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Energy efficiency is regarded as a critical concern in modern cellular networks, primarily due to the increasing demand for wireless communication services and the environmental impact associated with energy consumption. In this paper, an innovative approach named ACSO (Adaptive Cell Switch Off) is proposed to optimize energy efficiency in cellular networks. It focusses on leveraging clustering techniques and dynamic CSO (Cell Switch Off) strategies. In this approach, clustering of BSs (Base Station) is performed based on their traffic profiles, and within each cluster, an optimal set of BSs is determined to be switched off at different time intervals. By considering the characteristics of network traffic and spatial distribution, an attempt is made to strike a balance between energy conservation and the maintenance of high QoE (Quality of Experience) for users. Extensive simulations using a real-world dataset are conducted to compare our approach with existing methods, and superior performance is demonstrated in terms of efficient BS selection, algorithm execution time, and user QoE. Additionally, potential future research directions in the area of dynamic CSO are discussed, along with its implications for the development of energy-efficient cellular networks. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs' side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage. [ABSTRACT FROM AUTHOR]

The advances made in wireless communication technology have led to efforts to improve the quality of reception, prevent poor connections and avoid disconnections between wireless and cellular devices. One of the most important steps toward preventing communication failures is to correctly estimate the received signal strength indicator (RSSI) of a wireless device. RSSI prediction is important for addressing various challenges such as localization, power control, link quality estimation, terminal connectivity estimation, and handover decisions. In this study, we compare different machine learning (ML) techniques that can be used to predict the received signal strength values of a device, given the received signal strength values of other devices in the region. We consider various ML methods, such as multi-layer ANN, K nearest neighbors, decision trees, random forest, and the K-means based method, for the prediction challenge. We checked the accuracy level of the learning process using a real dataset provided by a major national cellular operator. Our results show that the weighted K nearest neighbors algorithm, for K = 3 neighbors, achieved, on average, the most accurate RSSI predictions. We conclude that in environments where the size of data is relatively small, and data of close geographical points is available, a method that predicts the coverage of a point using the coverage near geographical points can be more successful and more accurate compared with other ML methods. [ABSTRACT FROM AUTHOR]

Recent advancement in processing power, mobile wireless networks enabled technology are leading to more researches and studies, investigating mobility prediction in wireless network environments. User mobility behavior knowledge is a key element to understand the network traffic behavior in today mobile applications, including context-aware advertising and city wide sensing applications. We extend our previous work that was dedicated to mobility prediction schemes, in order to cover a correlated research topic: study of the user mobility behavior. [ABSTRACT FROM AUTHOR]

Resource allocation (RA) has always had a prominent place in wireless communications research due to its significance for network throughput maximization, and its inherent complexity. Concurrently, graph-based solutions for RA have also grown in importance, providing opportunities for higher throughput and efficiency due to their representational capabilities, as well as challenges for realizing scalable algorithms. This article presents a comprehensive review and analysis of graph-based RA methods in three major wireless network types: cellular homogeneous and heterogeneous, device-to-device, and cognitive radio networks. The main design characteristics, as well as directions for future research, are provided for each of these categories. On the basis of this review, the concept of Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) is proposed. It describes the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks via a hypergraph and its attributes. In addition, the implementation challenges of GRIST are explained in detail. Finally, to complement GRIST, a scheme for determining the appropriate balance between different design considerations is introduced. It is described via a simplified complete graph-based design process for resource management algorithms. [ABSTRACT FROM AUTHOR]

This work performs the statistical QoS analysis of a Rician block-fading reconfigurable intelligent surface (RIS)-assisted D2D link in which the transmit node operates under delay QoS constraints. First, we perform mode selection for the D2D link, in which the D2D pair can either communicate directly by relaying data from RISs or through a base station (BS). Next, we provide closed-form expressions for the effective capacity (EC) of the RIS-assisted D2D link. When channel state information at the transmitter (CSIT) is available, the transmit D2D node communicates with the variable rate $r_{t}(n)$ (adjustable according to the channel conditions); otherwise, it uses a fixed rate $r_{t}$. It allows us to model the RIS-assisted D2D link as a Markov system in both cases. We also extend our analysis to overlay and underlay D2D settings. To improve the throughput of the RIS-assisted D2D link when CSIT is unknown, we use the HARQ retransmission scheme and provide the EC analysis of the HARQ-enabled RIS-assisted D2D link. Finally, simulation results demonstrate that: i) the EC increases with an increase in RIS elements, ii) the EC decreases when strict QoS constraints are imposed at the transmit node, iii) the EC decreases with an increase in the variance of the path loss estimation error, iv) the EC increases with an increase in the probability of ON states, v) EC increases by using HARQ when CSIT is unknown, and it can reach up to $5\times$ the usual EC (with no HARQ and without CSIT) by using the optimal number of retransmissions. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication has been claimed to be an effective technique for services planned in next generation cellular networks (5G and 5G+). Effective 3D deployments of BS mounted on drone (drone-BS), in the Area of Interest (AoI) may increase the Quality-of-Service (QoS) of wireless communication in the Internet of Things (IoT). In this article, we solve a dynamic deployment problem (location optimization) of multiple drone-BSs that is NP-hard according to the Air-to-Ground (ATG) model using optimization algorithms with the aim of ensuring optimal rates of coverage of the ground users (user equipments) assumed to be located at certain distance intervals in a defined urban environment. In this regard, we have developed Optimal Fitness-value Search Approaches at Continuous-data range (OFSAC-PSO and OFSAC-EML) and Optimal Fitness-value Search Approaches at Discrete-data range (OFSAD-PSO and OFSAD-EML), which are both based on Particle Swarm Optimization (PSO) and Electromagnetism-Like (EML) algorithm. In the study where Monte-Carlo simulations are run, we have created different scenarios for uniform and non-uniform distributions of user equipments (UEs). We have made comparisons according to the following metrics for the approaches that we have developed: the fitness function values of drone-BSs, AoI coverage rates, the number of iteration of simulations, drone-BS altitudes, optimal 2D coverage map, and the 3D optimal locations of drone-BSs. Our simulation results show that, based on the compared metrics, OFSAC developed with PSO-based is optimal compared to other approaches. [ABSTRACT FROM AUTHOR]

Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

Reconfigurable intelligent surface (RIS) technology is promising to enhance wireless communications services by providing smart radio environment. In this paper, we investigate the RIS aided cellular networks with device-to-device (D2D) users, and maximize the sum of the transmission rate of the D2D communications and the cellular networks from a new perspective. In addition to solving the typical resource allocation problems for D2D communications, this paper further optimize the wireless environment by adjusting the position and phase shift of the RIS. To solve this non-convex problem, we propose a novel decentralized double deep Q-network (D3QN) framework for the resource allocation at users and a centralized DDQN for RIS optimization at the base station (BS), which are verified to achieve the near-optimal performance with lower complexity and enhanced robustness. Simulation results illustrate that the proposed framework can achieve higher transmission rates compared to benchmarks, meanwhile meeting the quality of service (QoS) requirements at the BS and D2D users. [ABSTRACT FROM AUTHOR]

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work. [ABSTRACT FROM AUTHOR]

It has been a challenging task to derive the complementary cumulative distribution function (CCDF) of aggregate interference as closed-form in Poisson point process (PPP). In this paper, we consider wireless communication networks where transmitters are all distributed according to homogeneous PPP and study the CCDF of the interference. Mainly, we derive generalized bounds on the CCDF of aggregate interference from whole homogeneous PPP based on discretization of interference, which considerably enhance tightness compared to the previous bounds. These bounds can be used to the analysis requiring the distribution for aggregate interference, such as deployment and activation of BSs, massive MIMO, and millimeter wave communication networks. As examples of utilizing the derived bounds, we derive tractable coverage probabilities of uplink non-orthogonal multiple access (NOMA) users and downlink users in cellular networks under line-of-sight(LoS)/non-line-of-signt (NLOS) channels, respectively. In NOMA, we derive the closed-form bounds on coverage probability when successive interference cancellation is adopted for NOMA signal demodulation. Using the proposed bounds, we also obtain the bounds on coverage probability of a cellular system with LoS/NLOS channels. The tightness of bounds in this work is shown via numerical comparison. Moreover, we mathematically prove the convergence of bounds to exact CCDF. [ABSTRACT FROM AUTHOR]

This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication is emerging as an effective technique that should be used for the services planned in the next-generation cellular networks or in disaster situations. Currently, effective 3D placement of BS mounted on UAVs or Drones (Drone-BS), also called Low-altitude Air Platforms, in the defined area significantly increases the Quality of Service (QoS) of wireless communication. This study has been aimed to be performed dynamic deployments (location optimization) of Drone-BSs randomly distributed in the 3D plane in order to optimally cover the users in the urban environment according to the Air-to-Ground (ATG) model. Using new approaches based on the Electromagnetism-Like (EML) Algorithm and the Whale Optimization Algorithm (WOA), which are widely used in the literature and are meta-heuristic, it was planned to be covered the maximum number of users on the ground by multiple Drone-BSs. In addition, Extensive-interval Optimal Fitness Search Algorithm (EOFSA-EML) and Discrete-interval Optimal Fitness Search Algorithm (DOFSAEML) approaches were developed based on the EML algorithm. Based on comparison metrics for multiple Drone-BSs distributed by EOFSA-EML, DOFSA-EML, and Pure-WOA, it has been found that EML-based algorithms achieve optimal results compared to Pure-WOA. [ABSTRACT FROM AUTHOR]

Wireless communication over terahertz (THz) frequency bands is envisioned as the key enabler of many applications and services offered in 6G networks. The abundantly available bandwidth in THz frequencies can satisfy the ultra-high user throughput requirements and accommodate a massive number of connected devices. However, poor propagation characteristics, shadowing, and blockages may result in sudden outages and necessitate frequent handovers. Therefore, an inefficient handover procedure will impose severe challenges in meeting the ultra-high reliability and low latency requirements of emerging applications. In blockage driven mmWave and THz networks, a higher multi-connectivity degree and efficient handover procedures are needed to reduce the data plane interruptions and to achieve high reliability. We present an analytical model to study the impact of handover procedures and multi-connectivity degree on the latency and reliability of blockage driven wireless networks. From the network protocol design perspective, our study offers a quick and accurate way to envisage how network architecture and protocols should evolve in terms of multi-connectivity degrees and handover procedural efficiency. Our results suggest that, for THz systems, coverage range should be increased even if it comes at the cost of increased initial access and base station discovery times. [ABSTRACT FROM AUTHOR]

