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The next-generation wireless network is expected to be highly dense with a large number of heterogeneous Internet of Things (IoT) devices, working in co-operation with various Small Cell Access Points (SAPs) and Fog Servers (FSs) underlying cellular 5G infrastructure, altogether. The densely deployed 5G architecture is expected to fulfill the IoT's growing data demand in a feasible computational complexity. In this work, the resource provisioning mechanism in a heterogeneous 5G network is formulated to maximize the overall benefit of SAPs by selecting the most appropriate FSs for offloading the set of IoT tasks, as an NP-hard problem. Moreover, a Restricted Three-sided Matching with Size and Cyclic (R-TMSC) preference model is implemented to obtain a stable solution among IoTs, SAPs, and FSs underlying cellular 5G networks. Furthermore, the effectiveness of the proposed heuristic is shown through theoretical, experimental, and real-world data analysis. [ABSTRACT FROM AUTHOR]

Large-scale propagation characterization for multiple measurements in a micro-cell scenario is carried out at 5G frequency (3.8 GHz). Distance-based path loss is computed for routes with line-of-sight, non-line-of-sight and obstructed-line-of-sight conditions, assuming a simple power law model. Path Loss exponents are computed for the whole routes, giving an effective understanding of the propagation environment. Empirical shadowing distribution is fitted to red Gaussian and non-Gaussian probability distributions. All previous propagation parameters are also found assuming measurements with similar transmitter locations come from the same channel conditions. This assumption is tested. Finally, although frequently absent in standard channel models, and useful for statistical ensemble average approximations, stationary regions are computed for all the routes. [ABSTRACT FROM AUTHOR]

A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed decoder initializes $L$ $(L\geq 1)$ active decoding paths with $L$ random codeword permutations sampled from the full symmetry group of the codes. The path extension in the permutation domain is carried out until the first constituent RM code of order 1 is visited. Conventional path extension of the successive-cancellation list decoder is then utilized in the information bit domain. The simulation results show that for a RM code of length 512 with 46 information bits, by running 20 parallel permuted FHT-FSCL decoders with $L=4$ , we reduce 72% of the computational complexity, 22% of the decoding latency, and 84% of the memory consumption of the state-of-the-art simplified successive-cancellation decoder that uses 512 permutations sampled from the full symmetry group of the code, with similar error-correction performance at the target frame error rate of $10^{-4}$. [ABSTRACT FROM AUTHOR]

The new Fifth generation (5G) of wireless communications and beyond 5G (B5G) networks are required to provide connectivity for massive number of devices at high speed and low latency. Within this context, non-orthogonal multiple access (NOMA) has been involved and emerged in recent research since it is considered as an enabling technology for 5G and B5G networks. NOMA allows several users to share the same resources of time and frequency, making it an excellent candidate to provide impressively large connectivity with high spectral efficiency. In this paper, we study the performance of NOMA systems assuming independent but not necessarily to be identical $N$ -Nakagami-m multipath fading channels. This channel model subsumes the double Nakagami-m, which was investigated in the context of intervehicular communications and keyhole multiple input multiple output systems, as special cases. To this end, we derive exact analytical and asymptotic expressions for the probability density function of the overall signal to noise ratio, system capacity, and the pairwise error probability (PEP) for each user. Numerical results are also provided herein to show the effects of fading parameters and number of users on the capacity and the PEP of each user. [ABSTRACT FROM AUTHOR]

This study investigated relay-assisted mode 1 sidelink (SL) multicast transmission, which encounters interference from mode 2 SL transmission, for use in low-latency vehicle-to-everything communications. To accommodate mode 1–mode 2 SL traffic, we use the hybrid multiple access (MA) approach, which combines orthogonal MA (OMA) and nonorthogonal MA (NOMA) schemes. We introduce a low-complexity location-based hybrid MA algorithm and its associated relay selection that can be used when SL channel state information is unavailable. [ABSTRACT FROM AUTHOR]

Most researches on the Internet of Vehicles (IoV) in the 5G era are focused on the low latency and the massive transmitted data. However, the 5G base station has relatively a small coverage and a high cost, which is the main reason to hinder the development of the 5G applications on the IoV. In this paper, we try to put forward some feasible solutions for this problem. We will use two physical layer communication techniques, the Coherent Beamforming (CB) technique and the Successive Interference Cancellation (SIC) technique, to jointly optimize the throughput of vehicles data transmission and the network infrastructure cost. We first establish the mathematical model based on CB and SIC technology and prove that it cannot be solved directly. Then, we design the Road and CB-nodes Assignment (RCA) algorithm and the Uninstalling Data Scheduling (UDS) algorithm to solve the problem. RCA is based on CB and SIC technology to reasonably arrange CB-nodes, so that CB-nodes set can send data from vehicles to the base station and the base station can receive multiple sets of data at the same time. UDS schedules vehicles based on various factors to optimize the total amount of data transferred by vehicles and improve throughput. In simulations, we conduct some comparative experiments, which are the not using Algorithm 3 scheme, the CB scheme, the SIC scheme, the common scheme and the Relay Task Offloading Scheme in Vehicular Edge Computing algorithm (RVEC Algorithm). Results show that our proposed method has the advantages of cost saving and throughput improvement. [ABSTRACT FROM AUTHOR]

Until now, multi-hop device-to-device (MD2D) routing in mobile wireless networks has been designed to operate over a single-protocol routing framework. Consequently, to the best of our knowledge, no framework has been developed to enable the dynamic deployment and switching of multiple MD2D routing protocols under one framework. This paper proposes a multi-protocol framework to introduce the idea of clustering a cell and deploying different MD2D routing protocols for each cluster based on the cluster requirements. To this end, four clusters are developed with varying network conditions (i.e., node density, mobility rate, and the number of flows). Then, the performance of our two previously designed multi-hop routing protocols, namely hybrid SDN architecture for wireless distributed networks (HSAW) and source-based virtual ad hoc routing protocol (VARP-S), are investigated in each cluster in terms of energy consumption, end-to-end (E2E) delay, packet loss, and cellular-band overhead. Based on the achieved simulation results, a multiple-criteria decision-making (MCDM) approach based on the analytic hierarchy process (AHP) is developed to choose the most suitable protocol for each cluster to provide the best performance. The simulation results indicate that our proposed multi-protocol framework provides better performance compared to traditional single-protocol architectures. [ABSTRACT FROM AUTHOR]

