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To meet the demand of green communications of ultradense mobile devices (MDs), an energy-efficient mechanism, jointly considering the computation offloading and resource management, is designed to minimize the network-wide weighted energy consumption under the delay constraints of MDs for ultradense heterogeneous networks. Such a mechanism tightly integrates with the adjustment of computation capability and transmission power of MDs. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we design an effective algorithm to solve it. Specifically, by utilizing the powerful global searching capability of genetic algorithm (GA) and the accurate local searching capability of particle swarm optimization (PSO), the adaptive GA with diversity-guided is firstly used for coarse-grained search, and then adaptive PSO is utilized for fine-grained search. After that, some detailed analyses on the convergence, computation complexity and parallel implement are provided for algorithms. Simulation results show that the designed algorithm can achieve a lower network energy consumption than other offloading algorithms in general. At the same time, the numerical simulation also reveals that the designed algorithm may be more suitable for ultradense networks than existing algorithms.

In typical wireless heterogeneous networks (HetNets), users are clustered around known hotspots, e.g., shopping centers or schools, but such a non-uniform distribution of nodes is difficult to analyze. This paper explicitly models this scenario, with macro base stations (MBSs) modeled by a homogeneous Poisson point process (PPP), and millimeter-wave small base stations (mmWave SBSs) and users clustered around the hotspot centers, forming two Poisson cluster processes (PCPs), respectively. Fractional frequency reuse (FFR) and coordinated multi-point transmission (CoMP) are assumed since they help to limit the co-tier interference and enhance the coverage and capacity of the network. We present a distance-based approach for grouping macro user equipments (MUEs) from the cell center (CC) and cell edge (CE) regions for FFR analysis. We first derive some distance distributions, including joint distance distribution from the typical user to the cooperative open-access mmWave SBS and distance distribution from the typical user to the non-cooperative open-access mmWave SBS. We obtain expressions for various performance metrics, including association probability, signal to interference-plus-noise ratio (SINR) coverage probability, and ergodic capacity, under these conditions. Due to the complexity of the exact expressions, we derive novel approximations, using Alzer's lemma, to obtain the lower bounds on coverage and ergodic capacity, which are shown to be accurate through Monte Carlo simulation. Simulation results analyze the effect of different parameters on the network performance to give some guidance for the design of future networks. Numerical optimization of a key parameter, in terms of association probability, coverage probability, and ergodic capacity, is enabled by our analysis.

New radio in unlicensed spectrum (NR-U) and network slicing have been emerged as two promising technologies to meet with the demand of enormous traffic volume, extreme transmission speed, and highly heterogeneous service requirements in future 5 G networks. Additionally, spurred by both economic considerations and environmental concerns, green communication becomes a new prominent figure of merit in the design of future 5 G networks. In this paper, we investigate an energy consumption scheduling for unlicensed heterogeneous networks, namely NR-U and WiFi, by leveraging the network slicing technology. First, we introduce a network slice broker, which enables virtual heterogeneous network operators to request and lease resources cooperatively via signaling means. Then, we investigate a joint network selection and resource allocation scheduling to minimize the network energy consumption in real time environment, taking into account various user data rate and delay requirements. An online Lyapunov optimization algorithm has been introduced to solve the proposed problem with low computational complexity and rapid convergence. We show that there exists a tradeoff between energy consumption and traffic delay, which can be adjusted through control parameters. Numerical results are presented to confirm our analyses and demonstrate the effectiveness of the proposed algorithms.

Recent years have witnessed a rise of learning-based ( i.e. , artificial intelligence driven or AI-driven) video transport design, in order to achieve consistently high performance, even when the modern Internet is becoming increasingly heterogeneous while the applications are becoming unprecedentedly demanding ( i.e. , the simultaneous high-throughput and low-latency requirements of HD video telephony or intelligent remote driving). While separate evaluation using proprietary platform has shown the advantage of AI-driven algorithms over their non-AI counterparts, a systematic study is missing for directly comparing these AI-driven design under a uniform and practical platform. To bridge the gap, in this work, we first design and implement a full-fledged evaluation platform named Arsenal, which incorporates multiple state-of-the-art congestion control algorithms, most of which are AI-driven. Using Arsenal, we carry out a thorough comparative study of the algorithms, over massive traces collected from heterogeneous networks including WiFi, 4G, 3G and even the rare commercial 5G wireless networks. In particular, to enable convincing measurements for the dominated real-time video applications, we collect millions of practical video sessions in cooperation with a prevailing video service provider. The evaluation provides a handful of important observations, which are undiscovered before and have important impacts on future protocol design. Moreover, we will make the platform and algorithms open-source to enrich the research tools in the intelligent transportation community.