This paper aims to characterize the capacity limits of a wireless cellular network with a full-duplex (FD) base-station (BS) and half-duplex user terminals, in which three independent messages are communicated: the uplink message m1 from the uplink user to the BS, the downlink message m2 from the BS to the downlink user, and the device-to-device (D2D) message m3 from the uplink user to the downlink user. From an information theoretical perspective, the overall network can be viewed as a generalization of the FD relay broadcast channel with a side message transmitted from the relay to the destination. We begin with a simpler case that involves the uplink and downlink transmissions of (m1, m2) only, and propose an achievable rate region based on a novel strategy that uses the BS as a FD relay to facilitate the interference cancellation at the downlink user. We also prove a new converse, which is strictly tighter than the cut-set bound, and characterize the capacity region of the scalar Gaussian FD network without a D2D message to within a constant gap. This paper further studies a general setup wherein (m1, m2, m3) are communicated simultaneously. To account for the D2D message, we incorporate Marton’s broadcast coding into the previous scheme to obtain a larger achievable rate region than the existing ones in the literature. We also improve the cut-set bound by means of genie and show that by using one of the two simple rate-splitting schemes, the capacity region of the scalar Gaussian FD network with a D2D message can already be reached to within a constant gap. Finally, a generalization to the vector Gaussian channel case is discussed. Simulation results demonstrate the advantage of using the BS as relay in enhancing the throughput of the FD cellular network. [ABSTRACT FROM AUTHOR]

The large spectral bandwidth at millimeter-wave (mmWave) frequencies provides a mean to achieve very high data rates in wireless communication systems. A unique characteristic of mmWave is that mmWave links are very sensitive to blockage and have large propagation path loss, which exhibits low line-of-sight (LoS) probability, unstable connectivity and unreliable communication. This paper studies the influence of the blockage in pedestrian scenario, explains in detail how blockage affects the mmWave propagation characteristics. In particular, we study the behavior of the blockage due to human mobility and how it affects the timescale for outage due to blockage using knife-edge diffraction model (KED). One of the existing solutions to overcome the influence of blockage, is to associate the user equipment (UE) with other available base stations (BSs) by handover (HO) if the serving BS is blocked. In this paper, for a pedestrian scenario, we propose two reinforcement learning (RL) based user association algorithms, which accounts for the past experience of the blockage on the position of the UE. One focuses on the reward to increase the sum LoS probability and is named as blockage-aware user association (BAUA). The other focuses on the reward to balance the trade-off between the throughput and the LoS probability and is named as modified BAUA. We compare the proposed algorithm with the conventional user algorithms such as the maximum throughput based algorithm and the maximum SINR based algorithm. Simulation results show that to increase the sum LoS probability BAUA would be suitable, and to increase the average throughput maximum throughput based method would be suitable. [ABSTRACT FROM AUTHOR]

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

With the assistance of device-to-device (D2D) communications, unmanned aerial vehicle (UAV) networks are anticipated to support widespread applications in fifth generation (5G) and beyond wireless systems, by providing seamless coverage, flexible deployment, and high channel rate. However, the networks face significant security threats from malicious eavesdroppers due to the inherent broadcast and openness nature of wireless channels. To ensure secure communications of such networks, physical layer security is a promising technique, which utilizes the randomness and noise of wireless channels to enhance secrecy performance. This article investigates physical layer security performance via spectrum sharing in D2D-enabled UAV networks. We first present two typical network architectures where each UAV serves as either a flying base station or an aerial user equipment. Then, we propose a spectrum sharing strategy to fully exploit interference incurred by spectrum reuse for improving secrecy performance. We further conduct two case studies to evaluate the spectrum sharing strategy in these two typical network architectures, and also show secrecy performance gains compared to traditional spectrum sharing strategy. Finally, we discuss some future research directions in D2D-enabled UAV networks. [ABSTRACT FROM AUTHOR]

Reliable and efficient networks are the trend for next-generation wireless communications. Recent improved hardware technologies – known as Large Intelligent Surfaces (LISs) – have decreased the energy consumption of wireless networks, while theoretically being capable of offering an unprecedented boost to the data rates and energy efficiency (EE). In this paper, we use stochastic geometry to provide performance analysis of a realistic two-step user association based millimeter wave (mmWave) networks consisting of multiple users, transmitters and one-hop reflection from a LIS. All the base stations (BSs), users and LISs are equipped with multiple uniform linear antenna arrays. The results confirm that LIS-assisted networks significantly enhance capacity and achieve higher optimal EE as compared to traditional systems when the density of BSs is not large. Moreover, there is a trade-off between the densities of LIS and BS when there is a total density constraint. It is shown that the LISs are excellent supplements for traditional cellular networks, which enormously enhance the average rate and area spectral efficiency (ASE) of mmWave networks. However, when the BS density is higher than the LIS density, the reflected interference and phase-shift energy consumption will limit the performance of LIS-assisted networks, so it is not necessary to employ the LIS devices. [ABSTRACT FROM AUTHOR]

This work investigates the degrees of freedom (DoF) of a downlink cache-aided cellular network where the locations of base stations (BSs) are modeled as a grid topology and users within a grid cell can only communicate with four nearby BSs. We adopt a cache placement method with uncoded prefetching tailored for the network with partial connectivity. According to the overlapped degree of cached contents among BSs, we propose transmission schemes with no BS cooperation, partial BS cooperation, and full BS cooperation, respectively, for different cache sizes. In specific, the common cached contents among BSs are utilized to cancel some undesired signals by interference neutralization while interference alignment is used to coordinate signals of distinct cached contents. Our achievable results indicate that the reciprocal of per-user DoF of the cellular network decreases piecewise linearly with the normalized cache size $\mu $ at each BS, and the gain of BS caching is more significant for the small cache region. Under the given cache placement scheme, we also provide an upper bound of per-user DoF and show that our achievable DoF is optimal when $\mu \in \left [{\frac {1}{2},1}\right]$ , and within an additive gap of $\frac {4}{39}$ to the optimum when $\mu \in \left [{\frac {1}{4},\frac {1}{2}}\right)$. [ABSTRACT FROM AUTHOR]