The evolution of telecommunication networks toward the fifth generation of mobile services (5G), along with the increasing presence of cloud-native applications, and the development of Cloud and Mobile Edge Computing (MEC) paradigms, have opened up new opportunities for the monitoring and management of logistics and transportation. We address the case of distributed streaming platforms with multiple message brokers to develop an optimisation model for the real-time assignment and load balancing of event streaming generated data traffic among Edge Computing facilities. The performance indicator function to be optimised is derived by adopting queuing models with different granularity (packet- and flow-level) that are suitably combined. A specific use case concerning a logistics application is considered and numerical results are provided to show the effectiveness of the optimisation procedure, also in comparison to a “static” assignment proportional to the processing speed of the brokers. [ABSTRACT FROM AUTHOR]

Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times. We solve this problem via a novel expediting beam selection by leveraging multimodal data collected from sensors like LiDAR, camera images, and GPS. We propose individual modality and distributed fusion-based deep learning (F-DL) architectures that can execute locally as well as at a mobile edge computing center (MEC), with a study on associated tradeoffs. We also formulate and solve an optimization problem that considers practical beam-searching, MEC processing and sensor-to-MEC data delivery latency overheads for determining the output dimensions of the above F-DL architectures. Results from extensive evaluations conducted on publicly available synthetic and home-grown real-world datasets reveal 95% and 96% improvement in beam selection speed over classical RF-only beam sweeping, respectively. F-DL also outperforms the state-of-the-art techniques by 20-22% in predicting top-10 best beam pairs. [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]

Soft-Input Soft-Output (SISO) polar decoding algorithms, such as Belief Propagation (BP) and Soft Cancellation (SCAN) polar decoding, offer iteration capability for facilitating turbo-style detection. However, at lower Signal-to-Noise Ratios (SNRs), the performance of the BP and SCAN decoders is about 1.5 dB and 0.5 dB worse than that of the state-of-the-art hard-decision Successive Cancellation List (SCL) decoding algorithm, respectively, despite iteratively exchanging information with a Multiple Input Multiple Output (MIMO) detector. Motivated by this gap, we conceive a novel G-SCAN polar decoder, which generates both soft-decision and hard-decision outputs. This is achieved by intrinsically amalgamating a list decoder with a novel SISO decoder. These soft-decision outputs may be used for turbo-detection, but they also support the hard-decision outputs of the SCL algorithm for achieving superior block error rate (BLER) performance. As a result of these benefits, the proposed G-SCAN algorithm using a list size of $L = 2$ offers around 1 dB BLER gain compared to the conventional hard-decision SCL decoder relying on $L = 32$. Furthermore, we have carried out its Extrinsic Information Transfer (EXIT) chart analysis, and characterized the performance vs. the complexity of the proposed G-SCAN algorithm, and compared it to various soft- and hard-decision output benchmarks for a wide variety of different rate-matching modes and block lengths. Furthermore, in order to reduce the complexity of the proposed algorithm, a novel Cyclic Redundancy Check (CRC)-aided G-SCAN algorithm is also proposed, which facilitates early termination and improves the BLER performance. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint resource scheduling problem for multiplexing two distinct 5 G services: enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC). While eMBB services require high data rates, URLLC services lay more emphasis on extremely low latency and high reliability. To satisfy these heterogeneous requirements for eMBB and URLLC coexistence, we propose a risk-resistant scheme for resource allocation to both eMBB and URLLC traffic that aims to maximize the eMBB data rate, while considering the URLLC delay threshold violation to maintain URLLC delay requirements. Based on the M/G/1 queueing model, the URLLC packet latency target is realized by minimizing the average packet delay in violation cases, where “violation” is judged by introducing the “conditional value at risk (CVaR)” as a risk metric (i.e., “risk” here is defined as URLLC delay threshold violation). To solve the formulated problem, we transform it into two convex optimization problems to obtain a near-optimal resource allocation solution. Simulation results show that the proposed scheme improves the fairness among the eMBB users and eMBB throughput by $30\%\sim 34\%$ compared to existing works, while satisfying the stringent URLLC delay requirements. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA), as a codebook-based non-orthogonal multiple access (NOMA) technique, has received research attention in recent years. The codebook design problem for SCMA has also been studied to some extent since codebook choices are highly related to the system’s error rate performance. In this paper, we approach the SCMA codebook design problem by formulating an optimization problem to maximize the minimum Euclidean distance (MED) of superimposed codewords under power constraints. While SCMA codebooks with a larger MED are expected to obtain a better BER performance, no optimal SCMA codebook in terms of MED maximization, to the authors’ best knowledge, has been reported in the SCMA literature yet. In this paper, a new iterative algorithm based on alternating maximization with exact penalty is proposed for the MED maximization problem. The proposed algorithm, when supplied with appropriate initial points and parameters, achieves a set of codebooks of all users whose MED is larger than any previously reported results. A Lagrange dual problem is derived which provides an upper bound of MED of any set of codebooks. Even though there is still a nonzero gap between the achieved MED and the upper bound given by the dual problem, simulation results demonstrate clear advantages in error rate performances of the proposed set of codebooks over all existing ones not only in AWGN channels but also in some downlink scenarios that fit in 5G/NR applications, making it a good codebook candidate thereof. The proposed set of SCMA codebooks, however, are not shown to outperform existing ones in uplink channels or in the case where non-consecutive OFDMA subcarriers are used. The correctness and accuracy of error curves in the simulation results are further confirmed by the coincidences with the theoretical upper bounds of error rates derived for any given set of codebooks. [ABSTRACT FROM AUTHOR]

With the development of new radio (NR) technologies, sidelink (SL) transmission plays a more vital role in supporting low latency, higher reliability vehicle-to-everything (V2X) communications. Compared with the Long-Term Evolution (LTE), NR V2X aims to support enhanced V2X use cases with more stringent latency and reliability requirements. 3GPP introduces NR V2X distributed resource allocation to fulfill the low latency requirement to reduce the delay caused by requesting transmission resources from gNB. In distributed mode, UEs autonomously select resources without the scheduling from gNB. NR V2X introduces the physical sidelink feedback channel (PSFCH) to enhance transmission reliability. This paper presents the resource allocation mechanism for NR V2X distributed mode. Since the SL feedback channel is a new feature different from LTE V2X, we discuss the potential enhancements on resource allocation utilizing SL feedback channel to improve the reliability. This paper proposes a Receiver Grant-based resource allocation, which allows the receiver to send the grant with the feedback channel. By allowing the transmitter to indicate the pre-selected resources before the end of the previous reservation, the receiver can inform the transmitter about the collided resource selection through the PSFCH. The analytical models for NR V2X distributed mode with the feedback channel are also provided for UE to estimate the collision ratio. The simulation results and the analytical model show that the proposed Receiver (Rx) Grant-based resource allocation improves transmission reliability in terms of collision ratio. For future advanced V2X use cases, the analytical models provide an evaluation method and help UE make decisions to meet the reliability requirements. [ABSTRACT FROM AUTHOR]