Non-orthogonal multiple access (NOMA) is considered as a potential multiple access technique for supporting massive number of devices with high spectral efficiency for the 5 G and beyond networks. Simultaneous wireless information and power transfer (SWIPT) is considered as an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in heterogeneous networks (HetNets) employing cooperative NOMA (CNOMA) is investigated. The HetNets consists of macro-base stations (MBS) under-laid by small-base stations (SBS). The distribution of MBSs follow Poisson point process model, while the SBS tier supports NOMA and carrier sensing for its transmission. Carrier sensing helps in reducing interference by blocking the base stations within a specific range from transmitting. In this work, the analysis at SBS tier is carried out in two phases. In the direct phase transmission, the SBS transmits the superimposed signal to a user pair, comprising of a cell-centre user (CCU) and a cell-edge user (CEU), using NOMA principle. The CCU acts as a cooperative relay to forward information to the CEU in the cooperative phase. Unlike existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating user (i.e. CCU) employs EH using interference from the adjacent base stations along with the EH from the superimposed signal. Expressions for outage probability and throughput are derived at the user pair served using NOMA. Comparison of the proposed EH system with existing solutions are highlighted and useful insights are drawn. Monte Carlo simulations are carried out to validate the analytical results.

Connected Autonomous Vehicles (CAVs) are Not-So-Futuristic. CAVs will be highly dynamic by intelligently exploiting multipath communication over several radio technologies, such as high-speed WiFi and 5G and beyond networks. Yet, the likelihood of data communication loss can be very high and/, or packets arrive at the destination not in correct working order due to erratic and mixed time-varying wireless links. Furthermore, the vehicular data traffic is susceptible to loss and delay variation, which recommends the need to investigate new multipath TCP (MPTCP) protocols for ultra-reliable low latency communication (URLLC) over such heterogeneous networks while reassuring CAVs’ needs. We undertake the challenge by jointly considering network coding and balanced linked adaptation for performing coupled congestion control across multiple wireless paths. Consequently, the proposed low delay MPTCP framework for connecting autonomous vehicles is efficient and intelligent by design. We conduct a rigorous convergence analysis of the MPTCP design framework. In summation, we provide a detailed mathematical study and demonstrate that the latency penalty for the URLLC-MPTCP developed over these networks becomes negligible when considering the possible benefits that multiple network convergence could offer. Our extensive emulation results demonstrate all these lucrative features of URLLC-MPTCP.

Full frequency reuse with nonorthogonal multiple access (NOMA) in heterogeneous networks (HetNets) can promisingly satisfy the fast-growing wireless transmission requirements, as the data of different users can be simultaneously delivered over the same frequency band. In HetNets, the transmissions from a micro base station (BS) to its cell-edge users often suffer from strong interference from the macro BS, which severely degrades the communication quality. The macro BS and the micro BS can transmit simultaneously to a common far-user using the distributed Alamouti coding technique to improve the communication robustness. Meanwhile, each BS also transmits data to a near-user using the NOMA protocol. The common far-user adopts the maximal ratio combining (MRC) technique to decode its desired data by treating the data of near users as interference. Each near-user decodes the data of far-user using the MRC technique and cancels it to decode its own data by treating the remaining undesired data as interference. Numerical results show that the Alamouti coding based NOMA transmission can greatly improve the throughput of HetNets.

With the rise of data-hungry applications and the rapid development of the Internet-of-Things (IoT), 5G and device-to-device (D2D) communications come into being. While such technologies are designed to support massive data access to meet the ever-increasing demands, the complexity and heterogeneity of the network structure bring bigger challenges for interference management. As such, downlink/uplink decoupling (DUDe) has become an attractive approach, as it offloads user equipments (UEs) to small-cells (SCells) and creates a more benign environment for D2D communication. In this paper, we investigate the application of DUDe in D2D-underlay heterogeneous networks, and propose an efficient joint cell-association, subchannel allocation, and power control scheme for network sum-rate maximization. The proposed scheme includes a minimum path-loss based DUDe to decide the uplink (UL) serving base-station, and a greedy coloring scheme with modified Munkres algorithm to cluster the UEs, and allocate subchannels. Furthermore, the difference of convex functions based programming algorithm for power control is utilized to maximize the network sum-rate while satisfying the UE transmit power and data rate constraints. The results presented show that the proposed scheme can achieve superior performance to the coupled benchmarks.

