transmitted energy constraint. The Lagrange multiplier method was used to solve the optimal double-robust transmitted waveform. The simulation results show that the double-robust transmitted waveform can maximize SINR and MI in the worst case; the performance of SINR and MI will degrade if other transmitted waveforms are employed in the radar system.     

Performance analysis of a hybrid fiber Bragg grating-based spectral-amplitude-coding/successive interference cancellation for optical CDMA systems

Successive interference cancellation (SIC) scheme is proposed as a new technique that has the potential to reduce the interference and suppress the multiple-access interference (MAI) in spectral-amplitude-coding (SAC) optical-code-division-multiple access (OCDMA) systems. The proposed receiver-based on fiber Bragg gratings (FBGs) techniques. The performance of the proposed system is theoretically analyzed, taking into account various types of noise and interference, including both multiple-access interference (MAI) and receiver noise. Analytical results show that the proposed system offers significant improvement in terms of bit error rate and system capacity (number of users). We have tested our results with both modified prime codes (MPR) and modified quadratic congruence (MQC) codes. In addition, we have compared our proposed system with the corresponding one without cancellation. It has been shown that the proposed scheme gives a substantial increase in capacity.     

Performance investigation on harmonic distortion and inter-modulation distortion induced degradation for a single- and two-tone IM-DD SCM optical link

Subcarrier multiplexing (SCM) has been utilized for data transmission in high performance broadband in access networks which requires high transport capacity and throughput with guaranteed quality of service. In a transmitter employing SCM technique in an Intensity Modulation Direct Detection (IM-DD) system, laser source nonlinearity is an important issue of concern. This work investigates the sensitivity of resulting harmonic distortion (HD) and inter-modulation distortion (IMD) to changes in subcarrier amplitude, laser bias current operational parameters and laser quantum efficiency, laser active layer volume and laser carrier life time design parameters. Results show that IMD impairment mechanism is more dominant than HD mechanism. The spectrum analyser shows distinct difference in the frequency domain display of HD and IMD dominated systems. HD and IMD mechanisms show high sensitivity to carrier amplitude and active layer volume with a positively increasing slope gradient and high sensitivity to bias current and carrier life time with negatively decreasing slope gradients. Also these impairments are moderately sensitive to laser quantum efficiency. The investigations will help in identification of the most influencing operational and design parameters and their suitable values to be used to effectively reduce the influence of the source nonlinearity of SCM links.     

On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission

The goal of this paper is to evaluate the performance of a proposed resource management approach that mitigates the co-channel interference (CCI) in non-orthogonal multiple access (NOMA) scenarios and maintains their enhanced spectral efficiency in distributed massive multiple input multiple output (d-massive MIMO) configurations as well. In this context, dedicated resource scheduling algorithms (RSAs) in power domain (PD) and frequency domain (FD) are studied in terms of resources’ orthogonality. Specifically, in PD case, adjusted power levels (denoted as PD-NOMA) per subcarrier and mobile terminal (MT) are assigned, while in FD case, the subcarriers are either orthogonal (FD orthogonal multiple access, FD-OMA) or non-orthogonal (FD-NOMA) to each other. The response of the d-massive MIMO system is evaluated statistically via independent Monte Carlo (MC) simulations considering a multicellular multi-user network topology and compared to typical multicellular MIMO configurations. In this framework, a simulation platform is implemented that integrates both the PD and FD RSAs. In PD, both intercell co-channel interference (Inter-CCI) and intracell co-channel interference (Intra-CCI) are modelled analytically in order to estimate the assigned power per subcarrier and MT. In the latter case (Intra-CCI), the worst-case scenario is assumed: a subcarrier can be assigned to multiple MTs (full spectral overlapping) leading to intense Intra-CCI. In FD, two subcarrier allocation approaches are considered: Pseudo-Random or maximum signal-to-noise ratio (MSNR). The simulations in both FD implementations (FD-NOMA, FD-OMA) show that, thanks to the proposed PD-NOMA scheme, each MT requires 1/4 of the maximum available power for downlink transmission. Moreover, in any of the investigated NOMA schemes, despite the intense Intra-CCI, roughly the same number of MTs as in the OMA case can access the network. Therefore, it is straightforward that, even in worst-case scenarios, the NOMA RSAs (i) are wisely exploiting the available resources; (ii) can inherently combat intense Intra-CCI and, in this way, maintain the system’s performance (number of MTs, power savings, resilience against CCI, computation complexity). Finally, it is worth noting that in contrary to typical MIMO configurations, the d-massive MIMO architecture alone can lead up to a 13.63% increase in the system’s capacity (10% maximum allowed blocking probability, 1 tier, 1 subcarrier per MT). In this case, the increased spatial separation that is achieved, along with the exploitation of NOMA RSAs, lead to a decreased CCI (both Intra- and Inter-); hence, SNR is improved and consequently the number of accepted MTs as well.     

