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The aspects of sum rate and fairness for dirty paper coding (DPC) based MIMO downlink communications are investigated in this paper. We first apply the $\ell _{1}$ -norm fairness measure to formulate the problem of fairness maximization for a given sum rate as an optimization problem. The problem is unfortunately nonconvex and cannot be efficiently solved. To overcome the difficulty, we invoke the uplink-downlink duality to transform the problem back and forth between uplink and downlink communications. An efficient, iterative waterfilling based algorithm is then proposed to yield achievable rates with the best possible fairness values. Simulation results show that the proposed approach offers an enormous gain in the achievable sum rates for a wide range of fairness values, when compared to the popular successive zero-forcing DPC-based and block diagonalization based coding schemes. [ABSTRACT FROM AUTHOR]

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework. [ABSTRACT FROM AUTHOR]

In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

Fixed Satellite Services (FSS) used to be alone at the C-Band spectrum in most countries. Since the deployment of 5G in many countries (i.e., 3.3–3.6 GHz), FSS is not the exclusive system in the C-Band anymore. In order to minimize the detrimental interference for the FSS to allowable levels, regional exclusion zones of maximum radiated power in 5G base stations (BS) are proposed and evaluated. In this paper, a measurement campaign has been carried out, and an analysis of the interference has been studied. A filtering model, namely Filter to Remove Broadband Interference 5G (FIREBRING), is proposed and analyzed concerning the carrier-to-noise ratio (C/N). Moreover, this paper focuses on the evaluation of the 5G interference into the FSS. The proposed solution deployed an Low-Noise Block (LNB) with a band frequency of 3.7 to 4.2 GHz to test the satellite down-conversion signal at the receiver. The paper offered a complete analysis of the 5G signal, taking into account the implications of out-of-band emissions, potentially LNB saturation into FSS receiver, and the repercussions of the deployment of the 5G BS active antenna systems. With the LNB and down-converter in place, it can be found that the signal interference between 1.450 GHz and 1.550 GHz, is nearly 18 dB. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community. [ABSTRACT FROM AUTHOR]

This paper considers the $\mathscr{L}_{2}$ string stability analysis and controller synthesis for vehicle platooning under jamming attacks and interferences from other communication channels. Jamming attacks and interferences for vehicle platooning systems are modeled as periodic denial of service attacks. Further, the vehicle platooning with denial of service attacks is modeled as a distributed switching system where an average dwell time approach is adopted for the stability analysis. Under the distributed switching system framework, this paper presents a co-design paradigm for the vehicle platooning. The distributed event-triggered method (ETM) and controllers are presented to guarantee $\mathscr{L}_{2}$ string stability, which can ensure that the output cannot be amplified along the vehicle platooning. Sufficient conditions for $\mathscr{L}_{2}$ string stability are then provided to guide the co-design of the ETM and controllers, which can be efficiently obtained by solving LMIs. Simulation results are presented to demonstrate the efficacy of the proposed co-design approach in addressing the denial of service attacks for vehicle platooning systems. [ABSTRACT FROM AUTHOR]

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quantization of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta-based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously sensing resources, probabilistically reselecting resources, and periodically reserving resources. However, vehicles randomly select resources from the available resource lists in the resource reselection process, resulting in frequent packet collisions especially when radio resources are insufficient. Unlike the traditional SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources with the aim of reducing packet collisions. Furthermore, a multi-head attention mechanism is adopted to improve the training efficiency by helping vehicles selectively pay attention to the observations and actions of neighbouring vehicles. It is worth noting that the RL-SPS algorithm fits the characteristics of Mode 4, which selects resources without requiring any global information. Simulation results show that RL-SPS outperforms other decentralized approaches and demonstrate the scalability and robustness of RL-SPS in a dynamic vehicular network. [ABSTRACT FROM AUTHOR]

The air-ground cooperative emergency networks can assist with the rapid reconstruction of communication in the disaster area, where unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are deployed as base stations. The trajectory optimization of emergency base stations is of vital importance to the communication performance, which is related to the timeliness and effectiveness of rescue. In this paper, federated multi-agent deep deterministic policy gradient (F-MADDPG) based trajectory optimization algorithm is proposed to maximize the average spectrum efficiency. Specifically, the property of MADDPG is inherited to jointly control of multiple vehicles and federated averaging (FA) is utilized to eliminate the isolation of data to accelerate the convergence. Distributed F-MADDPG (DF-MADDPG) is further designed to reduce the communication overhead with a distributed architecture. The simulation results indicate that the proposed F-MADDPG and DF-MADDPG based algorithms significantly outperform the existing trajectory optimization algorithms, in terms of the average spectrum efficiency and the speed of convergence. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility and scope of co-channel coexistence between a low data rate Orthogonal Frequency Division Multiplexing (OFDM) secondary network and a digital television (DTV) broadcast network operating in the same area. Since DTV receivers do not transmit, neither the presence nor the impact of interference from the secondary network on these DTV receivers can be known. Consequently, conventional spectrum sensing techniques cannot be employed effectively. This paper proposes to compensate the lack of feedback by combining the available information about population distribution in the region - ranging from coarse population density estimates to accurate geospatial maps - with better estimation of interference using a data-driven technique called Ordinary Kriging. Radio Environment Maps constructed using the Kriging method from field-reported observations from the secondary nodes are used to obtain better accuracy than the conventional statistical models. The estimates are then used to compute communication parameters for each secondary link in terms of power, number of active subcarriers, and active time slots, while keeping the average interference in the DTV network within threshold bounds. Thus, the viability of such co-channel license-exempt operation of secondary networks in the DTV black and grey spaces for low data rate applications in densely populated urban areas and higher data rate capabilities for sparsely populated rural areas is established. [ABSTRACT FROM AUTHOR]

