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To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not be directly transmitted from the track-side BS. In this paper, we propose an UAV-assisted scheduling scheme which utilizes a UAV to serve as relay for such flows. The proposed scheme also utilize two mmWave bands, one for the BS links and the other for the UAV links. The proposed algorithm aims to maximize the number of flows with their QoS requirements satisfied. Simulations demonstrate that the proposed scheme achieves a superior performance on the number of completed flows and the system throughput over two baseline schemes. [ABSTRACT FROM AUTHOR]

This paper presents techniques that are expected to improve the bit error rate (BER) performance of Unmanned Aerial Vehicles (UAVs) communication systems. The decode-and-forward (DF)-based cooperative multi-hop Rician network using space shift keying (SSK) modulation is employed for this purpose. The SSK modulation and cooperative communication are merged and used together. This enhances the BER performance for multiple-input multiple-output (MIMO) channels, thus accentuating its significance as an important technique for consistently reliable communication methodology while, at the same time, eliminating wastage of energy. The new technique is named as high performance (HP) due to not only BER performance improvement but also increase of the diversity of the communication system. This new model is further used for multi-hop networks and the resulting BER characteristics are assessed for possible enhanced performance in Rician channels. The paper also showcases the generalized-n-hop BER equation for various multi-hop DF-based cooperative communication systems in diverse UAV models. The Monte Carlo simulations further confirm the theoretical results that already exist for transmit-receive antennas. This work also underlines the fact that the number of hops in a communication system has no bearing whatsoever on the BER performance diversity value. [ABSTRACT FROM AUTHOR]

A commonly used assumption in the existing unmanned aerial vehicle (UAV) channel models is that the UAV flies along linear trajectories. However, this assumption does not always hold in realistic UAV communication scenarios. In this paper, we relax the linear trajectory restriction and present a temporally non-stationary channel model for UAV-to-ground communication scenario. The temporal non-stationarity of the channel stemming from time-varying heading directions of the UAV is properly modeled. This is achieved by combining a geometry-based stochastic model (GBSM) and an aeronautic random mobility model (RMM), which is introduced to characterize the movement pattern of UAVs. Based on UAV and ground station (GS) trajectories, time-varying model parameters and statistics including space-time-frequency correlation function (STF-CF), Doppler power spectrum density (PSD), delay PSD, and stationary interval are derived. The influences of the RMM-related parameters on the statistics are presented and analyzed. Some of those statistics are verified by measurements, showing the practicability of the model. The work presented in this paper is helpful in designing UAV communication system considering realistic UAV trajectories. [ABSTRACT FROM AUTHOR]

In this paper, we propose a physical layer secret key (PLSK) generation scheme for secure encrypted transmissions in orbital angular momentum (OAM) communication systems which improves the secret key capacity via taking advantage of OAM orthogonal modes. Based on the line-of-sight (LoS) quasi-static channel conditions in OAM systems, we propose a mode-based active defense phase in the PLSK scheme to invalidate keys generated by an eavesdropper when it is extremely close to the legitimate user. Our simulation results highlight that the key information generated by the eavesdropper is almost zero in the case of highly correlated legitimate and eavesdropping channels under LoS conditions. In addition, our proposed OAM PLSK scheme achieves a higher key generation rate (KGR) than existing works since mode-based active defense can disrupt the process of PLSK by the eavesdropper. [ABSTRACT FROM AUTHOR]

An electromagnetic map (EM) can provide movable base stations with information to optimize the communication quality of temporary communication systems in disaster areas, such as areas suffering from earthquakes. In this paper, a low-cost and effective communication optimization system model that uses sensing nodes and EMs is proposed. Moreover, two EM reconstruction methods, which serve as the keys to the model, are proposed to reconstruct EMs to provide information for communication optimization according to different demands. One method is KGA-GAN-SBI, which combines the kriging algorithm, generative adversarial network and split bregman iterations, and the other is APCA-KGA-DPA, which combines the affinity propagation clustering algorithm, kriging algorithm and domain mapping algorithm. The former can satisfy the demand of high-accuracy EMs. The latter can reconstruct an EM faster than KGA-GAN-SBI. The simulation results show that both low-cost methods can reconstruct more accurate EMs than several existing algorithms; they can provide an effective way to optimize the communication quality of temporary communication and fill the gap in the study of EM applications. [ABSTRACT FROM AUTHOR]

With the explosive growth of mobile data, the demand of high-speed railway (HSR) passengers for broadband wireless access services urgently needs the support of ultra-high-speed scenario broadband wireless communication. Millimeter-wave (mmWave) can achieve high data transmission rates, but it is accompanied by high propagation loss and vulnerability to blockage. To address this issue, developments of directional antennas and unmanned aerial vehicles (UAVs) enhance the robustness of the mmWave train-ground communication system. In this paper, we propose a UAV and MRs relay assistance (UMRA) algorithm to effectively overcome link blockage, which can maximize the number of transmission flows on the premise of meeting QoS requirements and channel qualities. First, we formulate a mixed integer nonlinear programming (MINLP) problem for UAV trajectory design and transmission scheduling in the full-duplex (FD) mode. Then, in UMRA, the relay decision algorithm and transmission scheduling algorithm based on graph theory are proposed, which make a good tradeoff between computation complexity and system performance. Extensive simulation results show that a suitable UAV position will greatly improve the performance of the UMRA algorithm and make it close to the optimal solution. Compared with the other two existing benchmark schemes, with the high channel quality requirements and large-area blockage, UMRA can greatly improve the number of completed flows and system throughput. [ABSTRACT FROM AUTHOR]