The exponential growth of data traffic from mobile devices leads to a need of heterogeneous networks (HetNets) which integrate multiple radio access technologies (multi-RATs) to allocate task-offloading with quick coordination. In this paper, we present a novel mobile edge computing (MEC) architecture for multi-RAT HetNets, and propose an MEC-centric offloading decision mechanism. By formulating the intended task as a multi-armed bandit (MAB) problem, we develop a fronthaul-aware upper confidence bound (FA-UCB) algorithm that is able to deal with uncertainty and asymmetry of network state information. It is rigorously established that the proposed FA-UCB algorithm has a sublinear regret bound against the optimal benchmark with full a-priori knowledge, given that the backhaul delays are independently and identically distributed over time. Furthermore, under a restless martingale (RM) bandit condition, we put forth a generalized RM-FA-UCB algorithm that can achieve a sublinear regret bound even under non-stationary network dynamics. Numerical results demonstrate the merits of the proposed schemes and algorithms.

The ultra dense deployment of small cells is considered a key technology to achieve the requested capacity in future cellular networks. However, the interference pattern becomes more unpredictable and challenging in these networks. Therefore, a suitable trade-off between spectrum spatial reuse and interference level has to be pursued to achieve good performance in terms of provided throughput. This paper proposes a new method to maximize the achievable throughput of an UDN with a suitable level of spatial spectrum reuse. In particular, the focus is on the small cells tier where the available spectrum is divided into sub-bands and each cell can use some of these to communicate with its associated users. The goal is to find the sub-bands allocation among cells that maximizes the system throughput. However, to limit complexity and signaling overhead that could result unaffordable in an UDN, a new metric to approximate the cell throughput to be optimized is defined. Moreover, the newly defined problem is solved using the Gibbs Sampling approach. The method effectiveness is proven by comparing the achieved results with those of the maximization of the effective system throughput and the optimal solution.

Machine-to-Machine (M2M) networks are an emerging technology with applications in various fields, including smart grids, healthcare, vehicular telematics and smart cities. Heterogeneous M2M networks contain different types of nodes, e.g., nodes that send emergency, periodic, and normal type data. An important problem is to rapidly estimate the number of active nodes of each node type in every time frame in such a network. In this paper, we design two schemes for estimating the active node cardinalities of each node type in a heterogeneous M2M network with $T$ types of nodes, where $T \ge 2$ is an arbitrary integer. Our schemes consist of two phases-- in phase 1, coarse estimates are computed, and in phase 2, these estimates are used to compute the final estimates to the required accuracy. We analytically derive a condition for one of our schemes that can be used to decide as to which of two possible approaches should be used in phase 2 to minimize its execution time. The expected number of time slots required to execute and the expected energy consumption of each active node under one of our schemes are analysed. Using simulations, we show that our proposed schemes require significantly fewer time slots to execute compared to estimation schemes designed for a heterogeneous M2M network in prior work, and also, compared to separately executing a well-known estimation protocol designed for a homogeneous network in prior work $T$ times to estimate the cardinalities of the $T$ node types, even though all these schemes obtain estimates with the same accuracy., 14 pages, 21 figures

Recently, the use of unmanned aerial vehicles (UAVs) or airship platforms to improve the performance of cellular networks has received growing interests. It is expected that the next generation cellular networks will enable the low-latency and high-reliability communications with the support of aerial-terrestrial heterogeneous networks (HetNets) composed of flying and ground base stations (BS). In addition, with the emergence of various delay-sensitive applications (e.g., autonomous vehicles), the end-to-end delay becomes a crucial quality of service (QoS) metric. However, how to design an efficient and delay-guaranteed aerial-terrestrial HetNets remains an open research challenge. In this paper, we develop an analytical approach to characterize the end-to-end delay performance of aerial-terrestrial HetNets by using network calculus. A stochastic model that takes into account the successful transmission and resource utilization in the radio access network is proposed to evaluate the delay performance within a target delay-violation probability. In addition, the effect of background traffic in the core network are carefully examined to present a tight upper bound for the end-to-end delay. Simulation results validate the proposed analysis under different network settings such as available bandwidth, the number of UAVs, and the trajectory of UAVs. Also, it is revealed that the trajectory of UAVs plays a more significant role in the delay performance compared to the number of UAVs.

Heterogeneous cellular networks (HetNets) are one of the key enabling technologies for fifth generation (5 G) networks. In HetNets, the use of small base stations (SBSs) inside the coverage area of a macro base station (MBS) offers higher throughput and improved coverage. However, such multi-tier base station deployment introduces new challenges, e.g., (i) All users experience significant inter-cell interference (ICI) due to frequency reuse, (ii) SBS associated users experience severe MBS-interference due to higher MBS transmit power, and (iii) MBS coverage edge users receive lower signal-to-interference ratio (SIR) due to longer distances. To address the aforementioned challenges, this work proposes a framework, including a novel cell deployment strategy, which combines Stienen's model with soft frequency reuse (SFR) and the corresponding performance analysis. According to Stienen's cell based SBS deployment, SBSs are deployed in MBS coverage edge area to enhance downlink SIR. To further mitigate ICI and MBS-interference, SFR is employed along with Stienen's cell based SBS deployment. Based on stochastic geometry, we derive expressions for coverage probability with four different types of cell deployment and SFR employment combinations. Numerical results indicate that SFR-enabled Stienen's cell based SBS deployment leads to enhanced edge user coverage and, hence, improves network performance gain.