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS. [ABSTRACT FROM AUTHOR]

The stochastic properties of vehicles’ arrival process and the high-speed mobility of vehicles lead to dynamic changes in the topology of vehicular networks, affecting the data download experience of users, especially for large files. In this paper, a novel elastic-segment-based vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) cooperative strategy (ESCS) is proposed to reduce the impact of the density of vehicles on achievable throughput. The achievable throughput and optimal parameters of the proposed system are analyzed. The strategy takes the impact of the density of vehicles on the data download process as the key element for consideration. The density of vehicles has a great influence on the distance between vehicles, and further affects the transmission data rate. To solve the problem of low transmission data rate induced by the density of vehicles, the sizes of the elastic-segments are optimized according to the distance between the vehicles. Owing to the stochastic process of the distance between vehicles, it is particularly challenging to derive a closed-form expression for the achievable throughput. To tackle this problem, we develop a new V2I/V2V cooperative model to adapt to the general two-way lanes, analyze the distribution and mobility of vehicles, and derive a closed-form expression for the achievable throughput, as a function of V2I effective transmission range, the V2V effective transmission range, and other system parameters. We then optimize V2V effective connection range and the vehicle density to maximize the achievable throughput. Simulation results show that the proposed strategy significantly improves the throughput compared with the existing cooperative strategy, especially in the scenario of high vehicle density. [ABSTRACT FROM AUTHOR]

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

In this study, we investigate the operation of a user-number threshold-based base station (BS) on/off control, in which the BS turns off when the number of users is less than a specific threshold value. This paper presents a space-based analysis of the BS on/off control system to which a stochastic geometric approach is applied. In particular, we derive the approximated closed-form expression of the coverage probability of a homogeneous network (HomNet) with the user-number threshold-based BS on/off control. Moreover, the optimal user-number threshold for maximizing coverage probability is analytically derived. In addition to HomNet, we also derive the approximated closed-form expression of the coverage probability and the optimal user-number thresholds for a heterogeneous network (HetNet). The results show that HetNet, the analysis of which seems intractable, can be analyzed in the form of a linear combination of HomNets with weighted densities. In addition, the optimal user-number threshold of each tier is obtained independently from other tiers. The modeling and analysis presented in this paper are not only limited to the case of user-number threshold-based on/off control, but also applicable to other novel on/off controls with minor modifications. Finally, by comparing the analytical results with the simulated results, the theoretical contributions of this study are validated. [ABSTRACT FROM AUTHOR]

The Internet of Underwater Things (IoUT) gradually becomes the future direction of ocean. Many devices, which are called the sources, are deployed in the sea to fulfill scientific, civilian, and military needs. If the locations of these sources are leaked, it will cause a serious economic and military crisis. Thus, in this context, the source location privacy protection becomes critical. In this paper, we focus on the source location privacy protection of underwater acoustic sensor networks (UASNs), where a multi-channel interference based source location privacy protection scheme (MCISLP) is proposed. The MCISLP mainly contains four steps. First, to enhance the sense of autonomous underwater vehicles (AUV) for nodes, a noise reduction method is proposed. Then, based on the previous step, an AUV movement prediction method for nodes is presented to ensure the success of packet transmission. After that, for the information exchange among nodes, given the interference of multiple underwater channels, this interference is then utilized to protect the source location privacy. Further, to reduce the channel interference on the source packet, the time slot control is added to weaken the influence. Last, the ant colony algorithm is adopted for the data collection of AUV. The simulation results show that the MCISLP can increase the network safety time by up to 57 $\%$ (compared with data collection algorithms), and balance the energy consumption of nodes (for about 20 $\%$ energy saving), with a minor compromise of energy consumption of AUV and delay. [ABSTRACT FROM AUTHOR]

Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments. [ABSTRACT FROM AUTHOR]