In this paper, we propose a channel allocation algorithm for heterogeneous multi-cell cognitive radio 5G networks. The proposed algorithm considers channel-availability and channel-demand heterogeneity across CR cells. We use the theory of continuous-time Markov chains to develop mathematical expressions for channel availability and channel demand. Both expressions are incorporated in a binary integer program to find the optimal channel allocation. Further, we equip the proposed channel allocation algorithm with a novel interference domain identification scheme to detect interference range overlaps between cognitive radio cells. This scheme is essential for channel allocation, as it plays an important role in avoiding inter-cell interference. Our simulation results show significant performance gains compared to existing algorithms overlooking channel-availability. Furthermore, we use the theory of Dantzig-Wolfe decomposition to develop a framework for transforming centralized channel allocation algorithms into distributed ones. Through simulation experiments, we demonstrate the efficacy of the proposed framework. [ABSTRACT FROM AUTHOR]

5G and beyond networks should efficiently support services with stringent and diverse QoS requirements. This includes services for verticals that demand Ultra Reliable and Low Latency Communications (URLLC). Scheduling strongly impacts the communication latency, and 5G NR introduces grant-free scheduling to reduce the latency at the radio level. Grant-free scheduling can use shared resources and the transmission of K replicas per packet to increase the packet delivery ratio and efficiently utilize the spectrum. Previous studies have shown that existing 5G NR grant-free scheduling has limitations to sustain URLLC requirements for aperiodic (or uncertain) and deterministic traffic that is characteristic of verticals such as Industry 4.0 or manufacturing. In this context, this paper proposes and evaluates a novel grant-free scheduling scheme that can efficiently support deterministic and aperiodic uplink traffic. The scheme avoids packet collisions among UEs sharing resources using a priority-based contention resolution process that relies on the transmission of announcement messages in minislots and a local channel sensing process. This study demonstrates that the proposed sensing-based grant-free scheduling scheme outperforms current 5G NR grant-free scheduling implementations, and can support a higher number of UEs with URLLC and deterministic requirements with a considerably lower number of radio resources. [ABSTRACT FROM AUTHOR]

Proliferation of 5G devices and services has driven the demand for wide-scale enhancements ranging from data rate, reliability, and compatibility to sustain the ever increasing growth of the telecommunication industry. In this regard, this work investigates how machine learning technology can improve the performance of 5G cell and beam index search in practice. The cell search is an essential function for a User Equipment (UE) to be initially associated with a base station, and is also important to further maintain the wireless connection. Unlike the former generation cellular systems, the 5G UE faces with an additional challenge to detect suitable beams as well as the cell identities in the cell search procedures. Herein, we propose and implement new channel-learning schemes to enhance the performance of 5G beam index detection. The salient point lies in the use of machine learning models and softwarization for practical implementations in a system level. We develop the proposed channel-learning scheme including algorithmic procedures and corroborative system structure for efficient beam index detection. We also implement a real-time operating 5G testbed based on the off-the-shelf Software Defined Radio (SDR) platform and conduct intensive experiments with commercial 5G base stations. The experimental results indicate that the proposed channel-learning schemes outperform the conventional correlation-based scheme in real 5G channel environments. [ABSTRACT FROM AUTHOR]

One of the pillars of the 5G architecture is network slicing, in which hardware, radio, and power resources are virtualized as a logical network taking into account the requirements of diverse applications. While ensuring performance isolation among different slices, resource allocation in 5G Radio Access Networks (RANs) is associated with different challenges due to network dynamics and the different applications’ requirements. In this paper, we have considered the allocation of power and radio resources to rate-based as well as resource-based users. We propose an energy-efficient deep reinforcement learning-assisted resource allocation (EE-DRL-RA) method for RAN slicing in 5G networks. The main idea of the proposed method is to exploit a collaborative learning framework that includes deep reinforcement learning (DRL) and deep learning (DL) to decide on resource allocation in the RAN. Specifically, we use DL for decision-making on resource allocation on a large time-scale and DRL for decision-making on resource allocation on a small time-scale. The asynchronous advantage actor-critic (A3C) and the stacked and bidirectional long-short-term-memory (SBiLSTM) network are used as DRL and supervised DL methods, respectively. Furthermore, we determine the optimal power and resource blocks (RBs) for rate-based users by formulating the energy-efficient power allocation (EE-PA) problem as a non-convex optimization problem and solve it by an efficient iterative algorithm. Our proposed approach is unique in that it simultaneously allocates power and RBs while ensuring slice isolation with low computational and time complexity. Simulation results show that EE-DRL-RA yields better performance compared to a state-of-the-art published method in terms of convergence speed, computational complexity, energy efficiency, and the number of accepted users as well as the degree of inter-slice isolation. [ABSTRACT FROM AUTHOR]

Pilot-pouring superimposed training (PPST) is a novel channel estimation technique specially designed for cyclic block filtered multi-tone (CB-FMT), where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the opt where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the optimization process, where the spectral efficiency must be maximized through the signal-to-interference plus noise ratio (SINR). Two optimization approaches are proposed, where the first one, referred as pilot-pouring optimization (PPO), is focused on performance at the expense of a high complexity, while the second one, denoted as low-complexity PPO (LPPO), is able to trade-off between performance and execution time. Numerical results are provided in order to show the validity of our proposal, where the different optimization problems are compared in terms of SINR and execution time. [ABSTRACT FROM AUTHOR]