Mobile edge computing (MEC) has been regarded as a promising technology to improve the quality of service (QoS) of users by offloading their computation-intensive and delay-sensitive tasks to the wireless network edge. Furthermore, applying non-orthogonal multiple access (NOMA) technology in heterogeneous networks (HetNets) has become a trend to improve system throughput and spectrum efficiency. Exploiting these benefits, we investigate the joint task offloading and resource allocation problem for MEC in NOMA-based HetNets. To minimize the energyconsumption of all users, we jointly consider task offloading decision, local CPU frequency scheduling, power control, computation resource and subchannel resource allocation. The optimization problem is challenging due to the combinatorial nature of the offloading decision and its strong coupling with the resource allocation. We thus decouple the problem into two sub-problems of offloading decision and resource allocation, and propose an efficient approach to find the joint solution by solving these two sub-problems iteratively. Simulation results show that the proposed approach can achieve a near optimal performance, and outperform other benchmark approaches in terms of saving energy consumption of all users with an acceptable complexity.

The 5G heterogeneous networks (HetNets) are capable of providing real-time computing services for autonomous vehicles (AVs) by deploying edge computing devices (ECDs) at macro cell base stations (MCBSs) and small cell base stations (SCBSs). With the imbalanced distribution and fast moving AVs contending intensely for computing services, how to efficiently exploit cooperations among participants in 5G HetNets to improve the service performance is therefore challenging. In this paper, we develop a game theoretic scheme for collaborative vehicular task offloading to facilitate the computing services in 5G HetNets. Specifically, we propose a two-stage vehicular task offloading mechanism to promote the cooperation among participants with the target of improving the task completion rate and the utilities of the participants, where the mechanism jointly considers the network architecture of the HetNets, the imbalanced distribution of AVs and the reuse of task results. In the first stage, an auction model is designed to help the MCBS select the optimal SCBS to execute the offloaded task based on the requirement of the task and the available computing resources of SCBSs. According to the task execution cost declared by the selected SCBS, the MCBS then bargains with the AV for the agreement of the task offloading service to maximize their utilities in the second stage. Using simulations, we show that the proposed collaborative task offloading scheme can achieve a higher task completion rate for the task offloading service and bring higher utilities to all participants than conventional schemes.

© 1967-2012 IEEE. Integrating millimeter Wave (mmWave) and long-term evolution (LTE) sub-6 GHz bands to provide wireless services for high-speed rails (HSRs) is a promising solution to balance the ever-rising service demand and operational cost. In this paper, we optimize the network interface selection of the mmWave and LTE interfaces for HSR communications, to minimize the operational cost while satisfying the service requests of passengers. The network interface selection problem is formulated as a finite-horizon discrete-time Markov decision process (MDP) problem. Based on the file requests and associated deadlines of a passenger and the train schedule, we solve the MDP problem ahead-of-time by using dynamic programming (DP) and achieve optimal interface selection for each waypoint along the railways. Further, by exploiting the subadditivity and superadditivity of the considered problem, we reveal that an optimal switch of interfaces is only triggered at the moment when the remaining undelivered data or the elapsed time reaches their specific thresholds. Based on the thresholds, the optimal interface selection can be directly achieved for every waypoint, hence eliminating the need for DP and substantially reducing the computational complexity. The proposed algorithms are evaluated with practical train schedules. Simulation results show that the proposed algorithms can substantially reduce the operational cost by 35% in our considered simulation setting, as compared to existing alternatives.

In this paper, we study the handover (HO), and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station, and some millimeter-wave (mmWave) small base stations. We establish an HO management, and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO, and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task, and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after the training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works.

Equipped with base station hardware, unmanned aerial vehicles (UAVs) can act as drone small cells (DSCs) to complement the traditional terrestrial small cells (TSCs) in providing ubiquitous connectivity for a wide range of applications. Although the performance of drone-assisted heterogeneous networks has been investigated, the state-of-the-art adopts simplified channel models that are not applicable to practical network scenarios. To make an accurate analysis on the network performance, and provide guidelines for the practical design of cell association and network deployment strategy, in this work, we consider a drone-assisted heterogeneous network that employs a flexible biased cell association policy and adopts a more general channel model that incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) components for both air-to-ground (ATG) and ground-to-ground (GTG) transmissions. We develop a unifying analytical framework to evaluate the network performance in terms of coverage probability, area spectral efficiency (ASE) and user throughput. Our results show that 1) deploying DSCs to complement traditional TSCs can only deteriorate the network performance within the ultra-dense network region, 2) by optimally setting the bias factor for cell association policy, the network performance can be further enhanced.