In this paper, we investigate joint retrospective interference alignment (RIA) and caching schemes for multiple-input single-output (MISO) broadcast channels (BC) with delayed channel state information at the transmitter (CSIT). By exploiting the cached content, we propose one sophisticated cache-aided RIA scheme to cancel the interference. Importantly, our scheme takes the number of symbols in a packet into account, as different number of symbols in a packet result in different system configuration, which may have significant impact on the design of the transmission scheme and achievable degrees of freedom (DoF). Furthermore, we present two theorems to quantify the achievable sum DoF of the proposed cached-aided RIA scheme for different system configuration. Moreover, the system configuration of the existing cache-aided RIA scheme with mixed CSIT can be treated as a special case in our paper. And also our scheme can simultaneously support more users than the existing cache-aided RIA scheme with mixed CSIT to invoke zero forcing (ZF) precoding, which limits the number of users simultaneously supported. [ABSTRACT FROM AUTHOR]

In this paper, we study the three-dimensional (3D) path planning problem for cellular-connected unmanned aerial vehicle (UAV) taking into account the impact of 3D antenna radiation patterns. The cellular-connected UAV has a mission to travel from an initial location to a destination. In this process, there is a trade-off between the flight time and the expected communication outages duration, which requires planning a suitable route to avoid the weak coverage region. However, as the existing cellular networks are primarily designed for ground coverage, the communication coverage in the sky tends to be intermittent and irregular due to the potential for severe interference and obstructions (such by buildings), which brings great challenges to 3D path planning. To address this challenge, we first construct a 3D coverage map that stores the expected outage probability over each locations. We then propose a multi-step dueling DDQN (multi-step D3QN) based algorithm to design the local optimal UAV path by leveraging the constructed coverage map. In this algorithm, the UAV acts as the agent to learn the appropriate action to complete the flight mission. Numerical results show the effectiveness of the proposed algorithm for connectivity-aware UAV path planning and the superiority of 3D path design over its 2D counterparts. [ABSTRACT FROM AUTHOR]

The problem of disseminating continuous data flow from a given source node to all other network nodes is known as the dynamic broadcast problem, which is also a fundamental problem in multi-hop wireless networks (e.g., wireless ad hoc and sensor networks). Due to link unreliability and node mobility, the topology of a network typically changes with time. Little work has been conducted to address the dynamic broadcast problem in time-varying wireless multi-hop networks, especially under the popular Signal-to-Interference-plus-Noise-Ratio (SINR) interference model. In this paper, we study the dynamic broadcast problem in SINR-based time-varying directed acyclic multi-hop wireless networks formed by point-to-point wireless links. We consider the SINR-based link transmission rate model involved in the slot-based link scheduling. We first prove a tight upper bound for the broadcast capacity of the multi-hop wireless networks under study. We then propose an online max-weight Throughput-Optimal Dynamic Broadcast algorithm (TODB) which performs link weight allocation, link scheduling, and data forwarding for each time slot according to the current network connectivity and data reception rates of nodes. We derive the throughput-optimality of the TODB algorithm by proving that the broadcast throughput of TODB can achieve the aforementioned upper bound of the broadcast capacity. We evaluate the performance of TODB in terms of throughput and latency via simulations and the results validate its effectiveness. [ABSTRACT FROM AUTHOR]

Adaptive radio techniques have been extensively studied over the past two decades. In this paper, we investigate an adaptive algorithm to attain additive white Gaussian noise (AWGN) performance in non-white channels, using multicarrier communications such as OFDM and filter-bank multicarrier (FBMC). The non-white channel can be from non-white noise, or more commonly, interference. Our proposed algorithm: spectral shaping (SS), follows a simple optimization problem to find what we term reliable subcarriers, and then per subcarrier power allocations are made in order to attain the AWGN channel bit error ratio (BER). Subcarriers that experience high interference are deactivated or chosen as guard subcarriers. After describing our analysis, we show some simulation results in two non-white interference signal examples: the Gaussian pulse shaped high-power distance measuring equipment (DME) and a classical rectangular-pulse signal. The DME example is pertinent for currently proposed aeronautical communication systems, where new multicarrier techniques, e.g., the L-band digital Aeronautical communication systems (LDACS) have been designed as an inlay approach between the high-power DME channels in L-band. Our results show that in high DME power interferences, using this adaptive technique can improve the overall reliability and attain AWGN BER performance in AWGN channel. We provide power spectral density (PSD) results to compare OFDM and FBMC based spectra when our SS algorithm is applied. Also, we provide BER vs. bit energy to noise density ratio (Eb/N0) results in the AWGN, as well as more realistic aeronautical channels to compare SS system performance. Our results show that in the presence of high power DME interference, using this adaptive technique can improve the overall reliability and enable us to attain non-interference BER performance in realistic aeronautical channels. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority. [ABSTRACT FROM AUTHOR]