Advanced driver-assistance system features in current vehicles (as precursor to fully autonomous, connected vehicular systems) will rely on 5G network links. Specifically, enabling various real-time operational aspects (e.g. emergency messaging), performance of ultra-reliable low-latency communications (URLLC) specified by 3GPP Release 16 is of direct relevance. A key challenge in achieving URLLC goals is the natural tension between simultaneously achieving high reliability (low block error rate or BLER) and low latency. When operating in a dynamic (i.e. significant time-varying channels due to vehicular mobility) environment, link adaptation is fundamental to URLLC implementation. In this paper, we introduce a new Markov Chain model for link adaptation that predicts the three key performance indicators (KPIs): end-to-end link latency, throughput and BLER as a function of the modulation and coding scheme used. The predictions from the Markov Chain model are supported by Monte Carlo simulations for various mobility scenarios, to explore how 5G network parameters may be adapted to achieve desired benchmarks, e.g. maximizing link throughput while providing strict latency and BLER bounds. [ABSTRACT FROM AUTHOR]

Heterogeneous cloud radio access network (H-CRAN) has been deemed as a promising architecture for the fifth generation wireless networks. In H-CRAN, small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to boost the spectral efficiency. H-CRANs with dense RRHs suffer from severe inter-cell interference due to the random deployment of dense SRRHs, which limits its performance. In this paper, we propose an Edge-Aware RRH-Cooperation (EARC) scheme to mitigate the impact of both the cross-tier and co-tier interference. We partition the users into two groups, Cell Edge Device (CED) and Non-cell Edge Device (NED). These two groups, CED and NED, are mapped into two association modes, dual-association and single-association mode, respectively. In single-association mode, the NED associates with the RRH that provides the maximum received signal. On the other hand, in the dual-association mode, the CED associates with the two strongest RRHs where the two RRHs may belong to the same or different tier(s). Using tools from stochastic geometry, we quantify the performance gains of the proposed EARC scheme in terms of outage probability and ergodic rate. To further show the efficacy of the proposed scheme, we compare it to four schemes developed in the literature. [ABSTRACT FROM AUTHOR]

The capability of Non-Orthogonal Multiple Access (NOMA) for minimizing interference at the receiver using successive interference cancellation (SIC) makes it as a promising solution for 5G mobile communication. Device-to-device (D2D) communication is a part of the 5G network which provides frequency reuse in cellular networks and provides a better quality of service (QoS) to co-channel users. In this paper, we investigate the resource allocation and time scheduling algorithms for a NOMA based D2D communication system. In addition, to overcome the problem of energy at D2D transmitter and receivers, wireless powered communication network (WPCN) is used. Considering these issues, the objective of the proposed scheme is to maintain energy efficient communication among D2D transmitters and receivers without compromising energy efficiency (EE) of cellular users. To solve the fractional form of an optimization problem, we employ the Dinkelbach method. A many-to-many matching game approach is used for channel allocation and successive convex approximation for low complexity (SCALE) is used to control the power of the D2D transmitter. The simulation results demonstrate that by maximizing the EE and optimizing the time and transmission power of D2D groups, the energy efficiency of the network can be significantly improved in comparison to the other competitive schemes in the existing literature. [ABSTRACT FROM AUTHOR]

Network slicing (NS) management devotes to providing various services to meet distinct requirements over the same physical communication infrastructure and allocating resources on demands. Considering a dense cellular network scenario that contains several NS over multiple base stations (BSs), it remains challenging to design a proper real-time inter-slice resource management strategy, so as to cope with frequent BS handover and satisfy the fluctuations of distinct service requirements. In this paper, we propose to formulate this challenge as a multi-agent reinforcement learning (MARL) problem in which each BS represents an agent. Then, we leverage graph attention network (GAT) to strengthen the temporal and spatial cooperation between agents. Furthermore, we incorporate GAT into deep reinforcement learning (DRL) and correspondingly design an intelligent real-time inter-slice resource management strategy. More specially, we testify the universal effectiveness of GAT for advancing DRL in the multi-agent system, by applying GAT on the top of both the value-based method deep Q-network (DQN) and a combination of policy-based and value-based method advantage actor-critic (A2C). Finally, we verify the superiority of the GAT-based MARL algorithms through extensive simulations. [ABSTRACT FROM AUTHOR]

This paper examines network slicing within the radio access network which employs an orthogonal frequency division multiple access system for downlink communications. Its radio resources are allocated among slices configured for specific 5G use cases as well as other non-5G services. This work sets out to achieve optimal resource provisioning and power allocation within the context of priority slicing where slices are assigned priority levels by the base station. A mixed-integer non-linear programming problem is formulated to maximize the throughput of a best effort slice while satisfying the constraints of the high priority 5G slices. The problem is simplified and relaxed into a convex optimization problem. Three scenarios under various conditions are presented to serve as benchmarks. The impact of the chosen schedulers on the allocations, intra-slice, and inter-slice contentions is also examined. The results highlight that the wireless network can satisfy the quality-of-service requirements of opposing levels of priorities of traffic while simultaneously mitigating both inter-slice and intra-slice contentions under conditions mirroring that of practical wireless network deployments. Furthermore, it is proven that the convex approximation problem serves as a reasonable approximation of the original MINLP problem. [ABSTRACT FROM AUTHOR]

In this paper, an endfire dual-polarized phased array is introduced for 5G mobile applications, which operates from 26 GHz to 30 GHz with low profile of only 1.1 mm (${\bf 0.1\lambda _0}$ at 26 GHz) and small clearance of 3.5 mm. The array consists of interleaved substrate integrated waveguide (SIW) based vertical-polarized elements and notch type horizontal-polarized elements. The bandwidth of each SIW element is greatly improved by introducing a wide-band impedance transition, which is composed by parallel plate resonators and a metallic via in between. The notch elements have very compact size, which helps to keep the half-wavelength inter-element distance. The array has a planar structure and is easy to fabricate. The measured and simulated results align well with each other. [ABSTRACT FROM AUTHOR]

A proper channel modeling methodology that characterizes the statistics of extreme events is key in the design of a system at an ultra-reliable regime of operation. The strict constraint of ultra-reliability corresponds to the packet error rate (PER) in the range of 10−9−10−5 within the acceptable latency on the order of milliseconds. Extreme value theory (EVT) is a robust framework for modeling the statistical behavior of extreme events in the channel data. In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation. This methodology includes techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the generalized Pareto distribution (GPD). The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Finally, the proposed approach chooses the best model with minimum complexity that represents the time variation behavior of the non-stationary channel data sequence. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate the capability of the proposed methodology in providing the best fit to the extremes of the non-stationary data. The proposed approach significantly outperforms the channel modeling approach using the stationary channel assumption in characterizing the extreme events. [ABSTRACT FROM AUTHOR]