The network security is becoming more and more important in the fifth-generation (5G) networks. Therefore, the physical layer security with power transfer in heterogeneous network is investigated in this paper. The co-channel interference in the system is exploited for both secure transmission and energy harvesting in two types of eavesdropping, i.e., non-colluding and colluding eavesdropping. As such, we propose a secure transmission scheme, which obtains the maximum secrecy rate, and at the same time satisfy the outage constraints of the users’ QoS and the energy harvesting requirements. Assuming that the imperfect channel state information (CSI) follows Truncated Gauss distribution, we propose a novel method based on the expectation and variance of the CSI to transform the original complex problem into the convex optimization problem. Then the algorithms for two eavesdropping cases are provided. At last, numerical results are presented to show the robustness and effectiveness of the proposed algorithms.

Next generation (5G and beyond) mobile networks will highly rely on distributed heterogeneous infrastructure, which is foreseen to create a versatile resource ecosystem. In such network deployments, efficient traffic allocation and management is of prominent priority. The increasing congestion conditions of cellular infrastructures have steered extensive research focus towards Heterogeneous Network (HetNet) access solutions. The deployment of dense multiple radio access technology (RAT) networks combined with the enhanced capabilities of mobile devices to maintain concurrent connections through different RATs, add up to the complexity of the traffic allocation problem. Traffic allocation becomes more challenging when using diverse technologies concurrently to serve multiple traffic classes of different Quality of Service (QoS) demands. Towards a viable solution, we have proposed a pricing-based network selection process for heterogeneous networks, running in a distributed manner, which has proved to lead to equilibrium under the developed policies. In this paper, we extend our pricing algorithm by providing a new approach that introduces personalized pricing based on the load pressure that an allocation decision will impose to a RAT. The central system decisions are confined to dynamically select the UE polling rank policy that is presenting the closest potential performance to that of the optimal centralized solution, used as the benchmark ranking. The optimal policy is based on proportional fairness and is used as a reference centralized policy to drive our distributed solutions. We assess the enhanced pricing scheme proposed in this work, and we employ extensive simulations and testbed experimentation to evaluate its performance in terms of efficient bandwidth allocation, price variation, fairness and induced cost to clients served by the HetNet.

In this paper, we investigate a power control of uplink connection in the user-centric ultra-dense heterogeneous networks (HetNets), which are studied as different types of access points (APs). The main objective of this investigation is to engage users in a cooperative game to confront with the problem of per-user power control and coordinate multi-user interferences. Thus, we formulate the optimization problem as a sum of the users’ cooperative energy efficiency (EE) functions. Firstly, a realistic and new model of the user device's power consumption is proposed. This model includes the power used for operating modes and signal processing of mobile devices during the uplink data transmission. Secondly, the EE optimization problem is formulated by maximizing the sum of the users’ cooperative EE function subject to each user's quality-of-service (QoS) and a power constraint. Then, we propose a fully distributed and clustered learning scheme for solving the optimization problem, where neighboring users are clustered to engage in the cooperative game of power control in order to coordinate the multi-user interferences. It is theoretically proved that the size of the clusters has an impact on the sum of the users’ cooperative EE. Additionally, our scheme can achieve convergence with imperfect channel feedback and the local knowledge of the user's constraints. Finally, simulation results confirm the theoretical analysis and demonstrate the robust performance of the proposed scheme compared with two benchmark methods.

This paper evaluates the performance of uplink transmission in an LTE/Wi-Fi coexisting heterogeneous network (HetNet), where user equipments (UEs) capable of jointly performing LTE/Wi-Fi dual access and dual power control have a higher preference in connecting the Wi-Fi access points (WAPs) than the LTE base stations (LBSs). Thereby, each UE competes for Wi-Fi connection using carrier-sense multiple access with collision avoidance (CSMA/CA) and, only if unsuccessful, employs cellular communication. During uplink transmission, the UEs employ truncated channel inversion power control with receiving thresholds adjusted according to the occupied communication modes. Based on the stochastic geometry theory, we derive the analytical expressions for the successful connection probability and the average energy efficiency (EE) of Wi-Fi communications. We show that there exists a maximal successful connection probability for Wi-Fi communications, where the optimal receiving signal and energy thresholds of WAPs depend critically on the density of WAPs. Moreover, the average EE of UEs successfully accessing WAPs saturates in the large receiving signal threshold regime, independent of the density of WAPs. These findings significantly differ from those in cellular communications, where maintaining stable service connection is more emphasized than other operation goals, and reveal that deploying Wi-Fi communication has a potential for improving the EE of the considered HetNet. Both simulation and experiment results validate the analytical derivations. These results together show that, compared with single-mode uplink transmission, the considered dual-mode transmission can achieve higher connection probability and EE simultaneously in LTE/Wi-Fi coexisting HetNets.