Complex building structures constrain indoor wireless communications. The significant dependence between the indoor wireless network performance and the building structures can be considered as an intrinsic property of a building, which means that the building has to be designed to achieve desirable indoor wireless network performance. The power gain (PG) and the interference gain (IG) have been defined as the figures of merit (FoMs) to evaluate the wireless performance of a building. Employing open space as the benchmark, the PG and the IG respectively quantify the effective change of the intended signal power and the undesired power, i.e., the interference and thermal noise power, received in a targeted user equipment (UE) location due to the presence of the building. In this paper, a tractable approach is proposed to obtain the PG and the IG with a partition-based path gain model. It enables the PG and the IG to capture the impact of both building materials and the building layout on indoor wireless network performance. Numerical results show that the analytical models in closed-form expressions facilitate the wireless performance assessment to a given building computational-efficiently. This work is a critical complement to the initial framework of the building wireless performance (BWP) evaluation. The proposed method can be employed in future wireless-friendly building design. [ABSTRACT FROM AUTHOR]

Cache-enabled small base stations (SBS) are capable of relieving the heavy burden of the backhaul link and reducing the transmission latency. The hit probability depends on the coverage probability and caching placement probabilities. However, the interference in the small-cell networks may significantly degrade the coverage probability. In this paper, for MIMO small-cell networks consisting of SBS and users, where both of them are equipped with multiple antennas, a joint interference alignment (IA) and probabilistic caching (JIA-ProbC) scheme is proposed. Using tools from stochastic geometry, the $K$ -th order Voronoi cells are constructed to form clusters, where $K$ SBSs cooperatively serve users within each of the $K$ -th order Voronoi cells. Then, the IA scheme for MIMO interference channel (IC) is employed to cancel the intra-cluster interference within each $K$ -th order Voronoi cell. By exploiting the advantage of multiples antennas at users, the IA scheme can simultaneously support more users interference-free than both the zero forcing (ZF) based interference cancellation scheme for MISO systems and SISO systems without interference management, as more interference can be canceled. Furthermore, the coverage probability is analytically approximated by the a closed-form expression. Moreover, the optimal caching placement probability is analytically derived. Numerical Simulation results show that the proposed JIA-ProbC can significantly outperform the existing joint ZF and probabilistic caching (JZF-ProbC) scheme for MISO systems and SISO probabilistic caching (SISO-ProbC) scheme as well as the joint IA and most popular caching (JIA-MPC) caching scheme. [ABSTRACT FROM AUTHOR]

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates distributed beamforming networks composed of multiple groups of transmitters where each group consists of two transmitters. The goal is to jointly optimize the phase components at the transmitters and the orientation angles of the groups that maximize the radiated energy over an angular sector containing an adversary while placing a null towards the client in order not to obfuscate the client. Since the optimization problem is non-convex, it is intractable to obtain the optimal solution in closed-form. To this end, we propose a non-iterative closed-form solution by adopting a group-wise nullforming approach where each group independently creates a null to the client. The effectiveness of the proposed scheme is validated through numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate hybrid beamforming, user scheduling, and resource allocation optimization based on spectrum coexisting forward transmission in integrated terrestrial-satellite network (ITSN) with the purpose of improving system sum rate and energy efficiency. Considering the limitation of on-board beamforming, a hybrid analog-digital beamforming scheme is designed under the scenario of millimeter wave (mmWave) coexisting in the ITSN framework. Besides, in order to further mitigate intra-beam and inter-beam interference, we propose an adaptive user scheduling scheme, which first determines the cluster center based on adaptive threshold, and then selects users with less channel correlation into a scheduling cluster. Moreover, we model system sum rate maximization problem that incorporates maximum power constrains and minimum data rate requirements. Combined with the aforementioned hybrid beamforming and user scheduling strategy, we formulate the sum rate maximizing problem to a pure power allocation issue. In view of the non-convexity and high complexity, we propose a feasible optimization method based on the minimum mean square error (MMSE) criterion and logarithmic linearization to optimize the power allocation for each user terminal (UT). Simulation results show that our proposed joint beamforming and resource allocation optimization scheduling scheme can achieve an attractive gain in system sum rate and energy efficiency compared with conservative beamforming and allocations. [ABSTRACT FROM AUTHOR]

In this paper, an antenna synthesis process is investigated for Intelligent Transportation System (ITS) application. The wireless communication between Road Side Units (RSUs) and On Board Units (OBUs) located in the vehicles enables smart mobility. However, to support high speed vehicles and high data rates, a wider coverage area by the RSU antennas projected on the highway is required. This is defined by the radiation patterns of the RSU antennas. On a multi-lane highway, interference from the adjacent lanes is a critical issue which determines the communication reliability in terms of Single-to-Interference plus Noise Ratio (SINR). Hence, in this paper, an interference-aware antenna synthesis process is proposed and a planar array is optimized based on the objective function of obtaining a wide coverage area in terms of SINR distribution for a given modulation scheme. The proposed antenna array enhances the reliable communication area and provides improved SINR distribution across the lane for misaligned vehicles from the center of the lane. The results indicate that the proposed antenna array performs better than the existing designs in terms of communication reliability. [ABSTRACT FROM AUTHOR]