A substrate integrated waveguide (SIW) H-plane horn antenna with miniaturized longitudinal dimension and increased gain is proposed by loading slow-wave (SW) structures in the form of metallized blind via-holes inside the flare region of the horn for vehicular millimeter wave communications. The slow-wave structure can reduce the phase velocity around the center line of the broad wall and compensate the large phase difference between the center and edge of the aperture caused by reduction of longitudinal length of the horn. Compared with optimum horn with the same aperture width, the gain of miniaturized SIW horn is successfully increased, while the longitudinal dimension is decreased. A SW-SIW horn antenna which covers the 28 GHz band (27.5–28.35 GHz) allocated by Federal Communications Commission for the 5G millimeter wave communications is designed to verify the feasibility of the miniaturized SIW horn antenna facilitated by the proposed slow-wave structure. The SW-SIW horn realizes 53.83% reduction of longitudinal length compared with the optimum horn. The gain of the SW-SIW horn is improved by 1.1 dB on average over 27.5–28.5 GHz compared with optimum horn. A prototype of the proposed SW-SIW H-plane horn antenna is manufactured and measured. The measured results agree well with the simulations. [ABSTRACT FROM AUTHOR]

The use of millimeter-wave (mmWave) bands in 5G networks introduces a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve a desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model. We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area, minimize the deployment cost, and maintain a desired quality-of-service level. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area. Moreover, the gNB placement aided with PMRs require fewer gNBs to cover the same area, which in turn reduces the deployment cost. [ABSTRACT FROM AUTHOR]

While 5G is being deployed all around the world, the industry and academia start the investigation of 6G. Network function virtualization (NFV) is seen as the key enabler towards the flexible resource management and sharing of 6G networks. The main idea of NFV is to decouple the physical devices from the specific functions that run on them. In NFV, virtual network service (NS) is modeled as a service function chain (SFC). The challenges in SFC description, composition, embedding, and scheduling are key issues in NFV. In this paper, we focus on the joint SFC embedding and scheduling for NFV in 6G networks. With the assistance of sensing the physical node resource abilities, we propose a novel SFC embedding and scheduling algorithm, which is named as RA-SFC-6G. A new service is first composed as an SFC which is an ordered sequence of virtual network functions (VNFs). Afterwards, our RA-SFC-6G algorithm will select the physical nodes with stronger resource abilities and abundant resources to accommodate the SFC. In addition, the NS is guaranteed to be implemented without violating the NS maximum allowed scheduling time. If violated, our RA-SFC-6G can re-embed and re-schedule the violated VNFs to suitable positions. In order to validate our RA-SFC-6G performance, we do the experiment work. Experiment results show that our RA-SFC-6G achieves at least 10% higher SFC acceptance ratio than the previous counterparts and the typical heuristics. [ABSTRACT FROM AUTHOR]

To support ultra-reliable and low-latency communication (URLLC) in vehicular networks, the virtual cells, where multiple access points (APs) cooperatively serve one mobile node, have been proposed to reduce the end-to-end latency in the downlink. The latency can be further reduced by eliminating the need for retransmission and feedback control, i.e., open-loop communications. However, it is difficult to achieve a high reliability of the uplink via virtual cells, because of multiple access interference and collisions from other virtual cells nearby. In this paper, we formulate the proactive radio resource allocation in the open-loop uplink of vehicular networks as a stochastic optimization problem with the objective to maximize the uplink reliability while ensuring the network stability. The optimal resource allocation policies are obtained solving the optimization problem using the Lyapunov optimization technique in a distributed manner. To reduce the computational and to improve the challenges of the Lyapunov optimization, we propose a virtual resource slicing algorithm that maps radio resource units to virtual resource blocks. Simulation results exhibit that both ultra-low latency and ultra high reliability are guaranteed in the open-loop uplink of vehicular networks. Based on the theoretical performance analysis and simulations, the uplink radio access procedure is summarized for the URLLC in vehicular networks. [ABSTRACT FROM AUTHOR]

In this paper, we developed a prototype of power domain downlink non-orthogonal multiple access (NOMA) using software defined radio (SDR). Our key focus was to experimentally evaluate the utility of dedicated relay-cooperative NOMA under diverse channel conditions. Furthermore, instead of using traditional power allocation mechanisms, users are categorized based on quality of service (QoS) requirements. For extensive comparative analysis, a series of experiments are performed to evaluate over-the-air performance of cooperative NOMA. The results indicate that relay-cooperative NOMA outperforms non-cooperative NOMA and yields significant performance gains. [ABSTRACT FROM AUTHOR]

With increasing vehicle density and the growth of accidents in urban areas, navigation management becomes a serious problem. Even though there is a multitude of navigation systems, ambulances, taxis, or even ordinary vehicles, sometimes find it challenging to reach their destinations on time. There are two main reasons for this difficulty: (i) lack of local knowledge of the area of navigation solutions and (ii) their inflexibility against unforeseeable situations that may occur on the roads. Indeed, the majority of navigation solutions are based only on the distance, the journey time, or even statistics related to the density of vehicles to plan the full paths, while neglecting the dynamic nature of the vehicle traffic. Also, their respective centralized architectures are unable to monitor both the traffic and unexpected events continuously and in real-time without not being overloaded by the flow of message exchanges between the road entities and the central processing entity. To address these issues, we propose in this paper a novel three-tier architecture, called SDN-enabled Approach for Vehicle Path-Planning (SEARCH), to enhance the situation awareness on urban roads, efficiently collect traffic information in real-time, and decide the best navigation strategy. The proposed architecture exploits Unmanned Aerial Vehicles (UAVs), Vehicular Ad hoc Networks (VANETs), 5G based cellular systems, and Software-Defined Networking (SDN) to provide better and faster communication to changing road conditions. Based on these technologies, some parameters related to vehicles and driving environments, such as speed, distance, traffic jams, incidents, and travel flow, are efficiently collected and dynamically exploited to achieve faster paths between any existing pairs of locations. Furthermore, the deployed architecture of SEARCH can provide sufficient bandwidth to support all data traffic needs to update vehicles during their journey efficiently and in real-time. To evaluate the performance of our architecture, we conduct a series of simulations and perform a set of comparisons with relevant route planning algorithms. We found that the proposed architecture works effectively in terms of saving on driving time to reach any target destinations. [ABSTRACT FROM AUTHOR]