With the proliferation of portable devices, the exponential growth of the global mobile traffic brings great challenges to the traditional communication networks and the traditional wireless communication technologies. In this context, converged networks and cache-based data offloading have drawn more and more attention based on the strong correlation of services. This paper proposes a novel popular services pushing and caching scheme by using converged overlay networks. The most popular services are pushed by terrestrial broadcasting networks. And they are cached in ${n}$ router-nodes with limited cache sizes. Each router-node only interconnects with its neighbor nodes. Users are served through the router’s WiFi link. If the services requested are cached in the routers, the user can be immediately responded; otherwise, the requests can be responded through the link from cellular stations to the router. In the proposed scheme, the cache size of the router, the maximum number of requests each router can serve, and the whole-time delay are limited. Three node-selecting and dynamic programming algorithms are adopted to maximize the equivalent throughput. Analytical and numerical results demonstrate that the proposed scheme is very effective. [ABSTRACT FROM AUTHOR]

The highly dynamic wireless communication environment poses a challenge for many applications (e.g., adaptive multimedia streaming services). Providing accurate TP can significantly improve performance of these applications. The scheduling algorithms in cellular networks consider various PHY metrics, (e.g., CQI) and throughput history when assigning resources for each user. This article explains how AI can be leveraged for accurate TP in cellular networks using PHY and application layer metrics. We present key architectural components and implementation options, illustrating their advantages and limitations. We also highlight key design choices and investigate their impact on prediction accuracy using real data. We believe this is the first study that examines the impact of integrating network-level data and applying a deep learning technique (on PHY and application data) for TP in cellular systems. Using video streaming as a use case, we illustrate how accurate TP improves the end user's QoE. Furthermore, we identify open questions and research challenges in the area of AI-driven TP. Finally, we report on lessons learned and provide conclusions that we believe will be useful to network practitioners seeking to apply AI. [ABSTRACT FROM AUTHOR]

Inter-cell interference is a major issue for next-generation wireless cellular networks, due to the increased user mobility, high user density, and backhaul bandwidth constraint. In this work, a unified approach is proposed to address these challenges. The proposal firstly performs a per-cell signal spreading by Zadoff-Chu (ZC) sequence, then the spread signal is precoded by a modified coordinated beamforming scheme. The proposal is robust to Doppler caused by user mobility, supports a high co-channel user capacity for ultra-dense networks, and requires only limited backhaul signaling exchange between basestations thus being suitable for massive MIMO deployment. The advantages of the proposal are firstly analyzed in theory, then evaluation results are presented to validate the proposal for MIMO and massive MIMO setups. It is found that the proposed scheme consistently outperforms the traditional scheme under various scenarios. The practical issues related to synchronization in coordinated beamforming are discussed. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs’ communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research. [ABSTRACT FROM AUTHOR]

Dissemination of wireless networks and mobile devices, such as smartphones, has motivated the appearance of several types of location-based services. Consequently, the interest in low-complexity cost-effective mobile positioning techniques against the traditional method based on global positioning system has emerged. An interesting alternative is to utilize radio frequency (RF) signals to estimate the location of mobile terminals, as in multilateration and RF fingerprinting techniques. In this context, the objective of this work is to propose a new radio strength signal-based user equipment location method using a machine learning regression-based model to find directly the geographical coordinates of the mobile user in cellular networks. Numerical results show that, in most cases, the proposed method can meet the location accuracy requirements established by the Federal Communications Commission for network-based localization methods. [ABSTRACT FROM AUTHOR]

Human-centric applications such as virtual reality and immersive gaming are central to future wireless networks. Common features of such services include: 1) their dependence on the human user’s behavior and state and 2) their need for more network resources compared to conventional applications. To successfully deploy such applications over wireless networks, the network must be made cognizant of not only the quality-of-service (QoS) needs of the applications, but also of the perceptions of the human users on this QoS. In this paper, by explicitly modeling the limitations of the human brain, a concrete measure for the delay perception of human users is introduced. Then, a learning method, called probability distribution identification, is developed to find a probabilistic model for this delay perception based on the brain features of a human user. Given the learned model for the delay perception of the human brain, a brain-aware resource management algorithm based on Lyapunov optimization is proposed for allocating radio resources to human users while minimizing the transmit power and taking into account the reliability of both machine type devices and human users. Then, a closed-form relationship between the reliability measure and wireless physical layer metrics of the network is derived. Simulation results show that a brain-aware approach can yield savings of up to 78% in power compared to the system that only considers QoS metrics. The results also show that, compared with QoS-aware, brain-unaware systems, the brain-aware approach can save substantially more power in low-latency systems. [ABSTRACT FROM AUTHOR]

A complete understanding of the dynamics of Multipath TCP (MPTCP) over Cellular and WiFi networks is still lacking. This is a highly challenging issue, as a consequence of the complex interdependencies between the losses, packet reordering due to heterogeneous wireless channel features, errors, and link layer retransmissions, as well as their (joint) influence on MPTCP's control mechanism. In this paper, we develop a comprehensive approach that is capable of assessing the performance of long-lived MPTCP flows with joint WiFi and Cellular network access, taking into account the diverse characteristics of both types of networks. Relying on a parallel queueing model, we develop a framework that features the controllable network parameters, such as the retransmission limit and the buffer sizes, so as to capture their impact on the TCP-level performance. We include a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive MPTCP design. In particular, we design a novel MPTCP algorithm to exploit the route heterogeneity, and demonstrate that the proposed algorithm is capable of learning the network's characteristics. It also identifies the improved MPTCP window increment parameters for any given set of channel errors, retransmission limits, and buffer sizes. [ABSTRACT FROM AUTHOR]