The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a downlink non-orthogonal multiple access (NOMA) transmission scheme for hybrid millimeter-wave (mmWave) system and study its user grouping and power allocation. In order to maximize the weighted sum-rate (WSR) of the system, we first propose a user grouping algorithm based on min-sum message passing (MSMP) strategy under the limited number of radio frequency (RF) chains. Then, with the zero-forcing (ZF) digital precoding, we propose a power allocation scheme for multiple-RF-chain NOMA (MRFC-NOMA) structure based on a non-convex transformation method, and design a low-complexity algorithm. The simulation results show that the proposed joint user grouping and power allocation method with lower complexity can achieve better WSR performance than the traditional schemes. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication has been designed to afford improvements in the traffic congestion and traffic accidents by directly exchanging information between nearby vehicles. However, V2V communication may have difficulty providing service reliability due to interference between V2V links and vehicle-to-cellular user equipment links. In this paper, we consider the transmit power control algorithm to minimize interference among cellular users and vehicles in the context of V2V communication underlaid uplink cellular networks. First, we formulate the problem, which is an NP-hard combinatorial optimization problem with linear constraints. Addressing this problem with traditional optimization methods is ineffective; therefore, we design and train a deep neural network to address this optimization problem. Computational complexity analyses and simulation results reveal that the proposed algorithm outperforms weighted minimum mean squared error (WMMSE), fixed transmit power, and Dinkelbach's methods, and achieves near-global optimum with lower computation complexity than the exhaustive search (ES). [ABSTRACT FROM AUTHOR]

The spectrum usage often comes in an online fashion. Considering the selfish behaviors of both primary users(PUs) and sencond users(SUs), we design online double spectrum allocation methods. We propose a truthful online double auction for spectrum allocation. Preempting existing spectrum usage is not allowed. We design a strategyproof mechanism for both the primary user side and the SU side. In previous studies, users who do not interfere with each other have not been reasonably allocated to use channels. There is one and only one user on the used spectrum channel at a certain time, which will result in discarding many user requests. Our model allows multiple users to use a spectrum channel at the same time. Aiming at the shortcomings of previous research, we propose the concept of grouping for online auctions, so that all SUs that do not interfere with each other can use spectrum channels at the same time. This will greatly increase the number of users who use the spectrum at the same time. We thus increase the utilization rate of the user market, and a large number of users will not be idle and abandoned.In the design of our experiments, the grouping model proposed in this paper has obtained at least $43{\%}$ utilization rate on the set channel, and our experiments have obtained good generality for different interference radius. [ABSTRACT FROM AUTHOR]

In this paper, we investigate security and energy efficiency of multicast communication in cognitive satellite terrestrial networks. To achieve high spectrum efficiency and low interference power transmissions in the cognitive terrestrial network, the rate splitting multiple access scheme (RSMA) is employed to prompt the massive access of the secondary users. In particular, we derive analytical and asymptotic closed-form expressions for secrecy outage probability of the satellite terrestrial network with eavesdropping, interfering, and imperfect channel state information. Hence, a beamforming (BF) scheme based on RSMA for secondary network is proposed to suppress eavesdropping, mitigate interference, and enhance energy efficiency of the proposed system model. Then, we transform non-convex optimization constraints into resoluble problems utilizing Taylor series expansion and successive convex approximation. Finally, simulation results corroborate the above theoretical analysis and highlight the superiority of the designed BF scheme on security and energy efficiency. [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]