Due to dense deployments of Internet of things (IoT) networks, interference management becomes a critical challenge. With the proliferation of aerial IoT devices, such as unmanned aerial vehicles (UAVs), interference characteristics in 3D environments will be different than those in the existing terrestrial IoT networks. In this paper, we consider 3D topology IoT networks with a mixture of aerial and terrestrial links, with low-cost cross-dipole antennas at ground nodes and both omni-directional and cross-dipole antennas at aerial nodes. Considering a massive-access communication scenario, we first derive the statistics of the channel gain at IoT receivers in closed form while taking into account the radiation patterns of both ground and aerial nodes. These are then used to calculate the ergodic achievable rate as a function of the height of the aerial receiver and the cumulative interference. We propose a low-complexity interference mitigation scheme that utilizes 3D antenna radiation pattern with different dipole antenna settings. Our results show that using the proposed scheme, the ergodic achievable rate improves as the height of the aerial receivers increases. In addition, we also show that the ratio between the ground and aerial receivers that maximizes the peak rate increases with the height of the aerial IoT receiver. [ABSTRACT FROM AUTHOR]

This paper describes the calibration of the Volcano ray-tracing engine against channel measurements collected in an urban environment with a state-of-the-art 28-GHz directional channel sounder. A discrete set of rays, representing planar wavefronts propagating between the transmitter and receiver, were extracted from the measurements and characterized in path gain, delay, and 3D angle-of-arrival through super-resolution techniques, with average errors of only 1.2 dB, 0.55 ns, and 2.05° respectively. The extracted rays were then tracked over space as the receiver, mounted on a mobile rover equipped with military-grade GPS, traversed 66 m while amassing a total of 488 channel acquisitions. The tracked rays were then mapped to rays predicted from ray-tracing, originating through specular reflection or diffuse scattering from ambient objects. The mapping enabled object-specific calibration, namely calibrating distinct diffuse-scattering models for buildings, vehicles, and foliage. To our knowledge, this is the first effort to calibrate ray-tracing with object-specific diffuse scattering models against rays individually mapped in the path gain, delay, angle, and space domains. Results, in terms of calibrated ray-tracing parameters, fit-error statistics, and lessons learned, are included. Our chief finding was that, while most papers on millimeter-wave ray-tracing do not even consider diffuse scattering, it accounted for 20% of the total received power, whereas diffraction accounted for less than 1%. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient downlink precoding scheme in a multi-cell Massive MIMO system. We approach the precoder design problem to maximize the system energy efficiency by jointly considering power control, interference management, antenna switching and user throughput in a cluster of base stations. This is computationally difficult as it requires solving a sparsity-inducing non-convex optimization problem, which is NP-hard. To alleviate the solution complexity, first a stochastic smooth approximation of zero-norm is applied in the antenna power management to enable fast, gradient-based algorithms. For efficient convergence, we develop a novel optimization algorithm combining augmented multiplier (AM) and quadratic programming (QP), and show how this scheme permits decentralized implementation by offloading parts of the computation to the individual base stations to reduce communication overhead. We provide theoretical proof that the proposed algorithm converges both locally and globally under realistic assumptions. Numerical results confirm that our method achieves higher energy efficiency with a superior convergence rate compared to different types of existing methods, and illustrate the relationship between energy efficiency performance and system design parameters. [ABSTRACT FROM AUTHOR]

This paper presents a novel efficient receiver design for wireless communication systems that incorporate orthogonal frequency division multiplexing (OFDM) transmission. The proposed receiver does not require channel estimation or equalization to perform coherent data detection. Instead, channel estimation, equalization, and data detection are combined into a single operation, and hence, the detector performs a direct data detector (D3). The D3 is applied to key practical wireless systems such as the Long Term Evolution (LTE) and the New Radio (NR) of the fifth-generation (5G) system. The performance of the proposed D3 is thoroughly analyzed theoretically in terms of bit error rate (BER), where closed-form accurate approximations are derived for several cases of interest, and validated by Monte Carlo simulations. Moreover, extensive complexity analysis are performed to evaluate the system suitability for implementation. The obtained theoretical and simulation results demonstrate that the BER of the proposed D3 is only 3 dB away from coherent detectors with perfect knowledge of the channel state information (CSI) in flat and frequency-selective fading channels for a wide range of signal-to-noise ratios (SNRs). If CSI is not known perfectly, then D3 outperforms the coherent detector substantially, particularly at high SNRs with linear interpolation. The computational complexity of D3 depends on the length of the sequence to be detected, nevertheless, a significant complexity reduction can be achieved using the Viterbi algorithm. [ABSTRACT FROM AUTHOR]

The integration of satellite communications into 5G ecosystem is pivotal to boost enhanced mobile broadband (eMBB) services in highly dynamic scenarios and in areas not optimally supported by terrestrial infrastructures. Given the heterogeneity of the networks involved, network slicing is key networking paradigm to ensure different grades of quality of service (QoS) based on the users’ and verticals’ requirements. In this light, this paper proposes an optimisation framework able to exploit the available resources allocated to the defined network slices so as to meet the diverse QoS/QoE requirements exposed by the network actors. Resource allocation schemes built upon neural network algorithms are validated through extensive simulation campaigns that have shown the superiority of the proposed concepts with respect to other solution candidates available from the literature. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) has been widely considered as a promising technology in the next generation wireless communication systems. Various beam-based directional transmission techniques have been proposed to compensate high pathloss caused by mmWave properties. However, mobility effect of users potentially causes severe inter-beam interferences, which has not been fully-investigated in existing studies. In this paper, we have analyzed different types of mobility-aware beam interference models. Additionally, the theoretical SINR is derived based on channel statistics and the trajectories of users. We propose a mobility-aware subband and beam resource allocation (MSBA) scheme for the mmWave subband-beam massive multi-input multi-output (SB-MMIMO) systems. The proposed MSBA benefits from reduced computation complexity for maximizing system throughput constrained by quality-of-service (QoS) demands of users. The first phase of MSBA scheme is responsible for resource block assignment; while the second phase allocates beamwidth and beam directions according to the analytical derivations. Numerical results show that the proposed MSBA scheme can effectively achieve the highest system throughput and the lowest complexity compared to existing schemes in open literatures. [ABSTRACT FROM AUTHOR]