To support the increasing data traffic demand in wireless communications networks, a multi-tier architecture that consists of multiple small cells is adopted in traditional (macro) cellular networks. Nevertheless, due to the expansion of various wireless devices and services, there is still a need to increase the network capacity in new generation networks such as 5G. Hence, the integration of Device-to-Device (D2D) communication as a promising technology with heterogeneous networks has been proposed. This integration not only increases network capacity but also improves spectral efficiency and alleviates the traffic load of base stations. However, the co/cross-tier interferences between D2D and cellular communications caused by resource sharing is a significant challenge. In this paper, we propose a distributed resource allocation algorithm based on matching theory to minimize these interferences and optimize the network performance. It is shown in this paper that the proposed algorithm converges to a stable matching and terminates after finite iterations. Simulation results show that the proposed algorithm is able to achieve more than 90 percent of the optimum network performance with much lower overhead and complexity. [ABSTRACT FROM AUTHOR]

Nowadays, wireless communication has become a fabric of our daily life, and it has been a universal demand for higher capacity and longer battery lifetime. For the sake of solving these problems, this paper considers taking the advantage of cooperative communication with the assistance of green relays. So, in this paper, we design an auction market that is composed of one base station (acts as the auctioneer), multiple green relays (act as sellers), and multiple mobile terminals (act as buyers). The terminals need to pay for the cooperative service, and the relays sell it for revenue. In addition, the terminals can raise their bid according to their residual energy; thus the terminals that lack energy have more opportunities to get cooperative service to avoid energy exhaustion. At the same time, the relays can reduce the price based on their instantaneous energy harvesting amount, and, hence, the relays can serve more terminals when they can harvest more energy from the environment. This paper also proposes three different auction rules to show the effect of relay selection. Furthermore, power allocation among relays and terminals is adopted to minimize the power consumption of terminals under the SNR requirement. Simulation results show that the three relay selection rules perform diversely and have their own advantages and disadvantages, but all of them can improve system capacity and prolong the lifetime of the mobile terminals effectively. And, with power allocation, the terminal can utilize the least energy to achieve its SNR requirement. [ABSTRACT FROM AUTHOR]

5G networks mainly rely on infrastructure-centric cellular solutions to address data traffic and service demands. Continuously scaling infrastructure- centric cellular networks is not exempt from challenges, and beyond 5G networks should consider the active coexistence and coordination of infrastructure-centric and device-centric wireless networks. Device-centric wireless networks will build from D2D and MCNs. Device-centric wireless networks can push the limits of edge computing and networking to smart devices (including smartphones, vehicles, machines and robots), and exploit their mobile computing, storage and connectivity capabilities. These capabilities can be more efficiently utilized using demand-driven opportunistic networking that establishes the connections between devices and nodes not just based on their presence, but also on their capacity to support the requested demand and services. This article presents results from experimental field tests that demonstrate the cellular spectral efficiency gains that can be achieved from the combined use of device-centric wireless communications and demand-driven opportunistic networking. The field trials demonstrate that these technologies can improve the cellular spectral efficiency of conventional cellular communications by up to a factor of 4.7 and 12 in outdoor pedestrian and vehicular scenarios, respectively, under the evaluated scenarios and conditions. These gains have been obtained using 4G and IEEE 802.11 technologies. However, the potential of device-centric wireless networks is not constrained to any particular radio interface. The results presented in this article provide empirical evidences that further motivate progressing toward a new paradigm where edge networking capabilities are moved to smart mobile devices that become part of the network fabric, and can opportunistically and locally integrate network management functions to ensure that sufficient resources are placed where the demand arises. [ABSTRACT FROM AUTHOR]

Next-generation heterogeneous cellular network (HCN) expects more and more deployment of the base stations and spectrum to achieve data rate in gigabits per second with seamless connectivity. Recently, small cell deployment has proven its effectiveness for increasing the coverage and area spectral efficiency (ASE) of the cellular network. Although, dense small cell deployment also increases the deployment cost and cross-tier interference of the network. This paper suggests switching-based mixed millimeter wave (mmW)/ultra high frequency (UHF) cellular network as a possible solution to overcome these challenges. In mixed mmW/UHF cellular network (named as MMUCN), mmW is used as the main transmission link, and UHF band is utilized as a backup link. In this paper, a theoretical framework is developed using tools from the stochastic geometry to analyze the outage performance, ASE, and deployment cost efficiency (DCE) of the MMUCN, which comprises existing macro base stations and mmW base stations. Optimal mmW base station densities constraints by the outage are calculated, which maximizes the ASE and DCE. Furthermore, a multiobjective optimization problem is formulated to maximize ASE and DCE simultaneously. Numerical results show that MMUCN outperforms UHF-based two-tier cellular network and efficiently mitigates the cross-tier interference of the system. [ABSTRACT FROM AUTHOR]

The next generation wireless cellular network is aimed to address the demands of users and emerging use cases set by industries and academia for beyond 2020. Hence, The next generation 5G networks need to achieve very high data rates, ultra-high reliability, extremely low latency, energy efficiency and fully connected coverage. To meet these demands, ultra-dense networks (UDN) or ultra-dense heterogeneous networks (UDHetNet), millimeter wave (mmWave) and multicell cooperation such as coordinated multipoint (CoMP) are the three leading technology enablers. In this paper, we have made an extensive survey of the current literature on 5G wireless communication focusing on UDN, mmWave and CoMP cooperation. We first discuss the architecture and key technology enablers to achieve the goals of the 5G system. Subsequently, we make an in-depth survey of underlying novel ultra-dense heterogeneous networks, mmWave and multicell cooperation. Moreover, we summarize and compare some of the current achievements and research findings for UDHetNet, mmWave and CoMP. Finally, we discuss the major research challenges and open issues in this active area of research. [ABSTRACT FROM AUTHOR]