This article studies the transmit beamforming security in the dual-functional radar-communication (DFRC) system, where the radar beam also serves as the artificial jamming to suppress potential eavesdropper channels. Unlike previous researches, which assumed that the lone target might be a potential eavesdropper on the adversary side, this paper analyses a more generic scenario in which the eavesdroppers’ channel state information (CSI) is unknown. The eavesdropping rate should be limited to guarantee transmission confidentiality, which is intractable owing to the unknown CSI. To address this issue, we present the long-term eavesdropping signal-to-interference-plus-noise ratio (SINR) as a more tractable security metric. Based on the long-term eavesdropping SINR, the sum rate maximization (SRM) and jamming power maximization (JPM) transmission scheme are investigated. For the SRM scheme, we propose a zero-forcing (ZF) suboptimal algorithm to solve the non-convex fractional programming (FP) problem, and for the JPM scheme, the resultant problem is solved via the semidefinite relaxation (SDR) technique. Finally, numerical results validate the effectiveness of the proposed beamforming approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reference-modulated permutation index differential chaos shift keying (RM-PI-DCSK) over multipath fading channels. Due to quasi-orthogonal property between different permutated chaotic signals, the proposed scheme can remarkably suppress the inter-sequence interference to recover the desired information. Moreover, the proposed RM-PI-DCSK has one more modulation bit in the reference segment than the conventional permutation index differential chaos shift keying (PI-DCSK). Both the reference signal in the first time-slot and a permuted replica of the reference in the second time-slot are modulated for efficient transmission. The theoretical bit-error-rate (BER) performance and the highly consistent simulation results demonstrate that the proposed RM-PI-DCSK can achieve a performance gain 3 dB over the conventional PI-DCSK at a BER of $10^{-4}$ , and this gain becomes larger in a high-delay-spread channel. In particular, the performance superiority of the RM-PI-DCSK is further demonstrated in a typical ultra-wideband (UWB) channel with narrow-band interference. Therefore, this proposed scheme can be deemed as an efficient transmission alternative for chaotic-based wireless communications, such as in wireless local area network (WLAN) applications and indoor applications of the transmitted-reference UWB. [ABSTRACT FROM AUTHOR]

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources owned by the mobile network operators (MNOs). Then, they provide specific services to their mobile users using the resources obtained from MNOs. However, wireless resource sharing among SPs is challenging as each SP wants to maximize their utility/profit selfishly while satisfying the QoS requirement of their mobile users. Therefore, in this paper, we propose joint users association and wireless resource sharing problem in the cell-free UAVs-assisted wireless networks with the objective of maximizing the total network utility of the SPs while ensuring QoS constraints of their mobile users and the resource constraints of the UAVs deployed by MNOs. To solve the proposed mixed-integer non-convex problem, we decompose the proposed problem into two subproblems: users association, and resource sharing problems. Then, a two-sided matching algorithm is deployed in order to solve users association problem. We further deploy the whale optimization and Lagrangian relaxation algorithms to solve the resource sharing problem. Finally, extensive numerical results are provided in order to show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In a platoon-based vehicular cyber-physical system (VCPS), the traffic capacity and the fuel efficiency can be significantly improved by maintaining a small inter-vehicle distance (IVD). Meanwhile, the vehicle-to-infrastructure (V2I) communications provided by the roadside unit (RSU) are important for promoting the development of autonomous vehicles and reshaping the driving experience. However, as maintaining a small IVD inside the platoon may result in severe inter-beam interference, there is a tradeoff between the traffic capacity and the communication efficiency. In this paper, we formulate an optimization problem to minimize the IVD (a.k.a spacing) under the V2I quality-of-service (QoS) requirements by jointly controlling the transmit power, the beamwidth, and the IVD in a platoon-based VCPS. The traffic and channel uncertainties significantly complicate the analytical characterization of the impact of IVD, transmit power, and beamwidth on the QoS requirements. To solve the intractable challenging problem and capture the inherent timescale difference between the communication and vehicular systems, we propose a novel data-driven two-timescale reinforcement learning framework to control the beamforming and the spacing of the platoon at two timescales. With elaborately designed state, action, and reward, we develop a soft actor-critic based two-timescale deep reinforcement learning (DRL) algorithm to learn an effective policy for the control-communication co-design problem. Simulation results validate the effectiveness of spacing control and show that the proposed two-timescale DRL algorithm can significantly improve the traffic capacity and reduce the air drag coefficient while satisfying the QoS requirements of V2I communications. [ABSTRACT FROM AUTHOR]

Dynamic topology, fast-changing channels and the time sensitivity of safety-related services present challenges to the status quo of resource allocation for cellular-underlaying vehicle-to-vehicle (V2V) communications. In this paper, we investigate a novel federated multi-agent deep reinforcement learning (FedMARL) approach for the decentralized joint optimization of channel selection and power control for V2V communication. The approach takes advantage of both deep reinforcement learning (DRL) and federated learning (FL), satisfying the reliability and delay requirements of V2V communication while maximizing the transmit rates of cellular links. Specifically, we elaborately construct individual V2V agent implement by the dueling double deep Q-network (D3QN), and design the reward function to train V2V agents collaboratively. As a result, each agent individually optimizes channel selection and power level based on its local observations, including the instantaneous channel state information (CSI) of corresponding V2V link, the instantaneous co-channel interference from the cellular link, the previous channels selections of nearby V2V pairs, and the queue backlog at the V2V transmitter. Another important aspect is that we incorporate FL to alleviate the training instability problem induced by cooperative multi-agent environment. The local DRL models of different V2V agents are federated periodically, addressing the limitations of partial observability on the entire network status for individual agent, and accelerating the training process of multi-agent learning. Validated via simulations, the proposed FedMARL scheme shows superiority to the baselines in terms of the cellular sum-rate and the V2V packet delivery rate. [ABSTRACT FROM AUTHOR]