Vehicular networks which are paralyzed by natural disasters is faced with communication dilemma. Through building a decentralized communication network, unmanned aerial vehicles (UAVs) with high mobility and flexibility are expected to be the solution to the post-disaster vehicular networks. The blockchain technology has been widely used in UAV networks to provision the prompt security of distributed communications. However, existing works ignore the dynamics of the network in that due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blockchain transactions. The delay of synchronization can result in severe security issues. On addressing the issues, this paper proposes UAVs-aided blockchain offline transactions to ensure the security and effectiveness of delay-tolerant blockchain transactions when UAVs are offline. In specific, we consider vehicle-to-vehicle (V2V) charging transactions in post-disaster vehicular networks. By establishing offline channel between charging and discharging electric vehicles (EVs) by hashed time locked contract (HTLC), we design a UAVs aided penalty algorithm with accountable assertions to prevent deposit forging attacks and double-spending attacks. In addition, considering the selfishness of EVs, a Stackelberg game based incentive scheme is developed to encourage EVs to participate the offline transactions and to improve their trust values. By suing the mechanisms above, our proposal addresses the security of offline EV charging, as well as the selfishness of participant. Using extensive simulations, we demonstrate that the proposed scheme can realize secure transactions among EVs and can effectively improve the utilities of EVs through the comparison with conventional schemes. [ABSTRACT FROM AUTHOR]

This paper evaluates the practically achievable best error performance obtained from a dimmable visible light communication (VLC) system that conveys information using binary modulations, such as on-off keying (OOK). As compared to previous studies that address the ideal performance of dimmable VLC systems only over reliable channels with arbitrarily small error probability, this paper presents the best achievable error probability of various configurations with the dimming targets, signal quality, and data rate. The numerical results establish the ultimate decoding error performance targeted for dimmable VLC systems and are expected to provide a practical guideline for the enhanced VLC system design. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is considered as a promising technology, which can effectively control the phase of signals with low-cost reflective elements. In this paper, we investigate the RIS-assisted D2D communication system under Nakagami- $m$ fading. Overlay mode and underlay mode are discussed respectively. To characterize the performance of proposed communication system, we propose novel closed-form analytical expressions for outage probability under two modes, respectively. Based on the analytical results, we derive the diversity order of outage probability. We show that under overlay D2D mode, the diversity order depends on the number of elements and fading parameters. For the underlay D2D case, 0 diversity order is obtained due to the existence of interference. Furthermore, we study the ergodic achievable rate for each mode and the corresponding tight bounds are derived in closed form. High SNR slopes achieved by users under two modes are also discussed. We observe a high SNR slope of order 1 for overlay D2D mode and 0 for underlay D2D mode. In addition, we also investigate the energy efficiency (EE) of the considered system. Numerical results show that the optimal phase shift proposed in our paper enhance system performance effectively comparing to the traditional D2D communication and random phase shift scheme. Significant EE gains can be obtained for low to middle SNR region by passive reflectors with reconfigurable phase. Unlike relay system, the nearer RIS to the D2D transmitter/receiver, the better performance is obtained. [ABSTRACT FROM AUTHOR]

Multi-numerology transmission is a promising multiple access scheme for flexible communication systems, which can simultaneously support users with different service requirements. To this end, both inter- and intra-numerology interference (inter-NI and intra-NI) should be suppressed, which is not straightforward. This paper proposes a novel zero-forcing (ZF) precoding design, which perfectly eliminates all the interference, for massive multiple-input multiple-output (MIMO) systems with multi-numerology. First of all, we express the signal-to-interference-plus-noise ratio (SINR) of the multi-numerology system with spectrum sharing. From the SINR representation, we identify the channels that precoders must nullify to suppress inter-NI. After that, we propose a ZF precoder suppressing both inter-NI and intra-NI perfectly. The lower bound of ergodic achievable rate with perfect channel state information at the transmitter (CSIT) is analyzed. Based on the lower bound of ergodic achievable rate, we present the feasible rate region. Also, we propose two power allocation schemes: user fairness and the ergodic sum rate maximization. The simulation results demonstrate the advantage of the proposed precoding design and verify our analysis results. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a passive antenna array composed of a large number of reflecting elements. In a RIS-assisted communication system, it is a challenging task to acquire accurate channel state information (CSI). In this paper, we propose a semi-blind channel estimation method for a RIS-assisted massive multiple-input single-output (MISO) system. Assuming a Gaussian priori on the data symbols, an iterative expectation maximization (EM)-based algorithm is developed to obtain the maximum likelihood (ML) estimate of the cascaded RIS channel. Different from existing pilot-based methods, the proposed semi-blind channel estimation method exploits the data symbols for channel estimation enhancement and only a fraction of full-pilot signaling overhead is required. Simulation results verify that the proposed method achieves significant improvement in the accuracy of the channel estimation while it noticeably reduces the training pilot overhead. [ABSTRACT FROM AUTHOR]

Machine-type communications (MTC) is a key technology for Internet-of-Things (IoT) services in 5G mobile communications and beyond. An essential design problem for an MTC network is the efficient and scalable data collection from low-power machine-type communication devices (MTCDs). This paper uses unmanned aerial vehicles (UAVs) to facilitate data collection from a clustered MTC network on the ground. The notion of artificial energy map (AEM) is introduced as a novel modeling technique for energy efficiency analysis, which is critical to the subject of investigation here considering the limited energy of battery-powered MTCDs and UAVs. The proposed design framework first determines the number of MTCD clusters $L_\mathrm{{opt}}$ according to a certain criterion. A greedy learning clustering (GLC) algorithm is then employed to divide the MTCDs into $L_\mathrm{{opt}}$ clusters. For each MTCD cluster, an AEM is constructed, and the optimal UAV hovering strategy within the cluster can be obtained accordingly. Finally, the UAV stations travel across the clusters and collect data from each cluster while hovering above it. This AEM-based modeling technique leads to a solution that can effectively improve the energy efficiency (EE) of UAV-enabled data collection. However, the MTCD clustering strategy, UAV hovering strategy, and UAV flying strategy all have impacts on the overall energy efficiency, which results in a coupled optimization problem that is difficult to solve. The GLC-AEM method is proposed to decouple the original EE optimization problem into sub-problems that can be handled easily by standard optimization techniques. Simulation results show that the GLC-AEM algorithm can be applied to UAV-enabled data collection scenarios with single and multiple UAV stations, and it can improve the overall EE effectively. Besides, the GLC-AEM algorithm shows good scalability and consistent performance in clustered MTC networks. The more MTCDs, the higher the achieved EE. [ABSTRACT FROM AUTHOR]