This paper explores a model on cooperative cognitive radio network (CCRN) to enable the in-band bidirectional device-to-device (D2D) communications by sharing the licensed spectrum of cellular system (CS) in a 5G heterogeneous networks (HetNets) framework. In this model, cellular nodes (CNs) are modelled as primary users (PUs) and devices involved in D2D communications are referred as secondary users (SUs). The D2D pair, as unlicensed users, access to the licensed spectrum originally assigned to the CN pair while assuring relaying action to assist two-way communications between the CNs. Further, the D2D pair exploits the information-bearing radio frequency (RF) signals transmitted by the CNs to energize their transceivers, using the principle of simultaneous wireless information and power transfer (SWIPT). Closed form outage expressions of both cellular and D2D communications are derived. Simulation results are used to validate our analytical results. Simulation results show that about ${\bf \sim 68\%}$ and ${\bf \sim 277\%}$ gains are achieved by the proposed system in terms of spectrum efficiency and energy efficiency, respectively as compared to another prominent study on spectrum sharing enabled two-way PU and two-way SU communications.

Non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communication are two important technologies considered for the fifth generation (5G) of mobile networks. In this paper, we propose a machine-to-machine (M2M) communication underlaying heterogeneous network (HetNet) with mmWave-NOMA transmission, where small cell users (SCUs) and machine type communication (MTC) devices share the same resource block (RB) to increase the network capacity. We formulate an optimization problem to select efficient users and allocate transmission powers to SCUs and MTC devices. The object of optimization is to maximize the energy efficiency (EE) subject to the minimum spectral efficiency (SE) requirements and maximum transmission powers. We present a low computational complexity sub-optimum solution based on the non-cooperative game. An iterative algorithm with fast convergence speed and a reduced complexity search-based algorithm are respectively presented to solve the power control and user selection problems. The comparison between the proposed solution and optimum solution for different number of transmit antennas indicate that there is a small gap between SEs and EEs of the proposed and optimum solutions. Further, the results of user selection scheme demonstrate that the location of strong user affects the performance. We also derive analytical expressions for the outage probability which confirm Monte Carlo simulations.

In this paper, we consider a communication scenario for heterogeneous networks (HetNets) in which the base stations transmit information over both microwave ( $\mu$ W) and millimeter wave (mmW) bands. To exploit the spatial characteristics of the channel, we consider three-dimensional multiple input multiple output (3D-MIMO) technology. In addition, to increase the spectral efficiency of the network, sparse code multiple access (SCMA) is used as the transmission technology for both bands. A practical scenario in which the channel information is not known perfectly is assumed, and hence a robust scheme is developed. We formulate the resource allocation problem as an optimization problem which is highly nonlinear and non-convex. To solve the problem, we rely on iterative approximation methods, such as successive convex approximation, and we provide a joint transmit power allocation and beam-forming to increase the secrecy rate of the network. Using simulations, we evaluate the performance of the proposed scheme for different network settings. Simulations show a performance increase of about 24.85% with 3D-MIMO over two-dimensional MIMO (2D-MIMO). In addition, the use of both $\mu$ W and mmW bands results in a 20.25% performance increase over a case where only $\mu$ W is used. We also show that 3D-MIMO and dual connectivity have better performance than 2D-MIMO and $\mu$ W in high-rise and dense urban areas.

The continuously growing mobile traffic demand is pushing wireless networks to dense heterogeneous architectures with different types of base stations (BSs). In such dense heterogeneous networks (HetNets), especially in the high-rise building 3-D environment, the users need to determine which BS to be associated with before communication. Different user association schemes lead to different system performance. In this paper, a maximum instantaneous signal-to-interference-plus-noise ratio (MISINR) user association scheme is studied in the dense urban environment. A general 3-D model is utilized to model the dense urban HetNets deployed in the high-rise buildings. The coverage probability and average user rate are theoretically derived by using stochastic geometry in a general case and an interference-limited case, respectively. Numerical and simulation results demonstrate that the MISINR user association scheme has the best performance compared with other two association schemes in 3-D HetNets. From the results, it is interesting to observe that the expressions of coverage probability and average user rate can be simplified to closed-forms in the interference-limited case.

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.