In recent years, intelligent transportation systems are a vital part of the development of smart cities. In intelligent transportation systems, it is necessary for each vehicle to know the states of all other running vehicles within a range, depends on the road speed limit. The states include location, travelling direction, speed, and possibly other useful information. Knowing the states of surrounding vehicles is critical in high-risk locations, such as traffic intersections and roundabouts. The development of 5G and its technologies, such as massive multiple-input multiple-output antenna, enables a new communication model, termed vehicle-to-infrastructure edge network (V2IEN), to achieve the above communication goal. In this paper, we study the optimization of the data throughput of the proposed V2IENs in 5G with the Time Division Duplex scheme. We show that due to the nature of information flow in the proposed V2IENs, the uplink and downlink traffic loads are asymmetric. This asymmetric characteristic allows maximizing the degrees of freedom of the V2IENs by optimizing the time-slot resources for uplink and downlink transmission. In this work, we present the sum-DoF capacity of the V2IENs with proof. We demonstrate that a significant DoF gain is achieved for the V2IENs by carefully allocate the system transmission time resources. We further propose an iterative algorithm for network sum-rate maximization via the optimization of the vehicle and base station precoders. Numerical results demonstrate that a careful design of the precoders at the base station and at the vehicles can considerably improve the V2IENs performance. [ABSTRACT FROM AUTHOR]

The third generation partnership project (3GPP) has issued specifications for the autonomous assignment of radio resources by vehicles on the sidelink of the cellular-vehicle-to-everything (C-V2X) technology. It is based on a sensing mechanism for resource selection and a semi-persistent scheduling for resource reservation to periodic safety messages. Imperfect sensing due to hidden terminals and to half-duplex on board transceivers may result in the selection of interfered resources for successive message transmissions. As a consequence of the lost packets, involved vehicles may become blind to the presence of other vehicles in their vicinity even for many seconds, with threats to the road safety. In this paper, we define these events as wireless blind spots (WBSs) and characterize their probability to occur. We propose an enhancement to the autonomous mode in order to reduce the WBS duration and demonstrate the benefits of the proposal against the legacy mode, both analytically in a simplified scenario and through simulations in a highway environment. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of parameter estimation for a Fluctuating Two-Ray (FTR) model in the context of wireless mobile communications. Precisely, the received signal is assumed to be the superposition of two dominant components (typically a direct plus a reflected path signal) in addition to diffusive secondary contributions. The signal components may be affected by random amplitude shadowing, statistically modeled by Nakagami- $m$ distribution, multiplied by unknown scaling factors with random uniform independent phases, whereas the diffusive component is assumed to follow the complex Gaussian distribution. Exploiting the method of moments, a $4\times 4$ nonlinear system is herein mathematically derived, which is very hard to be solved due to the strong nonlinearity. Therefore, a sequential procedure based on some prior information about the diffusive component power level is devised to solve it. The effectiveness of the proposed estimation technique is shown by evaluating the normalized root mean square errors as well as mean errors and standard deviations in several operating conditions of practical interest also considering the limit case of only one-ray in order to compare the proposed approach to simpler estimators, already presented in the literature. The results show the robustness of the new estimator even under a multipath model mismatch. Finally, the effectiveness of the proposed estimation procedure is confirmed through measured mmWave data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a system model for cellular vehicle-to-everything (C-V2X) networks mode-3, with two user groups: cellular users and vehicular users. Cellular users communicate with each other through the base station (BS). Vehicular users communicate through direct vehicular links while sharing the spectrum of an orthogonal frequency division multiplexing (OFDM) system with cellular users. In particular, we focus on the underlay approach, in which vehicular users can transmit at the same time in a sub-carrier that is being used by a cellular user. Assuming a limited number of sub-carriers, the number of allowed vehicular links is limited by the quality of service (QoS) requirement for cellular users because of the interference from vehicular links. Our goal is to maximize the number of vehicular links while satisfying the QoS for cellular users. To achieve this goal, we state the optimization problem of resource allocation in C-V2X networks mode-3, assuming high mobility of vehicular users, which results in Doppler effect for them. We also assume that the global channel state information (CSI) of mobile links is not available for the BS. We propose a novel scheme for the BS to jointly allocate resources, such as power and sub-carriers, to cellular and vehicular users, while minimizing the number of non-allowed vehicular links, considering high data rate requirements for cellular users and high reliability requirements for vehicular users. Using numerical results, we obtain the maximum number of vehicular links that can be established for a given available bandwidth and cellular user distribution. [ABSTRACT FROM AUTHOR]

Vehicular networks provide various applications for vehicles to improve road safety and traffic efficiency as well as infotainment. Security and privacy are of great importance for the deployment of vehicular networks. However, how to efficiently secure vehicular networks with privacy-preserving remains a big challenge. Many solutions have been proposed in the past decade, but most of them either rely too much on the ideal tamper-proofed devices or not being efficient enough for scenarios with high vehicle density. Meanwhile, in recent years, great progresses have been made in both the Long-Term Evolution (LTE) and the fifth-generation (5G) wireless network based vehicular networks. The 5G enabled vehicular networks are envisioned to support higher data transmission rate and a larger number of connected devices than the 802.11p and LTE based networks. Noting the great potential of 5G technology, in this paper we explore a new architecture of 5G software defined vehicular network and propose a secure and efficient privacy preserving authentication scheme for vehicular networks. The proposed scheme uses elliptic-curve public-key cryptography and a registration list to achieve efficient message authentication and to avoid the usage of ever-growing certificate revocation list. The security analysis shows that the proposed scheme has strong security guarantees without using the ideal tamper-proofed devices. Simulation results exhibit that the proposed scheme has ultra low computational overhead and packet loss ratio. [ABSTRACT FROM AUTHOR]

Ultra-reliable and low-latency communications (URLLC) partakes a major role in 5 G networks for mission-critical applications. Sparse vector coding (SVC) appears as a strong candidate for future URLLC networks by enabling superior performance in terms of bit error rate (BER). SVC exploits the virtual digital domain (VDD) and compressed sensing (CS) algorithms to encode and decode its information through active symbol indices. In this paper, first, a clever encoding/decoding algorithm is proposed for the SVC scheme, which allows the use of all possible activation patterns (APs) resulting in increasing spectral efficiency. Second, a novel solution is proposed to convey additional information bits by further exploiting index modulation (IM) for the codebooks of the SVC scheme. Computer simulation results reveal that our low-complexity algorithm and novel IM solution provide not only a superior BER performance but also an increase in the number of bits conveyed by IM compared to the ordinary SVC approach. [ABSTRACT FROM AUTHOR]