In connected vehicular networks, it is vital to have vehicular nodes that are capable of sensing surrounding environments and exchanging messages with each other for automating and coordinating purposes. Towards this end, integrated sensing and communication (ISAC), combining both sensing and communication systems to jointly utilize their resources and pursue mutual benefits, emerges as a new cost-effective solution. In ISAC, the hardware and spectrum co-sharing leads to a fundamental tradeoff between sensing and communication performance, which is not well understood except for very simple cases with the same sensing and channel states, and perfect channel state information at the receiver (CSIR). In this paper, a general point-to-point ISAC model is proposed to account for the scenarios where the sensing state is different from but correlated with the channel state, and the CSIR is not necessarily perfect. For the model considered, the optimal tradeoff is characterized by a capacity-distortion function that quantifies the best communication rate for a given sensing distortion constraint requirement. An iterative algorithm is proposed to compute such tradeoff, and a few non-trivial examples are constructed to demonstrate the benefits of ISAC as compared to the separation-based approach. [ABSTRACT FROM AUTHOR]

Combining powerful error-correction codes (ECCs) with multipulse pulse-position modulation (MPPM) has great potential to realize high-throughput and high-reliability transmissions in free-space optical (FSO) communication systems. This paper conducts an insightful investigation on protograph low-density parity-check (PLDPC)-coded MPPM systems over Poisson channels. To be specific, we first put forward a novel type of constellations, referred to as irregular pulse-distribution (IPD) constellations, for MPPM. We also conceive a bit-level rate-adaptive (RA) scheme, which can exploit the structural property of the IPD constellations to significantly improve the convergence performance at the price of transmission-rate loss. Furthermore, with the aid of a modified protograph extrinsic information transfer (PEXIT) algorithm, we construct three enhanced PLDPC codes with code rates $1/2, 2/3$ and $3/4$ that exhibit excellent decoding thresholds over Poisson channels. The proposed PLDPC-coded MPPM systems with the use of the IPD constellations, RA scheme, and enhanced PLDPC codes are superior to the state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

This paper studies a secure multiuser multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS), where multiple colluding eavesdroppers (EVEs) coexist. We aim to maximize the sum secrecy rate (SSR) via jointly optimizing the beamforming vectors, the artificial noise (AN) and the phase shifts at the IRS subject to the maximum transmit power constraint and unit modulus constraints. To address the non-convex optimization problem, we first propose an alternating optimization (AO) algorithm based on semidefinite relaxation (SDR) and obtain a high-quality sub-optimal solution. In order to reduce the high computational complexity, a low-complexity alternating optimization (LC-AO) algorithm is developed, in which the beamforming vectors, AN and the IRS phase shifts are optimized alternately by the generalized power iteration (GPI) and the Riemannian manifold conjugate gradient (RMCG) algorithm, respectively. Simulation results show the advantages of deploying the IRS in improving the system secrecy performance. [ABSTRACT FROM AUTHOR]

Recently, research on the design of relay structure and optimal precoding matrix in massive multiple-input multiple-output (MIMO) relay systems has been proposed in many works assuming that partial or all of the channel state information (CSI) is known. However, in the practical communication systems, the CSI is unknown and needs to be estimated. In particular, channel estimation is more difficult when the relay adopts a hybrid precoding structure. In this paper, we propose an efficient channel estimation scheme based on Tucker-2 tensor model for a multi-way massive MIMO hybrid relay system. The proposed algorithm provides all the CSI involved in the communication links at each user end. Compared with traditional channel estimation algorithms, the proposed algorithm has better estimation performance and requires few iterations to achieve convergence. In addition, we derive the analytical expression of the Cramér-Rao lower bound (CRB) for reference. Simulation results verify the effectiveness of the proposed Tucker-2-based channel estimation approach. [ABSTRACT FROM AUTHOR]

Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two core services of the emerging fifth-generation (5G) systems. URLLC services require lower latency and high reliability, whereas eMBB services expect maximum data rates. Since 5G new radio (NR) aims to support heterogeneous services, the 3rd generation partnership project (3GPP) supports a highly flexible frame structure by introducing mixed numerology and mini-slot approaches. In these systems, the time slot is further divided into mini-slots. The coexistence of these two heterogeneous services leads to a challenging resource allocation problem. In this paper, we study the joint resource allocation problem of eMBB and URLLC schedulers to maximize the minimum expected achieved rate (MEAR) of eMBB users while satisfying URLLC users’ quality of service (QoS) constraints. The resource allocation of eMBB traffic occurs at time slot boundaries. In contrast, URLLC traffic is overlapped at the mini-slot timescale by adopting the non-orthogonal multiple access (NOMA) superposition technique. The suitable pairs of eMBB, URLLC users are evaluated using the one-one matching theory by inculcating fairness among eMBB users while satisfying the QoS parameters of URLCC users. The significance of the proposed approach over the baseline approaches is evaluated through numerical simulations in terms of the MEAR, mixed numerologies, and fairness scores among eMBB users. [ABSTRACT FROM AUTHOR]

Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability of improving coverage and communication performance. The development of RIS-assisted communication technology relies on deep understanding of RIS channel characteristics. However, most of the existing investigations focus on the impact of RIS, ignoring the existence of local scatterers in the real propagation environment. The ideal propagation condition limits the accuracy and application of the developed channel model. In this paper, we aim to analyze the channel characteristics of RIS-assisted near-filed communication by considering different conditions of local scatterers. Specifically, we propose a three-dimensional (3D) RIS-assisted multiple-input multiple-output (MIMO) channel model based on a three-dimensional cylinder model, which considers line-of-sight, single-bounced at RIS (SBR) and double-bounced (DB) modes where radio waves reflect from RIS and scatterers near receiver. Based on the proposed channel model, we investigate some key statistical properties for SBR and DB propagation, including the time-frequency-space correlation function and Doppler power spectrum. From the simulation results, it is found that RIS element number, RIS horizontal and vertical locations, distribution of surrounding scatterers, and movement of Rx have significant impacts on channel characteristics. Finally, channel capacities of SBR and DB modes are calculated, where the large size and appropriate locations of RIS significantly improve the channel capacity of RIS-assisted communication. These numerical results and observations can be used for design of RIS-assisted near-field communication systems. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme. [ABSTRACT FROM AUTHOR]

From the fourth generation (4G) of cellular systems onward, wireless personal devices (WPDs) support multi-input multi-output (MIMO) communication. However, the impact of MIMO communication on the electromagnetic field (EMF) of WPD users has yet to be fully understood and analyzed at the system level. In this paper, we first provide a generic model for assessing the individual exposure dose of multi-antenna WPD users in a multi-user multi-carrier communication system. An optimization framework for minimizing this exposure dose is then developed based on our exposure model. This framework helps us to identify a new criterion, i.e., the ratio between the normalized exposure dose and the channel to noise ratio (CNR), as the main system level criterion for minimizing the individual exposure dose of multi-antenna WPD users. This criterion is further integrated in the design of two novel centralized resource allocation schemes that take advantage of the multiple antennas at the WPD to minimize the per-user exposure dose, when full or limited knowledge of each user channel is available. Our new schemes can significantly reduce the individual exposure dose of WPD users (by approximately 80%) in comparison with the most relevant existing resource allocation schemes. Our results also provide insights into the logarithmic relationship between the per-user exposure dose and the number of receive antennas (or the number of time slots), and how such a parameter can be exploited to further reduce the exposure and/or provide a higher SE while maintaining a low exposure dose. [ABSTRACT FROM AUTHOR]

In recent years, the growing development of Connected Autonomous Vehicles (CAV), Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent of Autonomous Intersection Management (AIM) systems. AIMs present a new paradigm for CAV control in future cities, taking control of CAVs in scenarios where cooperation is necessary and allowing safe and efficient traffic flows, eliminating traffic signals. So far, the development of AIM algorithms has been based on basic control algorithms, without the ability to adapt or keep learning new situations. To solve this, in this paper we present a new advanced AIM approach based on end-to-end Multi-Agent Deep Reinforcement Learning (MADRL) and trained using Curriculum through Self-Play, called advanced Reinforced AIM (adv.RAIM). adv.RAIM enables the control of CAVs at intersections in a collaborative way, autonomously learning complex real-life traffic dynamics. In addition, adv.RAIM provides a new way to build smarter AIMs capable of proactively controlling CAVs in other highly complex scenarios. Results show remarkable improvements when compared to traffic light control techniques (reducing travel time by 59% or reducing time lost due to congestion by 95%), as well as outperforming other recently proposed AIMs (reducing waiting time by 56%), highlighting the advantages of using MADRL. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) is being envisioned as an enabling technology for the physical layer of the sixth generation (6G) of communication networks which promises to support the massive connectivity requirements of the Internet of things (IoT). Due to its ability to safely function in areas that are otherwise susceptible to electromagnetic interference (e.g., aircraft, hospitals, etc.), VLC is a popular candidate for intravehicular communication systems in flights, ambulances, etc. However, the achievable data rate in VLC systems is severely limited by the low modulation bandwidth of the light sources. This limitation is ameliorated by using non-orthogonal multiple access (NOMA) to multiplex users in the power domain. In this paper, we propose to enhance the fairness among users in terms of their error performance and the achievable data rate in a NOMA-VLC system. Based on numerical and simulation results, we show that the fair power allocation (FPA) scheme not only provides significantly higher fairness but it also entails better error performance, improved energy efficiency, and enhanced system throughput as compared to the existing power allocation schemes. We also demonstrate that the FPA scheme exhibits superior performance even when the system parameters, like the number of users, transmitter semi-angle, and detector field-of-view, are changed. We conclude that the FPA scheme is a promising technique for the efficient implementation of NOMA in 6G-VLC broadcasting systems. [ABSTRACT FROM AUTHOR]

The internet of vehicles needs mobile communication systems for efficient communications. On the physical downlink control channel (PDCCH) of 5G, blind detection is performed to identify the downlink control information (DCI). Due to the uncertainty of DCI formats and PDCCH formats, the existing two-stage blind detection schemes separately decode every PDCCH code block under different DCI formats, leading to a high decoding complexity in the first stage. To reduce the detection complexity, this paper proposes an efficient DCI detection method, where a significance-test strategy is designed to detect the DCI formats. The significance-test strategy can forecast the DCI lengths without separately decoding the whole codeword, thus reducing the decoding complexity by several times. Moreover, the accuracy of the DCI detection is analyzed, with which the test scope of the test statistic is optimized for minimizing the decoding complexity on condition of meeting the detection accuracy requirements. Simulation results show that the proposed method significantly reduces the decoding complexity relatively to the existing two-stage detection schemes. [ABSTRACT FROM AUTHOR]