In secure spatial modulation with a malicious full-duplex attacker, how to obtain the interference subspace or channel state information (CSI) is very important for Bob to reduce or even completely cancel the interference from Mallory. In this paper, different from existing work with perfect CSI, the covariance matrix of malicious interference (CMMI) from Mallory is estimated to construct the interference subspace. To improve the estimation accuracy, a CMMI rank detector relying on the Akaike information criterion (AIC) is derived first. To achieve a high-precision CMMI estimation, two methods, principal component analysis-eigenvalue decomposition (PCA-EVD) and joint diagonalization (JD) are proposed. The proposed PCA-EVD is a rank deduction method whereas the JD method is a joint optimization method with improved performance in the low jamming-to-noise ratio (JNR) region at the expense of increased complexity. Simulation results show that the proposed PCA-EVD performs much better than existing methods like sample estimated covariance matrix (SCM) and EVD in terms of normalized mean square error (NMSE) and secrecy rate (SR). Additionally, the proposed JD has achieved a better NMSE performance than PCA-EVD in the low JNR region (JNR $\leq$ 0 dB) while the proposed PCA-EVD performs better than JD in the high JNR region. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) and millimeter wave (mmWave) communications are two major technologies to enhance the spectrum efficiency (SE). Considering that the SE improvement in the CRNs is limited due to the interference temperature imposed on the primary user (PU), and the severe path loss and high directivity in mmWave communications make it vulnerable to blockage events, we introduce an intelligent reflecting surface (IRS) into mmWave CRNs. Due to the estimation mismatch and the passivity of Eavesdroppers (Eves), perfect channel state information (CSI) of wiretap links is challenging to obtain, which promotes our research on robust secure beamforming (BF) design in the IRS-assisted mmWave CRNs. This paper considers the collaborate scenario of Eves, which allows us to investigate the BF design in the harsh eavesdropping environment. Specifically, by using a uniform linear array (ULA) at the cognitive base station (CBS) and a uniform planar array (UPA) at the IRS, and supposing that imperfect CSIs of angle-of-departures for wiretap links are known, we formulate a constrained problem to maximize the worst-case achievable secrecy rate (ASR) of the secondary user (SU) by jointly designing the transmit BF at the CBS and reflect BF at the IRS. To solve the non-convex problem with coupled variables, an efficient alternating optimization algorithm is proposed. As for the transmit BF at the CBS, we propose a heuristic robust transmit BF algorithm to attain the BF vectors analytically. As for the reflect BF at the IRS, by means of an auxiliary variable, we transform the non-convex problem into a semi-definite programming problem with rank-1 constraint, which is handled with the help of an iterative penalty function, and then obtain the optimal reflect BF through CVX. Finally, simulation results indicate that the ASR performance of our proposed algorithm has a small gap with that of the optimal solution with perfect CSI compared with the other benchmarks. [ABSTRACT FROM AUTHOR]

The terahertz (THz) waves with enormous bandwidth can be used along with the existing sub-6GHz and millimeter wave (mmWave) bands to achieve the ever evolving ecosystem of applications that need to be supported by the modern wireless networks. This paper investigates a user-centric dynamic base station (BS) clustering design for a hybrid network where THz, mmWave, and sub-6GHz BSs coexist. Invoking the proposed clustering model, the BS cooperation within each band is made possible by considering long term channel variations and all the surrounding BSs within a cluster with tunable size. A typical user is associated with the best BS cluster, from either a sub-6GHz, mmWave or THz tier based on the maximum signal-to-interference-plus-noise-ratio (SINR) criterion or the maximum rate criterion. Using tools from stochastic geometry, we assess the performance of the proposed user-centric hybrid system in terms of SINR and rate coverage performances, while accounting for: band specific propagation models, directional beamfroming, and BSs random locations. The accuracy of the analytical results is validated with Monte-Carlo simulations. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz BSs improves the rate significantly but may degrade the coverage performance. Thus, with carefully planned networks, enabling user-centric BS cooperation for hybrid wireless systems can achieve ultra-high rates while maintaining sufficient coverage in sixth-generation (6G) networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