Consider multicell heterogeneous networks (HetNets), where each cell consists of a number of small (micro/pico) base stations (SBSs) and the SBSs perform coordinated multipoint (CoMP) transmission/reception to serve a number of downlink/uplink (DL/UL) users over the same frequency band. The SBSs work in full-duplex (FD) mode and the DL/UL users are half-duplex (HD). Due to co-channel transmissions, the DL and the UL are mutually interfered. This motivates us to consider a joint DL/UL beamformer design for maximizing the system sum rate. Different from the conventional sum rate maximization (SRM), where the user's rate is allowed to be any nonnegative real number, herein we focus on the discrete-rate case — each user's rate is constrained to a prescribed discrete-rate set. This discrete-rate constraint is motivated by the fact that practical communication systems are designed for a finite number of transmission rates, owing to the finite number of modulation and coding schemes (MCS). In view of this, our SRM problem is coined as discrete SRM (DSRM). The DSRM problem is essentially a mixed-integer nonlinear program, and NP-hard. To tackle it, two different approaches are proposed. The first approach is built upon the simplex reformulation of the discrete rate and the mirror descent (MD) algorithm. The second approach exploits the correspondence between the rate and minimum mean-square error (MMSE), and employs the block-coordinate descent (BCD) method to find an approximate solution. Simulation results demonstrate the superior rate performance of the proposed designs as compared with the weighted MMSE (WMMSE) design with naive rate quantization.

In order to cope with the rapid increase in power consumption of heterogeneous cellular networks, this article proposes a new efficient transmission technique for heterogeneous networks with massive MIMO wireless backhaul with the objective of minimizing power consumption cost. We assume that transmissions on the backhaul link and the access link occur simultaneously on the same frequency band (in-band) thanks to MIMO spatial multiplexing. On the other hand, we consider that uplink and downlink transmissions are separated in time. In order to prevent multi-user and inter-tier interference, block diagonalization beamforming is considered at the macro base station (MBS) and the signal from the small base station (SBS) to the MBS is transmitted orthogonally to the channel of the SBS's users. The problem of minimizing the transmit power of base stations under users’ minimum-rate constraints is formulated. We first derive analytically the optimal time splitting parameter and the allocated transmit power considering that the inter-SBS interference is generated by fixed power. Next, we solve the power allocation problem when the generated inter-SBS interference is no longer considered fixed by proposing an efficient iterative power allocation algorithm. A heuristic user scheduling algorithm is devised in order to deal with the feasibility problem. Finally, simulations validate our analysis and show that the proposed transmission technique outperforms the conventional reverse time division duplex with bandwidth splitting (out-band) in terms of total power consumption.

With the advances in hand-held devices (smartphones and tablets, etc.) and high speed wireless networks, users have an explosive growth demand for live streaming service. Due to the diversity of user equipments (UEs), the live streaming has to be transcoded into different versions. However, transcoding is a computationally expensive and time consuming process. Due to the dynamic characteristics of wireless network environment, providing high quality and low latency live videos for UEs is a big challenge. This study investigates user scheduling, transcoding decision, computational and wireless spectrum resources allocation problem in edge-clouds aided heterogeneous networks (HetNets). The research focuses on improving UEs’ quality of service (QoS) for live-streaming services, which includes quality of video and latency requirements. Different from existing literature, to approach the real wireless environment, the available computational and wireless spectrum resources are modeled as random processes in the work. Considering dynamic characteristics of wireless networks and available resources, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, it is difficult to solve this problem by traditional learning algorithms. Therefore, an enhanced actor-critic algorithm is proposed to resolve the problem, in which both the actor part and the critic part employ eligibility traces. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm.

Compromised by the ever increasing amount of mobile data traffic generated by mobile devices, edge servers are expected to provide caching resources for users so as to enable low-latency content delivery and release the burden on backhaul network. Considering the limited caching capacities of edge servers, it is essential to design an efficient content placement strategy to increase the data offloading ratio, i.e., the proportion of requested contents that are delivered via edge caching rather than backhaul links. In this paper, we propose a spatially cooperative caching strategy for a two-tier heterogeneous network consisting of edge servers and caching helpers aiming at reducing the storage space taken by duplicate contents on caching helpers and increasing hit probability. We develop an analysis framework for the proposed spatially cooperative caching strategy by using tools from stochastic geometry and maximize the hit probability by optimizing the caching probabilities of contents on edge servers and caching helpers. Particular, we derive the closed-form solution of the optimization problem for unit cache size. The simulation results are identical with the analytical ones and the outperformance is observed with the optimal caching probability derived in this paper compared with the existing caching strategies.