We develop a novel technique to accelerate minorization-maximization (MM) procedure for the non-orthogonal multiple access (NOMA) weighted sum rate maximization problem. Specifically, we exploit the Lipschitz continuity of the gradient of the objective function to adaptively update the MM algorithm. With fewer additional analysis variables and low complexity second-order cone program (SOCP) to solve in each iteration of the MM algorithm, the proposed approach converges quickly at a small computational cost. By numerical simulation results, our algorithm is shown to greatly outperform known solutions in terms of achieved sum rates and computational complexity. [ABSTRACT FROM AUTHOR]

Recently, millimeter-wave (mmWave) 5G localization has been shown to be to provide centimeter-level accuracy, lending itself to many location-aware applications, e.g., connected autonomous vehicles (CAVs). One assumption usually made in the investigation of localization methods is that the user equipment (UE), i.e., a CAV, and the base station (BS) are time synchronized. In this paper, we remove this assumption and investigate two two-way localization protocols: (i) a round-trip localization protocol (RLP), whereby the BS and UE exchange signals in two rounds of transmission and then localization is achieved using the signal received in the second round; (ii) a collaborative localization protocol (CLP), whereby localization is achieved using the signals received in the two rounds. We derive the position and orientation error bounds applying beamforming at both ends and compare them to the traditional one-way localization. Our results show that mmWave localization is mainly limited by the angular rather than the temporal estimation and that CLP significantly outperforms RLP. Our simulations also show that it is more beneficial to have more antennas at the BS than at the UE. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communication is a key enabler that connects vehicles to neighboring vehicles, infrastructure and pedestrians. In the past few years, multimedia services have seen an enormous growth and it is expected to increase as more devices will utilize infotainment services in the future i.e. vehicular devices. Therefore, it is important to focus on user centric measures i.e. quality-of-experience (QoE) such as video quality (resolution) and fluctuations therein. In this paper, a novel joint video quality selection and resource allocation technique is proposed for increasing the QoE of vehicular devices. The proposed approach exploits the queuing dynamics and channel states of vehicular devices, to maximize the QoE while ensuring seamless video playback at the end users with high probability. The network wide QoE maximization problem is decoupled into two subparts. First, a network slicing based clustering algorithm is applied to partition the vehicles into multiple logical networks. Secondly, vehicle scheduling and quality selection is formulated as a stochastic optimization problem which is solved using the Lyapunov drift plus penalty method. Numerical results show that the proposed algorithm ensures high video quality experience compared to the baseline. Simulation results also show that the proposed technique achieves low latency and high-reliability communication. [ABSTRACT FROM AUTHOR]

As an essential part of vehicle networks, the Vehicle to Infrastructure (V2I) needs the support of millimeter wave and massive MIMO technologies to enable high data rate applications, such as automated driving, real-time high-quality multimedia services and so on. As the scale of the antenna array increases, the complexity of the beamforming and channel estimation algorithms under high mobility conditions also increases significantly. In particular, highly robust beamforming methods need to cope with fast changing transmission environments. In this paper, we adopt a biological inspired self-adaptive selection algorithm called attractor selection algorithm (ASA) to support uplink beamforming. The ASA requires only a little feedback information from the Road Side Infrastructure (RSI) to perform fast beam training, hence making the transmission link more stable. The simulation results indicate that the proposed ASA-assisted algorithm can significantly reduce the time required to achieve a timely beam training, which would be essential for V2I high communications under high mobility conditions. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) antennas for smartphones are one of the key components to complete the transition to 5G mobile networks. Although research and development of mmWave 5G antennas for cellular handsets are currently at the center of a significant research effort in both academia and telecommunication industry, a systematic antenna design approved by wireless community has not been proposed yet. With this communication, we propose a novel, high gain, wide band and compact mmWave 5G antenna, namely clover antenna for cellular handsets. The presented antenna has clover like conductor profile whose parameters can be adjusted to obtain high gain or wide band. The designed antennas are simulated with a widely used full-wave analysis tool. Numerical results of the mmWave antenna are confirmed successfully by the experimental results in ${{\text{24}}}$ – ${\text{28}}$  GHz band. The antenna achieves measured peak gain of ${\text{ 7.8}}$ – ${\text{9}}$  dBi in the band. Besides, with a ${\text{16}}$ -element clover antenna array, the beam steering capability of the antenna is demonstrated. Beam steering between ${{ \pm \text{45}^\circ }}$ is achieved with low side lobe levels. Practical design considerations for the integration of the arrays in handset to obtain full-coverage in horizontal plane are investigated. The calculated spatial peak power density values of the proposed array on the outer surface of a head phantom are demonstrated for different scan angles. [ABSTRACT FROM AUTHOR]

Load imbalance among small and macro cells is a major challenge that undermines the gains of emerging ultradense heterogeneous networks (HetNets). Existing load balancing (LB) schemes have one common caveat which is operating in reactive mode i.e., cell parameters are tweaked reactively in accordance with the dynamics of cell loads. The inherent reactiveness of these LB schemes hinder in achieving promising quality of experience (QoE) gains from 5G and beyond. To cope with this issue, in this paper we propose a novel proactive load balancing framework “OPERA” empowered by mobility prediction paradigm for future ultra dense networks (UDNs). The pro-activeness of OPERA stems from its novel capability that instead of passively waiting for congestion indicators to be observed and then reacting to them, OPERA predicts future cell loads and then proactively optimizes key antenna parameters and cell individual offsets (CIOs) to preempt congestion before it happens. OPERA also incorporates capacity and coverage constraints and load aware association strategy for ensuring conflict free operation of LB and coverage and capacity optimization (CCO) self-organizing network (SON) functions. Simulation results show that compared to real network deployments settings and published state-of-the-art reactive schemes, OPERA can yield significant gain in terms of fairness in load distribution and percentage of satisfied users. Superior performance of OPERA on several fronts compared to current schemes stems from its following features: 1) It preempts congestion instead of reacting to it; 2) it actuates more parameters than any current LB schemes thereby increasing system level capacity instead of just shifting it among cells; 3) while performing LB OPERA simultaneously maximizes residual capacity while incorporating throughput and coverage constraints; 4) it incorporates a load aware association strategy for ensuring conflict free operation of LB and CCO SON functions; 5) the ahead of time estimation of cell loads allows ample time for heuristics search algorithms to find LB solutions with high gain. [ABSTRACT FROM AUTHOR]