Optimal power allocation for maximizing the energy efficiency of NOMA enabled full-duplex coordinated direct and relay transmission (CDRT) system with SWIPT

In this paper, we investigate the energy efficiency (EE) performance of non-orthogonal multiple access (NOMA) enabled full-duplex (FD) coordinated direct and relay transmission (CDRT) system (i.e., NOMA-FD-CDRT system). Firstly, we consider a two-user scenario, where the base station (BS) can directly communicate with the near user, while it requires the help of a dedicated FD relay node to communicate with the far user. In the second part, we consider that there are two near users and two far users in the system. To improve the EE, we consider integrating the simultaneous wireless information and power transfer (SWIPT) technique at the FD relay. We formulate an analytical expression for the overall EE of the SWIPT-assisted NOMA-FD-CDRT system. We determine optimal power allocation (OPA) for the downlink users at the BS that maximizes the EE. An iterative algorithm based on Dinkelbach method is proposed to determine the OPA vector. With the help of detailed numerical and simulation investigations, it is demonstrated that the proposed OPA can provide significant enhancement of EE of the considered SWIPT-assisted NOMA-FD-CDRT system.     

Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications

We have studied massive MIMO hybrid beamforming (HBF) for millimeter-wave (mmWave) communications, where the transceivers only have a few radio frequency chain (RFC) numbers compared to the number of antenna elements. We propose a hybrid beamforming design to improve the system’s spectral, hardware, and computational efficiencies, where finding the precoding and combining matrices are formulated as optimization problems with practical constraints. The series of analog phase shifters creates a unit modulus constraint, making this problem non-convex and subsequently incurring unaffordable computational complexity. Advanced deep reinforcement learning techniques effectively handle non-convex problems in many domains; therefore, we have transformed this non-convex hybrid beamforming optimization problem using a reinforcement learning framework. These frameworks are solved using advanced deep reinforcement learning techniques implemented with experience replay schemes to maximize the spectral and learning efficiencies in highly uncertain wireless environments. We developed a twin-delayed deep deterministic (TD3) policy gradient-based hybrid beamforming scheme to overcome Q-learning’s substantial overestimation. We assumed a complete channel state information (CSI) to design our beamformers and then challenged this assumption by proposing a deep reinforcement learning-based channel estimation method. We reduced hybrid beamforming complexity using soft target double deep Q-learning to exploit mmWave channel sparsity. This method allowed us to construct the analog precoder by selecting channel dominant paths. We have demonstrated that the proposed approaches improve the system’s spectral and learning efficiencies compared to prior studies. We have also demonstrated that deep reinforcement learning is a versatile technique that can unleash the power of massive MIMO hybrid beamforming in mmWave systems for next-generation wireless communication.     

Outage performance of CoMP-CNOMA networks with duplex mode selection

This paper investigates the outage performance of a downlink coordinated multipoint cooperative non-orthogonal multiple access (CoMP-CNOMA) network, where two near users cooperatively relay the information to a far user. It is assumed that the near users support both the half-duplex relaying mode and the full-duplex relaying mode. Since the full-duplex mode may be inferior to the half-duplex mode due to the residual self-interference under the full-duplex mode, we propose a duplex mode selection strategy for the near users to dynamically choose the duplex mode for relaying. Under the proposed duplex mode selection strategy, the closed form expressions for the outage probabilities of all the users are derived. The theoretical results match well with the Monte Carlo simulations. It is shown that the proposed duplex mode selection strategy can provide low outage probabilities of all the users simultaneously, while the existing duplex mode selection strategies either achieve higher outage probabilities of all the users or achieve slightly better outage performance of the far user at the sacrifice of much worse outage performance of the near users.     

Digital Self-interference cancellation in the era of machine learning: A comprehensive review

In-band full-duplex (IBFD) transmission has been identified as a promising solution to improve spectral efficiency as compared to half-duplex (HD) systems. One of the major challenges of FD systems is to effectively manage the self-interference (SI) produced by the transmitter antenna to the local receiver antenna. In this survey paper, we first review and discuss the preliminaries related to SI cancellation (SIC). Furthermore, this survey presents the advancements to the date of digital SIC approaches and highlights the advantages and limitations of each approach. The survey also summarizes different hardware platforms used for SIC along with their key features. Additionally, we also survey and compare different FD techniques for their SIC performances. Finally, this paper identifies a variety of new research areas in the era of machine learning and deep learning for digital SIC.     

基于贝叶斯网络的认知引擎设计与重配置

以往的通信行为指导系统未来通信, 以满足用户需求并适应环境变化, 是认知无线电系统的核心所在, 为此提出了一种基于贝叶斯网络的认知引擎, 用于解决在复杂多变的电磁环境与用户需求条件下, 认知无线电系统参数自适应调整的问题. 通过对系统过去通信行为样本数据, 进行结构学习和参数学习建立认知引擎, 将系统当前环境状态和用户需求信息经预处理作为推理的证据, 应用引擎决策出系统此时最佳的工作参数, 完成系统参数重构. 本文利用OPNET工具建立一个移动无线网络完成仿真实验, 仿真结果表明该认知引擎能有效地使移动无线网络适应环境变化, 改善端到端通信性能, 进一步验证了建模方法的可行性.