Due to the higher wireless transmission rates in the 5G cellular networks, higher computation overhead is incurred in smartphones, which can cause the wireless transmission rates to be limited by the computation capability of wireless terminals. In this case, is there a maximum receiving rate for smartphones to maintain stable wireless communications in 5G cellular networks? The main objective of this article is to investigate the maximum receiving rate of smartphones and its influence on 5G cellular networks. Based on Landauer's principle and the safe temperature bound on the smartphone surface, a maximum receiving rate of the smartphone is proposed for 5G cellular networks. Moreover, the impact of the maximum receiving rate of smartphones on the link adaptive transmission schemes has been investigated. Numerical analyses imply that the maximum receiving rate of smartphones cannot always catch up with the downlink rates of future 5G cellular networks. Therefore, the link adaptive transmission scheme for future 5G cellular networks has to take the maximum receiving rate of smartphones into account. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communications underlaying cellular networks have been recognized as a promising technology to improve the system capacity and spectral efficiency of 5G networks. In this paper, we investigate the problem of designing the joint uplink subcarrier assignment and power allocation scheme to optimize the sum data rate of D2D pairs, under a general “many-to-many” scenario in which a subcarrier could be shared by multiple D2D pairs and a D2D pair could also reuse multiple subcarriers. In particular, the optimization problem is to maximize the sum data rate of all D2D pairs under the constraints of minimum data rate requirements of cellular user equipments (CUEs). The formulated problem falls into a mixed integer nonlinear programming, which is generally NP-hard. To solve the problem, we decompose it into two subproblems: the subcarrier assignment subproblem and the power allocation subproblem. More specifically, we first propose an effective algorithm to assign subcarriers for both D2D pairs and CUEs under the assumption that the transmit power of each user is equally allocated among all the subcarriers. After that, we apply the successive convex approximation approach to transform the nonconvex power allocation problem into a sequence of convex problems, based on which an effective power allocation algorithm is designed. Simulations validate that the proposed scheme can significantly improve the achievable sum data rate of D2D pairs, compared with the other existing schemes. [ABSTRACT FROM AUTHOR]

Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main research paradigms in cellular UAV communications, namely, cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location that are given, during which it needs to maintain reliable communication with the cellular network by associating with one of the available GBSs at each time instant that has the best line-of-sight channel (or shortest distance) with it. We aim to minimize the UAV’s mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target, which needs to be satisfied throughout its mission. To tackle this challenging non-convex optimization problem, we first propose an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization. The proposed methods are analytically shown to be capable of achieving a flexible tradeoff between complexity and performance, and yielding a solution in polynomial time with the performance arbitrarily close to that of the optimal solution. Numerical results further validate the effectiveness of our proposed designs against benchmark schemes. Finally, we make concluding remarks and point out promising directions for future work. [ABSTRACT FROM AUTHOR]

In this paper, we generalize conventional time division multiple access (TDMA) wireless networks to a new type of wireless networks coined generalized wireless powered communication networks (g-WPCNs). Our prime objective is to optimize the design of g-WPCNs where nodes are equipped with radio frequency (RF) energy harvesting circuitries along with constant energy supplies. This constitutes an important step towards a generalized optimization framework for more realistic systems, beyond prior studies where nodes are solely powered by the inherently limited RF energy harvesting. Towards this objective, we formulate two optimization problems with different objective functions, namely, maximizing the sum throughput and maximizing the minimum throughput (maxmin) to address fairness. First, we study the sum throughput maximization problem, investigate its complexity and solve it efficiently using an algorithm based on alternating optimization approach. Afterwards, we shift our attention to the maxmin optimization problem to improve the fairness limitations associated with the sum throughput maximization problem. The proposed problem is generalized, compared to prior work, as it seemlessly lends itself to prior formulations in the literature as special cases representing extreme scenarios, namely, conventional TDMA wireless networks (no RF energy harvesting) and standard WPCNs, with only RF energy harvesting nodes. In addition, the generalized formulation encompasses a scenario of practical interest we introduce, namely, WPCNs with two types of nodes (with and without RF energy harvesting capability) where legacy nodes without RF energy harvesting can be utilized to enhance the system sum throughput, even beyond WPCNs with all RF energy harvesting nodes studied earlier in the literature. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that conventional TDMA wireless networks and WPCNs with only RF energy harvesting nodes are considered as lower bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and maxmin throughput. Moreover, the results reveal valuable insights and throughput-fairness trade-offs unique to our new problem setting. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the feasibility of wireless edge caching in a hybrid millimeter-wave (mmWave)–micro-wave ($\mu $ Wave) network. Considering the average success probability (ASP) of file delivery as the performance metric, we derive expressions for the association probability of the typical user to the mmWave and $\mu $ Wave networks using stochastic geometric tools. Accordingly, we provide an upper bound on the ASP of file delivery and formulate the content caching placement scheme as an optimization problem with respect to caching probabilities, which jointly optimizes the ASP of file delivery considering both content placement and delivery phases. To simplify the non-convex problem and obtain design insights, we split it into two scenarios: 1) noise-limited and 2) interference-limited, and then propose optimal caching placement algorithms for both. We numerically evaluate the performance of the proposed schemes under several essential factors, such as content popularity, cache size, target data rate, blockages in the mmWave network, BS density, and path loss and also compare it with other common proactive caching schemes, namely uniform caching, caching $M$ most popular files, and random caching. Numerical results demonstrate the superiority of the proposed caching scheme over others, albeit certain trade-offs. [ABSTRACT FROM AUTHOR]