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

From adapting to the propagation environment to reconstructing the propagation environment, reconfigurable intelligent surface (RIS) changes the design paradigm of wireless communications. To reconstruct the propagation environment, joint beamforming of RIS and multi-input multi-output (MIMO) is crucial. Unfortunately, due to the coupling effect of the active beamforming of MIMO and passive beamforming of RIS, it is difficult to find the optimal solution to the joint beamforming problem, so a serious performance loss will be caused. In this paper, inspired by the end-to-end (E2E) learning of communication system, we propose the E2E learning based RIS-aided communication system to mitigate the performance loss via deep learning techniques. The key idea is to simultaneously optimize the signal processing functions at base station (BS), RIS, and user, including active beamforming for BS and user, passive beamforming for RIS. This way is able to avoid the performance loss caused by alternately optimizing each function of the RIS-aided system. Specifically, we firstly utilize a deep neural network (DNN) to realize the modulation and beamforming for BS and utilize another DNN to realize the demodulation and combining for user. Then, the RIS passive beamforming is also represented by trainable parameters, which could be simultaneously optimized with the DNNs at BS and user. Simulation results show that the proposed E2E learning based RIS-aided communication system could achieve the better bit error rate (BER) performance than traditional RIS-aided communication systems. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) which enables communication, computation and storage capabilities at the network edge, is an emerging paradigm for 5G and beyond 5G networks. Resource-limited mobile devices can offload their tasks to the MEC server to reduce computation latency and save energy consumption. However, limited wireless spectrum resources make it hard for MEC to guarantee low latency when a large number of users offload the computation tasks or download the computed results. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band is a promising solution to the shortage of spectrum resources. In this paper, we study a multi-user FD-MEC system involving all the phases of uploading, computing, and downloading. With the help of an FD-capable base station (BS), two half-duplex (HD) users can form an FD pair and share the same time-frequency resource for task uploading and result downloading. We formulate a latency minimization problem considering power control, time scheduling, offloading decision, and user pairing strategy, which is a mixed-integer non-linear programming (MINLP) problem. The problem can be decomposed into two subproblems. The first subproblem is delay minimization for each possible FD pair, and the second one is to decide the offloading and user pairing strategy, and the user scheduling order. We analyze the problem in two cases: (1) the computing delay at the edge server is negligible and (2) the computing delay at the edge server is non-negligible. For both cases, the first subproblem is solved using a bisection algorithm. Then, we solve the second subproblem in the two cases by a relaxing-based algorithm and a concave-convex procedure (CCCP)-based algorithm, respectively. Finally, numerical results demonstrate that with our proposed strategies, the overall latency can be significantly reduced by integrating FD communication with the MEC system. [ABSTRACT FROM AUTHOR]

In cellular communication systems, it is conventional to assume the absence of a line of sight (LoS) path between the users and theirassociated access points (APs). This assumption however becomes questionable in the context of recent developments in the direction of cell-free (CF) massive MIMO systems. In the CF massive MIMO, the AP density is assumed to be comparable with the user density, which increases probability of existence of an LoS path between the users and their associated APs. In this paper, we analyze the performance of an uplink CF massive MIMO system, with a probabilistic LoS channel model. Here, we first derive the effective statistics of this channel model, and argue that their behaviour is fundamentally different from that of the conventional rich scattering channels. Utilizing these statistics, we next compare the rates achievable by CF massive MIMO systems, under both stream-wise and joint decoding at the central processing unit. Following this, we also discuss the centralized MMSE based data detection to obtain a complexity/ performance trade-off. Finally, using detailed Monte-Carlo simulations, we validate our analytical results, and evaluate the performance of the three data detection schemes. [ABSTRACT FROM AUTHOR]

Security is a crucial feature of current (5G) and even more for future (6G) communication networks. While the innate nature of the wireless channel is an issue for guaranteeing security, its physical characteristics can be exploited for protecting confidential data. Physical-layer Security (PLS) mechanisms take advantage of unique features of the transmission media or of the user equipment to introduce an additional level of data protection. The physical-layer security (PLS) can be seen as the first level of defense. The noise-loop (NL) modulation is a PLS technique that intrinsically provides confidentiality. In this paper, we first provide the theoretical analysis to evaluate the reliability and security in multipath fading channels, and then the theoretical results are evaluated by experimentation. A Software Defined Radio (SDR) testbed that exploits the NL secure modulation was implemented and a measurement campaign has been carried out in an indoor environment, with passive and active malicious nodes. An evaluation of the complexity of the proposed system is also reported, showing that the NL has low hardware resource occupancy and low computational cost. These fundamental characteristics make the NL modulation a valid PLS technique for providing a security solution for beyond 5G and 6G networks. [ABSTRACT FROM AUTHOR]

Inspired by the promising potentialof reconfigurable intelligent surface (RIS)-based transmission, this paper investigates the RIS-aided vehicle-to-everything (V2X) communication system with the social trust, where multiple vehicle-to-infrastructure (V2I) links share the spectrum with vehicle-to-vehicle (V2V) links. Subject to the reliability constraint of V2V links, the sum V2I link capacity is maximized, through jointly optimizing the power allocation, RIS reflection coefficients and spectrum allocation. The formulated mix-integer and non-convex problem is effectively converted into three subproblems, which can be iteratively solved by our proposed three-stage joint resource allocation and RIS optimization (JRARO) algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm, and that the RIS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity. [ABSTRACT FROM AUTHOR]