In a vehicular ad hoc network (VANET) based on IEEE 802.11p, the performance of the communication link is heavily influenced by interference. To quantify the impact of interference, analytical models usually explicitly or implicitly assumed the interference range beyond which interference is ignored. We find that the potential maximum interference range should be related to the minimum interference power that the device can perceive. However, most previous works on VANET performance modeling simply assumed a fixed interference range. The most recently proposed effective interference range dependent on the signal-to-interference-and-noise ratio (SINR) threshold may appear to exceed the potential maximum interference range, resulting in an overestimation of the impact of interference. This paper proposes the SINR related Effective Distance Constrained by the Maximum interference range-based (SED-CM) model for IEEE 802.11p VANET performance evaluation on the Highway. First, we give the potential maximum interference range setting by studying the empirical distribution of interference distance corresponding to the minimum interference power in NS2 simulation. The performance metrics are then derived. The proposed SED-CM model is further extended to the intersection scenario. We conducted extensive NS2 simulations to evaluate the proposed model and compare it with three typical models. Results show that the SED-CM model presents the best evaluation ability. Moreover, we experimentally analyzed the effect of vehicle speed on the performance of VANET by mapping the vehicle speed to the vehicle density. Results show that a faster beacon rate is required to meet the reliability requirement of safety applications with the increase in vehicle speed. [ABSTRACT FROM AUTHOR]

Vehicular networks are envisioned to deliver data transmission services ubiquitously, especially in the upcoming autonomous driving era. Accordingly, the high data traffic load poses a heavy burden to the terrestrial network infrastructure. Unmanned Aerial Vehicles (UAVs) show enormous potential to assist vehicular networks in providing services. In a dense UAV-assisted vehicular network with a large number of users, spectrum sharing is leveraged for alleviating the spectrum scarcity. However, the increasing data traffic still leads to the UAV energy consumption problem. This paper considers a specific network scenario where a UAV transmits its cached content files to vehicular users over UAV-to-vehicle (U2V) links while vehicle-to-vehicle (V2V) links reuse the U2V spectrum for safety-critical message exchanges. To improve the UAV's energy efficiency while guaranteeing users’ quality of service (QoS), we jointly optimize content placement, spectrum allocation, co-channel link pairing, and power control, which are the key factors affecting energy efficiency and QoS. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by combining Hungarian and DDQN. We perform system performance evaluations, demonstrating that our approach can not only improve the UAV's energy efficiency while satisfying the users’ QoS requirements but also increase the timeliness of making decisions. [ABSTRACT FROM AUTHOR]

In this paper, we propose one joint interference alignment and subchannel allocation (JIARA) interference management scheme, aiming at maximizing the number of satisfactory users with different degrees of freedom (DoF) demand in ultra-dense wireless networks, while the existing works only consider the same DoF demands. The original JIARA scheme is equivalent to the maximum $K$ -colorable subgraph (MKCS) problem in graph theory for a given $K$ -colors and the multicoloring demand at each vertex, hence NP-hard. Therefore, we propose one graph theory based low complexity sub-optimal JIARA scheme. Firstly, we propose one virtual allocation of subchannel based scheme to construct transformed the conflict graph, which is applicable for the different DoF demands and different size of the feasible IA group. Furthermore, we propose one greedy multi-coloring based algorithm to determine the maximum number of satisfactory users. Simulation results show that the proposed JIARA scheme outperforms the subchannel resource allocation only scheme. [ABSTRACT FROM AUTHOR]

In this paper, stochastic geometry tools are utilized to analyze the performance of a wireless network with Unmanned Aerial Vehicles (UAVs). The evaluation is based on the concept of a truncated octahedron that is the fundamental three-dimensional (3D) shape that models the basic cell in an entirely aerial cellular structure with UAV nodes only. The spatial locations of the aerial users, are modeled as a 3D Binomial Point Process (BPP). Subsequently, stochastic geometry analysis in a reference truncated octahedron located at the origin is performed and exact expressions for the distances between aerial users, are derived. To this end, expressions for evaluating the volume of the regions of the 3D cell in which an aerial user may fall, are derived. Then, by defining the signal-to-interference-plus-noise (SINR) ratio at the reference UAV-base station (UAV-BS) receiver, a performance analysis in terms of coverage probability is conducted under two interference scenarios, i.e., a pure intra-cell interference and an intra- plus inter-cell interference case. As intermediate step in the coverage probability analysis, expressions for relevant distance distributions and the Laplace transform of the aggregate interference power distribution are derived. For the sake of completeness, coverage probability analysis under intra-cell interference is also performed and compared to a 3D cell modeled as a sphere. The analytical expressions are also compared to simulation results to validate the accuracy of the proposed framework and reveal valuable insights. [ABSTRACT FROM AUTHOR]

Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity. In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control. In this paper, we propose a model-free method adopting the deep deterministic policy gradient algorithm (DDPG) with feedforward neural networks (NNs) to solve the downlink max-min power control problem in CF massive MIMO systems. Our result shows that compared with the conventional convex optimization algorithm, the proposed DDPG method can effectively strike a performance-complexity trade-off obtaining 1,000 times faster implementation speed and approximately the same achievable user rate as the optimal solution produced by conventional numerical convex optimization solvers, thereby offering effective power control implementations for large-scale systems. Finally, we extend the DDPG algorithm to both the max-sum and the max-product power control problems, while achieving better performance than that achieved by the conventional deep learning algorithm. [ABSTRACT FROM AUTHOR]