In wireless communications, as the spatio-temporal distribution of the traffic is dynamic, the performance degradation of cellular networks becomes inevitable. Especially in catastrophic scenarios or hot-spot areas, terrestrial base stations may be poorly functioning and/or congested; thus, deploying ASCs that were carried by a UAV swarm is reasonable and cost-effective. Moreover, services such as disaster evaluation and live broadcasting require high-definition video streams, which undoubtedly need broad-band wireless transmission. To achieve this goal, the mmWave approach is introduced in the UAV swarm. However, wireless backhaul links, the mobility of UAVs and system coexistence hamper performance. In this article, we propose a novel spectrum management (SM) architecture for UAV-assisted cellular networks. Considering the special feature of mmWave, we also study SM techniques for opportunistic utilization of low-altitude UAV swarm using multi-mode radio access technologies (RATs). Both motivations and challenges of the proposed SM architecture for the UAV swarm are analyzed. To evaluate the performances of the proposed mmWave based wireless backhaul in UAV-assisted cellular networks, different SM schemes have been discussed and verified with numerical results in five typical scenarios. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) based offloading stands out as a promising approach to mitigate the pressure over cellular networks. From the perspectives of both operators and users, an ideal solution is expected to maximize the offloading efficiency (ratio) while guaranteeing the quality-of-service (QoS) of users. However, existing approaches seldom jointly take into consideration offloading efficiency optimization and QoS guarantee. In this paper, we study how to maximize the percentage of total offloaded traffic of mobile content downloading via D2D, while guaranteeing the user minimum throughput under limited buffer size. The major challenge is how to incorporate all potential D2D opportunities to maximize the offloading performance. We propose a spatial–temporal dynamic graph (STDG) to capture all possible D2D opportunities in D2D-based traffic offloading. Based on STDG, we mathematically formulate an optimization problem of maximizing the offloading efficiency, subject to throughput requirement and buffer size constraint. The formulated problem is a linear-fractional problem and can be transformed into an equivalent linear program, whose solution reveals the maximum offloading ability that D2D achieves under QoS satisfaction. Trace-based simulations validate the superiority of our approach over three benchmark approaches. [ABSTRACT FROM AUTHOR]

Caching in wireless device-to-device (D2D) networks can bring contents closer to users and reduce backhaul traffic. However, the major obstacle to realize cache-enabled D2D communications is the selfish nature of users. In this paper, we propose an incentive mechanism for pricing the contributions of D2D transmitters in cache-enabled D2D-underlaid cellular networks modeled by stochastic geometry. A Stackelberg game is employed to resolve the conflict of interests between the operator and D2D transmitters. The main objective of this game is to reach the Stackelberg equilibrium, in which both the operator and D2D transmitters achieve the maximum profit by designing the optimal incentive price and the optimal caching strategy. We show the existence of the equilibrium and propose a low-complexity algorithm to obtain the near-optimal caching strategy and incentive price. Numerical results verify the existence of the SE and the advantages of the proposed scheme in terms of the utilities for the operator and D2D transmitters over existing caching schemes with fixed incentive. [ABSTRACT FROM AUTHOR]

While steady-state solutions of backlog and delay have been derived for wireless systems, the analysis of transient phases still poses significant challenges. Considering the majority of short-lived and interactive flows, transient startup effects, as caused by sleep scheduling in cellular networks, have, however, a substantial impact on the performance. To facilitate reasoning about the transient behavior of systems, this paper contributes a notion of non-stationary service curves. Models of systems with sleep scheduling are derived and transient backlogs and delays are analyzed. Further, measurement methods that estimate the service of an unknown system from observations of selected probe traffic are developed. Fundamental limitations of existing measurement methods are explained by the non-convexity of the transient service and further difficulties are shown to be due to the super-additivity of network service processes. A novel two-phase probing technique is devised that first determines the shape of a minimal probe and subsequently obtains an accurate estimate of the unknown service. In a comprehensive measurement campaign, the method is used to evaluate the service of cellular networks with sleep scheduling (2G, 3G, and 4G), revealing considerable transient backlog and delay overshoots that persist for long relaxation times. [ABSTRACT FROM AUTHOR]

Social connections among network nodes have been well investigated as an additional opportunity in network design (e.g., in routing strategies and trusted networking). This paper presents a paradigm shift that explores the design and performance analysis of combining social links jointly with communication links for message delivery in wireless networks. In a combined multi-layer social and communication network, communication links are based on conventional wireless technologies (e.g., WiFi, Bluetooth) and social links are overlaid over a communication infrastructure (e.g., cellular network) that provides an alternative way for data transmission. The goal is to characterize the performance analytically when routing is designed by combining social and communication links. A distance discretization technique is applied to model the reliability and delay of message delivery. The analytical foundation is developed to analyze the end-to-end delay and success probability under various effects of persistent transmission, potential error in distance estimation, and mobility. Systematic routing strategies that employ network inference are then designed to improve the performance in different aspects, such as delivery delay, delivery success probability, and energy-saving. A network emulation testbed is implemented with actual radios and real-world social network datasets to measure the performance of a heterogeneous network with social and communication links. The results in this paper show that the integration of social links in wireless network routing as a multi-layer design leads to substantial performance improvement for delay and reliability of message delivery. [ABSTRACT FROM AUTHOR]