Automatic modulation classification (AMC) plays a vital role in cognitive radio to improve spectrum utilization efficiency, however, most of the existing works have focused on single-carrier communications in single-input single-output systems. In this paper, we propose an efficient AMC method for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems with the assumption of unknown frequency-selective fading channels and signal-to-noise ratio. At the receiver, the complex envelope samples of a burst signal acquired by multiple antennas are decomposed into in-phase and quadrature samples, which are then structured into a high-dimensional data array. To learn the modulation patterns from received signals, we develop a deep network, namely three-dimensional MIMO-OFDM convolutional neural network (MONet). With cuboidal convolution filters, the proposed MONet allows the network to capture underlying features as intra- and inter-antenna correlations at multi-scale signal representations. Relying on simulations, MONet achieves the classification accuracy of over 95% at 0 dB SNR under various channel impairments and shows the robustness with different MIMO antenna configurations. [ABSTRACT FROM AUTHOR]

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway. [ABSTRACT FROM AUTHOR]

The performance of train communication network (TCN) is one of the most important factors to ensure the safety of train operation, and there is a lack of research for the TCN performance currently. To master the state of TCN, a combined method to evaluate and predict the TCN performance is proposed in the paper. First, we select appropriate evaluation indexes and determine their comprehensive weights. Then, the evaluation model of TCN performance is established based on a multidimensional normal cloud model combined with weights. According to the evaluation results, the artificial neural network (ANN) is used to predict the TCN performance to realize the real-time evaluation. Finally, the effectiveness of the evaluation and prediction model of TCN performance is verified by the simulation result, which is obtained from the simulation platform of TCN. The results show that service quality for TCN has been effectively improved, which provides a theoretical reference for evaluation and prediction of TCN performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed joint sensing and communication (DJSC) system in which each radar sensor as a JSC node is equipped with a sensing function and a communication function. Deploying multiple JSC nodes may require a large amount of bandwidth. Therefore, we investigate the bandwidth allocation problem for the DJSC system. In particular, we aim to optimize the bandwidth allocation to the sensing function and the communication function of the JSC nodes. To improve the allocation efficiency while benefiting the spatial diversity advantage of the DJSC systems, the objective is to maximize the sum of sensing performances, i.e., estimation rates, communication performances, i.e., communication data rates, and fairness of all the users. The optimization problem is non-convex and difficult to be solved. For this, we propose a fully polynomial time approximation algorithm, and we prove that the approximation algorithm can guarantee a near-optimal solution with an accuracy bound of $\epsilon$. Furthermore, we propose to use a heuristic algorithm with lower complexity. The simulation results show that both the proposed algorithms are able to achieve the solutions close to the optimum in a computationally efficient fashion. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) communication system using hybrid precoding of subarray-connected structure at the base station. To maximize the received signal power of the user, we jointly optimize the active beamforming at the BS and passive beamforming at the IRS, respectively. Exploiting the rank-one property of mmWave channels, we transform the original problem into an approximate optimization problem and give the closed-form solution of the approximated problem. Simulation results show that our proposed scheme can effectively improve the received signal quality of the user with a high energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we investigate device-to-device (D2D) communication networks which are one of the key technologies for next-generation mobile communication networks and many other applications such as unmanned aerial vehicles (UAVs), vehicle-to-vehicle (V2V), and Internet of things (IoT). The overlay D2D communication networks that are considered in our study use dedicated radio resources separate from what cellular networks use and there exists co-channel interference in D2D networks without cross-channel interference between two networks. We propose a new transmission scheme for overlay D2D networks that uses a dueling deep reinforcement learning (DRL) architecture. The DRL is especially effective in environments where actions do not affect subsequent states as in wireless communication networks. The main contribution of this paper is that the proposed architecture is designed to utilize only information that each D2D devices can easily obtain by measuring channels. The proposed scheme thus enables D2D devices to train their own neural networks and to decide autonomously whether to transmit data without any intervention from infrastructures. The performance of the proposed scheme is analyzed in terms of average sum-rates and is compared to three baseline schemes. Simulation results show that the proposed scheme can achieve almost optimal sum-rates with low signal-to-noise (SNR) values without any intervention from infrastructure. [ABSTRACT FROM AUTHOR]

The performance evaluation of wireless systems is crucial for the development of future systems with more connected devices. It is essential to have an easy and relevant method for ensuring the wireless communication performance of the devices. We have recently developed a new random line-of-sight (random-LOS) measurement system for evaluating the communication performance of wireless devices, e.g., transceivers and antennas installed on a vehicle. In the measurement system, a plane wave is generated in the test zone emulating the far-field wave transmitted from a radio base station. In this paper we present both numerical simulations and actual experimental results of the random-LOS over-the-air (OTA) measurement setup operating in the 1.5–3 GHz band. The measurement accuracy is determined by the field variations within the test zone, where a smaller variation gives better measurement accuracy. In this paper the achieved accuracy expressed in terms of standard deviation (STD) was evaluated to be approximately 1 dB of the power within a cylindrical test zone of height 0.4 m and diameter 2 m. The active multiple-input multiple-output (MIMO) performance of antenna systems installed on an actual vehicle was measured and evaluated using the presented setup. A comparison to a theoretical zero forcing (ZF) receiver is also presented. [ABSTRACT FROM AUTHOR]

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR. [ABSTRACT FROM AUTHOR]