The rapid increase of wireless applications and services coupled with the arrival of 5G and Internet of Things will not only exacerbate demand for further capacity at the downlink (DL) but also crucially at the uplink (UL). One of the most potential enablers to simultaneously optimize both links is the DL/UL decoupling (DUDe) technique which does so by exploiting the possibility of associating each user to a different base station (BS) in each link direction. Moreover, the increasing desire to incorporate millimeter-wave (mmWave) communications in future networks further enriches the possibilities to achieve higher capacities. To this end and in contrast to existing works which use dual association based on minimum path-loss (Min-PL), this paper investigates the merits of adopting capacity-based multi-association in ultra-high frequency (UHF) and mmWave hybrid networks, where mobile users may simultaneously connect to multiple UHF small cells (SCells), millimeter-wave SCells and/or UHF macro cells (MCells). It will be shown that, our joint association and resource management approach can provide higher data rates and energy efficiency than many benchmark techniques. To achieve an insight into the performance of the proposed design, the results present a comprehensive comparison based on single and multi-connectivity as well as coupled and decoupled association for a variety of important metrics.

In this paper, we study the problem of multirate packet delivery in heterogeneous packet erasure broadcast networks. The technical challenge is to enable users receive packets at different rates, as dictated by the quality of their individual channel. We present a new analytical framework for characterizing the delivery rate and delivery delay performance of a previously proposed non-block-based network coding scheme in the literature. This scheme was studied in homogeneous network settings. We show for the first time, via new theoretical analysis and simulations that it can actually achieve multirate packet delivery. Using acknowledgments from each user, we show that the user with the highest link capacity achieves the maximum possible throughput. Also, a non-zero packet delivery rate is possible for other users, and the delivery rate depends on the difference between the packet arrival rate at the sender and the link capacity of each user. The accuracy of our analytical framework is confirmed by comparing the results with simulations for different settings of packet arrival rate at the sender and link capacities.

In heterogeneous networks (HetNets), user association approaches should be able to achieve load balancing among base stations (BSs). This paper investigates the joint optimization of user association and resource allocation in Backhaul-constrained HetNets for capacity enhancements. We consider two major limitations in HetNets: the backhaul bottleneck of BSs and the capability of user equipment (UE). We establish a framework based on a multi-leader multi-follower Stackelberg game, in which resource allocation is formulated as a follower-level game and user association is cast as a leader-level game. Because of the backhaul bottleneck of small BSs, the given preference order of users renders the final association result unstable. Thus, the resident-oriented Gale-Shapley (GS) algorithm is included in the proposed framework to obtain a stable single-BS association. Furthermore, congestion factors are introduced to reflect the relative backhaul congestion degrees of BSs, which enables load balancing among the small BSs in the proposed algorithm. The study considers user association and resource allocation with and without limitations on the number of serving users for small BSs in HetNets. Extensive simulation results suggest that the proposed algorithm can adaptively respond to a wide variety of network situations.

Flexible duplexing is a promising technique to improve the spectral efficiency of future cellular networks, which has been proposed mainly to provision asymmetric uplink (UL) and downlink (DL) traffic scenarios, through flexible channel allocations. However, this flexibility in the channel allocation process, which is responsible for allocating the underlying channel to different base stations in a heterogeneous network (HetNet), has brought new technical challenges due to the introduction of complex UL-to-DL and DL-to-UL interference scenarios. This paper analyzes the joint channel allocation (CA) and user scheduling (US) process for orthogonal frequency-division multiple access-based flexible HetNets, while considering exact inter-cell/intra-cell interferences. The resulting joint problem is a large-scale mixed-integer nonlinear programming problem that is computationally intractable, therefore, it has been re-formulated into a convex upper bound problem to find benchmark solutions for CA in flexible HetNets. Since, no new CA scheme has been proposed yet for the HetNets employing flexible duplexing techniques, we discuss the efficacy of existing schemes under various UL and DL traffic scenarios.

In this paper, a novel interference mitigation and power allocation (PA) scheme is investigated for downlink non-orthogonal multiple access (NOMA) with multiple input multiple output (MIMO) technology in heterogeneous networks (HetNets). The proposed scheme, named PA based interference alignment and coordinated beamforming (PA-IA-CB), consists of two stages. The first stage applies two steps of IA-CB; one step for canceling the inter-cluster and the co-tier interference among small cells, whereas the other step deals with the inter-cluster interference within the macro cell. In the second stage, the cross-tier interference is proposed to be managed by properly handling the allocated power to the macro base station (MBS) and the small base stations (SBSs). The PA problem is modeled as a non-cooperative game between the MBS and the SBS with the aim of increasing the system sum rate. Simulation results verify the efficiency of the proposed technique in terms of total system sum rate and outage probability compared to the conventional schemes.