While cellular networks connect over 3.7 billion people worldwide, their availability and quality is not uniform across regions. Under-provisioned and overloaded networks, as are common in rural or post-disaster areas, lead to poor network performance and a poor-quality user experience. To address this problem, we propose HybridCell: a system that leverages locally-owned small-scale cellular networks to augment the operation of overloaded commercial networks. HybridCell is the first system to allow a user with their existing SIM card and mobile phone to seamlessly switch between commercial and local networks in order to maintain continuous connectivity. HybridCell accomplishes this by identifying poorly-performing networks and taking action to provide seamless cellular connectivity to end users. Using traces from commercial cellular networks collected during our visit to the Za'atari refugee camp in Jordan, we demonstrate HybridCell's capability to detect and act upon commercial network overload, offering an alternate communication channel during times of congestion. We show that even in scenarios where provider networks deny calls due to overload, HybridCell is able to accommodate users and facilitate local calling. [ABSTRACT FROM AUTHOR]

Fifth-generation cellular mobile networks are expected to support mission critical URLLC services in addition to enhanced mobile broadband applications. This article first introduces three emerging mission critical applications of URLLC and identifies their requirements on end-to-end latency and reliability. We then investigate the various sources of end-to-end delay of current wireless networks by taking 4G LTE as an example. Then we propose and evaluate several techniques to reduce the end-to-end latency from the perspectives of error control coding, signal processing, and radio resource management. We also briefly discuss other network design approaches with the potential for further latency reduction. [ABSTRACT FROM AUTHOR]

This paper provides an analytical performance characterization of both the uplink (UL) and downlink (DL) user-centric network multiple-input multiple-output (MIMO) systems, where a cooperating base station (BS) cluster is formed for each user individually and the clusters for different users may overlap. In this model, cooperating BSs (each equipped with multiple antennas) jointly perform zero-forcing beamforming to the set of single-antenna users associated with them. As compared with a baseline network MIMO system with disjoint BS clusters, the effect of user-centric clustering is that it improves signal strength in both the UL and DL, while reducing cluster-edge interference in the DL. This paper quantifies these effects by assuming that BSs and users form Poisson point processes and by further approximating both the signal and interference powers using Gamma distributions of appropriate parameters. We show that BS cooperation provides significant gain as compared to single-cell processing for both the UL and DL, but the advantage of user-centric clustering over the baseline disjoint clustering system is significant for the DL cluster-edge users only. Although the analytic results are derived with the assumption of perfect channel state information and infinite backhaul between the cooperating BSs, they nevertheless provide architectural insight into the design of the future cooperative cellular networks. [ABSTRACT FROM AUTHOR]

The integration of local device-to-device (D2D) communications and cellular connections has been intensively studied to satisfy co-existing D2D and cellular communication demand. In future cellular networks, there will be numerous standby users possessing D2D communication capabilities in close proximity to each other. Considering that these standby users do not necessarily request D2D communications all the time, in this paper we propose a hybrid D2D-cellular scheme to make use of these standby users and to improve the rate performance for cellular users. More specifically, through D2D links, a virtual antenna array can be formed by sharing antennas across different terminals to realize the diversity gain of MIMO channels. This paper considers the use of millimeter wave links to enable high data rate D2D communications. We then design an orthogonal D2D multiple access protocol and formulate the optimization problem of joint cellular and D2D resource allocation for downlink transmissions using the proposed scheme. We obtain a closed-form solution for D2D resource allocation, which reveals useful insights for practical system design. Numerical results from extensive system-level simulations demonstrate that the rate performance of cellular users is significantly improved. [ABSTRACT FROM AUTHOR]

This paper studies physical-layer security for a cache-enabled heterogeneous cellular network comprised of a macro base station and multiple small base stations (SBSs). We investigate a joint design on caching placement and file delivery for realizing secure and energy-efficient transmissions against randomly distributed eavesdroppers. We propose a novel hybrid “most popular content” and “largest content diversity” caching placement policy to distribute the files of different popularities. Depending on the availability and placement of the requested file, we employ three cooperative transmission schemes, namely, distributed beamforming, frequency-domain orthogonal transmission, and best SBS relaying. We derive analytical expressions for the connection outage probability and secrecy outage probability for each transmission scheme. Afterward, we design the optimal transmission rates and caching allocation successively to achieve a maximal overall secrecy throughput and secrecy energy efficiency, respectively. Numerical results verify the theoretical analyses and demonstrate the superiority of the proposed hybrid caching policy. [ABSTRACT FROM AUTHOR]

In this paper, we strive to improve the throughput of heterogeneous cellular networks by exploiting the pre-cached files at user end to manage interference. We consider a transmission scheme, where network-coded multicast is employed to help cancel multi-user interference, and reconstructed interference cancelation (RIC) is used to help eliminate inter-cell interference. Because RIC is opportunistic, base station (BS) muting is used to coordinate the residual strong interference. Since user association affects residual interference and is coupled with BS muting while both are operated in a very different timescale from content caching, we jointly optimize user association and BS muting for a given caching policy to maximize the number of users simultaneously served by the transmission scheme. By transforming the formulated problem into a maximal independent set problem with constructed conflict graph, the global optimal solution is found with graph theory methods. By exploiting the topology feature of heterogeneous networks, we proceed to propose two low-complexity algorithms, respectively, implemented in a centralized and distributed manner, which are viable for large-scale networks. Simulation results show that the optimized transmission scheme achieves a remarkable performance gain over the existing schemes. [ABSTRACT FROM AUTHOR]

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that face a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The random access channel (RACH) procedure is the first step of connection establishment between IoT devices and base stations in the cellular-based mIoT network, where modeling the interactions between static properties of the physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio-temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network is modeled and analyzed based on stochastic geometry in the spatial domain, and the queue evolution is analyzed based on probability theory in the time domain. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with different RACH schemes in each time slot, which offer insights into the effectiveness of each RACH scheme. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device. This mathematical model and the analytical framework can be applied to evaluate the performance of other types of RACH schemes in the cellular-based networks by simply integrating its preamble transmission principle. [ABSTRACT FROM AUTHOR]