The rapid development of maritime activities has led to the emergence of more and more computation-intensive applications. In order to meet the huge demand for wireless communications in maritime environment, mobile edge computing (MEC) is considered as an effective solution to provide powerful computing capabilities for maritime terminals of resource scarcity or latency sensitive. A basic technology to implement MEC is virtual machine (VM) multiplexing, through which multi-task parallel computing on a server is realized. In this paper, a two-layer unmanned aerial vehicles (UAVs) maritime communication network with a centralized top-UAV (T-UAV) and a group of distributed bottom-UAVs (B-UAVs) is established and MEC is used on T-UAV. We aim to solve the latency minimization problem for both communication and computation in this maritime UAV swarm mobile edge computing network. We reformulate this problem into a Markov decision process (MDP), since it is a non-convex and multiply constrained but has the characteristics of MDP. Based on this MDP model, we take deep reinforcement learning (DRL) as our tool to propose a deep Q-network (DQN) and a deep deterministic policy gradient (DDPG) algorithms to optimize the trajectory of T-UAV and configuration of virtual machines (VMs). Using these two proposed algorithms, we can minimize the system latency. Simulation results show that the given solutions are valid and effective. [ABSTRACT FROM AUTHOR]

Satellite and unmanned aerial vehicle (UAV) networks have been introduced as enhanced approaches to provide dynamic control, massive connections and global coverage for future wireless communication systems. This paper considers a coordinated satellite-UAV communication system, where the UAV performs the environmental reconnaissance task with the assistance of satellites in a hostile jamming environment. To fulfill this task, the UAV needs to realize autonomous trajectory control and upload the collected data to the satellite. With the aid of the uploading data, the satellite builds the environment situation map integrating the beam quality, jamming status, and traffic distribution. Accordingly, we propose a closed-loop anti-jamming dynamic trajectory optimization approach, which is divided into three stages. Firstly, a coarse trajectory planning is made according to the limited prior information and preset points. Secondly, the flight control between two adjacent preset points is formulated as a Markov decision process, and reinforcement learning (RL) based automatic flying control algorithms are proposed to explore the unknown hostile environment and realize autonomous and precise trajectory control. Thirdly, based on the collected data during the UAV’s flight, the satellite utilizes an environment situation estimating algorithm to build an environment situation map, which is used to reselect the preset points for the first stage and provide better initialization for the RL process in the second stage. Simulation results verify the validity and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

M2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. Usually, the traffic generated by M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements. The M2M traffic generally flows in the uplink direction. It imposes several challenges in designing scheduling for uplink in the LTE network. The research community proposed various solutions regarding the QoS handling in M2M communications. Allocating the resources based on the QoS of machine type communications (MTCs) has a challenge in deciding on devices’ priority. This work applies game theory to control devices’ strategic behavior claiming a false priority. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. The packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the devices’ strategic behavior. Simulation is performed in MATLAB R2019b. The performance of the proposed schemes is evaluated in terms of the total system utility in QoS satisfaction against standard schedulers like Round Robin and Proportional Fair schedulers and some other schedulers proposed in the literature. [ABSTRACT FROM AUTHOR]

As an important technique of enabling global access for 6G, low earth orbit satellite (LEO-Sat) communication is still facing the challenges of large path-loss, and severe Doppler effect. This paper studies the reliability performance of a downlink LEO-Sat communication system, where the orthogonal time frequency space (OTFS) scheme is employed to combat severe Doppler effect. Further, the unmanned aerial vehicle (UAV) based cooperative transmission is developed, in order to compensate for the large margin of path-loss caused by the long transmission distance. In particular, the closed-form expression for the outage probability of the OTFS based LEO-Sat transmission is derived, where the novel moment matching approach is used to closely approximate the PDF of a sum of shadowing Rician (SR) variables. Further, the condition of using UAV cooperation is obtained so that the positive reliability gain is guaranteed. Finally, both the theoretical analysis and simulation results show that, the OTFS scheme can significantly outperform the traditional OFDM scheme, while demanding to carefully address the trade-off between reliability improvement and implementation complexity determined by modulation size. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV’s maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach. [ABSTRACT FROM AUTHOR]

A hybrid satellite-terrestrial relay network (HSTRN) has been envisioned as a promising communication architecture for future wireless communications, which can prominently reduce the impact of shadow fading and expand service coverage. In this paper, we conduct theoretical performance analysis for a dual-hop communication system, consisting of multiple relays and multiple users in the HSTRN with the amplify-and-forward protocol. For the shadowing effect and the multipath effect, we introduce the shadowed-Rician distribution and Nakagami- $m$ fading to characterize the channel models for the satellite-relay and the relay-user links, respectively. Additionally, the opportunistic scheduling scheme is employed, where the optimal relay and the optimal user are selected from the alternative nodes with the maximum instantaneous signal-to-noise ratio. Thereafter, the analytical expressions including ergodic capacity and average symbol error rate (SER) are derived. More specifically, we also present the asymptotic behavior of the average SER at the high signal-to-noise ratio (SRN) regime. The theoretical analysis is validated by the numerical results. Moreover, the results also reveal that the performance of the system can be improved by increasing the number of relays and users under different channel parameters and various modulation schemes. [ABSTRACT FROM AUTHOR]

In mobile communication networks, when a user roams to and accesses a foreign network, the foreign operator needs to request the user’s subscription data from a centralized authentication server, which is managed by the user’s home operator. However, centralized authentication introduces single point of failure. Meanwhile, the real-time participation of the home operator and the trust relationship between the foreign operator and the home operator are difficult to guarantee. In this paper, by adopting blockchain and smart contracts, we propose a secure and efficient access control scheme of user subscription data in roaming scenarios. We further design a flexible user authentication scheme, which utilizes derivable tokens based on the proposed access control scheme. By using blockchain to store and manage user subscription data, access control can be decentralized without any trusted third party. Besides, by implementing automatic verification of access privilege through smart contracts, the limitation of the home operators’ real-time participation is eliminated. In addition, to further improve security and reduce the on-chain storage overhead, we optimize the data encryption and storage scheme utilizing threshold secret sharing. Our security and performance analysis show that the proposed user subscription data access control scheme for roaming service provides high-level security while causing acceptable time and storage overhead. [ABSTRACT FROM AUTHOR]

Explorations on the Lunar Far-side Surface (LFS) have recently developed rapidly with various landers, cruisers, and orbital space vehicles. Research on relay satellites with Earth-Moon Libration Point (EMLP) orbits has aroused widespread interest in supporting these probe missions, i.e., “Queqiao” spacecraft, due to their particular relative positions in the Earth-Lunar system. For example, a Halo orbital relay satellite at the Earth-Moon L2 point can provide more transmission opportunities for communication nodes on LFS to the destination nodes on the Earth. However, a single Halo orbital spacecraft could not provide all-time connections for the cislunar communication with its limited coverage on the LFS. In this paper, we proposed a dual-relay Halo orbit constellation for the cislunar communication network, providing high-proportion coverage on the LFS at any time using an analytical method of the grid point. Based on the proposed constellation, we designed an energy efficiency-optimal relay selection algorithm for the nodes on the LFS to return data back to the Earth by solving a MIP (Mixed-Integer-Programming) problem with collaborative considerations of source and relay node’s power allocation. The simulation results demonstrate that the proposed algorithm has the maximum energy efficiency (EE) while ensuring the user’s quality of service, compared with the algorithm of Spectrum Efficiency Maximization (SEM). [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is deemed a promising technique for future wireless and mobile technologies due to its ability to reconfigure the radio propagation environment. In this paper, we analyze the physical layer security (PLS) of a downlink non-orthogonal multiple access (NOMA) network through an IRS intended for assisting the wireless transmission, taking into account the external and internal eavesdropping scenarios. Considering the hardware limitations, a 1-bit coding scheme is utilized to investigate the secrecy performance of the IRS-aided NOMA (IRS-NOMA) network. Exact and asymptotic expressions in terms of the secrecy outage probability (SOP) and the effective secrecy throughput (EST) are presented, which are in consideration of the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC). Based on the analytical results, the secrecy diversity orders are related to the number of reflecting elements and EST ceilings are observed in the high SNR regime. Monte Carlo simulations are presented to corroborate the correctness of the analyses and illustrate that the IRS-NOMA scheme is superior in the secrecy outage performance to the IRS-aided orthogonal multiple access scheme. In addition, the SOP performance enhancement through the increased number of reflecting elements and the improvement of EST through proper target secrecy rate are also evidenced by the simulations. [ABSTRACT FROM AUTHOR]

In this paper, an aerial heterogeneous wireless network consists of a high altitude platform (HAP) and low altitude platforms (LAPs) as aerial base stations (ABSs) in a downlink orthogonal division multiple access (OFDMA) transmission is investigated. Two types of spectrum sharing, including spectrum underlay access (SUA) and spectrum overlay access (SOA) are employed to evaluate the inter-tier interference. Distributed problem formulation and solving method are used to reduce the computational complexity, communication overhead and system latency. Subcarrier assignment and power allocation in spectral efficiency (SE), energy efficiency (EE) and transmission power optimization problems are accomplished by the proposed two-layer algorithm. In the proposed algorithm, first, subcarrier assignment based on constraints of the optimization problems is carried out to cover both SUA and SOA with low computational complexity. Second, the proposed algorithm performs power allocation. The simulation results show the relation between spectrum reuse and inter-tier interference, such that system SE, EE and the number of served users are maximized when both spectrum reuse and inter-tier interference management are performed together. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) composed of unit cells with tunable reflection coefficients, amplitude, phase, and frequency has received great interest from wireless communication communities. In this paper, a non-stationary three-dimensional (3-D) wideband IRS-assisted channel model, including spatial and temporal non-stationarity, is proposed for high altitude platform (HAP) multiple-input multiple-output (MIMO) communication systems. The proposed channel model considers the propagation environment around the receiver (Rx) and that around both the IRS and Rx, which are modeled using multiple 3-D cylinders and confocal elliptic-cylinders, respectively. In the proposed model, the optimal reflection phases of the IRS reflection units are proposed, and the number and the size of the IRS reflection units are also considered, which are used to investigate received signal strength, multipath fading effect, and spatial non-stationarity of IRS-aided communication systems. The numerical results show that the IRS reflection units can improve the received signal strength by adjusting the reflection phases and multipath fading effect can be effectively reduced by increasing the number and the size of IRS reflection units. Also, it is found that the spatial correlation significantly decreases with the increase of the number and size of the IRS reflection units, and the proposed IRS-aided channel model presents spatial non-stationarity. These observations can provide guidelines and references for systems design and performance analysis of IRS-aided HAP-MIMO communication systems. [ABSTRACT FROM AUTHOR]

In this paper, we consider the orthogonal frequency division modulation (OFDM) based integrated radar-communication (Rad-Com) for automobile systems operating in millimeter wave band with imperfect time-frequency synchronization for communications. Since different pilot power allocation and placement strategies can result in different performance, we investigate the power allocation and placement design for the Rad-Com system. We analyze the effect of power allocation and pilot placement on the system performance. Based on the analysis, we provide, along with a channel estimation scheme, the pilot power allocation and placement designs that can optimize the radar and communication performance, respectively. Moreover, the tradeoff between radar and communication performance is discussed and a tradeoff design is presented. We conduct simulations to validate our analysis and evaluate the proposed designs. Results show that our analysis is valid and insightful and the proposed designs are effective. [ABSTRACT FROM AUTHOR]