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In this paper, we provide an analytical framework to analyze heterogeneous downlink millimeter-wave (mm-wave) cellular networks consisting of $K$ tiers of randomly located base stations (BSs), where each tier operates in an mm-wave frequency band. Signal-to-interference-plus-noise ratio (SINR) coverage probability is derived for the entire network using tools from stochastic geometry. The distinguishing features of mm-wave communications, such as directional beamforming, and having different path loss laws for line-of-sight and non-line-of-sight links are incorporated into the coverage analysis by assuming averaged biased-received power association and Nakagami fading. By using the noise-limited assumption for mm-wave networks, a simpler expression requiring the computation of only one numerical integral for coverage probability is obtained. Also, the effect of beamforming alignment errors on the coverage probability analysis is investigated to get insight on the performance in practical scenarios. Downlink rate coverage probability is derived as well to get more insights on the performance of the network. Moreover, the effect of deploying low-power smaller cells and the impact of biasing factor on energy efficiency is analyzed. Finally, a hybrid cellular network operating in both mm-wave and $\mu$ -wave frequency bands is addressed. [ABSTRACT FROM AUTHOR]

Wireless network virtualization is a promising technique for future wireless networks. In this paper, different from traditional virtualization approaches by means of resource isolation at the subchannel or time-slot level, we propose a novel framework of heterogeneous cellular network virtualization combined with interference alignment (IA) technology, which utilizes IA to cancel the mutual interference, by aligning the interference from other transmitters into a lower dimensional subspace at each receiver. In this framework, we formulate the virtual resource allocation as a joint virtualization and IA problem, considering the gain not only from interference mitigation introduced by IA but from the sum-rate improvement brought by virtualization as well. In addition, to reduce the computational complexity, with the recent advances in discrete stochastic approximation (DSA), we propose a two-step algorithm to solve the formulated problem. The basic principle is to design IA schemes for each feasible association combination and then to traverse the association space to search for the optimal association combination with the maximum sum rate. Extensive simulations are conducted with different system parameters to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, the downlink of a clustered coordinated multicell multiuser multiple-input multiple-output (MIMO) system is considered, and various low-complexity user scheduling algorithms to maximize the sum rate are proposed. The users' data signals are transmitted cooperatively via the base stations (BSs) of a cluster within the sectorized multicell heterogeneous cellular network. Exhaustive search as the optimal scheduling method is extremely complex, particularly for large numbers of users requesting service. We therefore propose suboptimal scheduling algorithms based on the lower complexity metaheuristic stochastic optimization techniques of simulated annealing (SA) and particle swarm (PS). Moreover, a hybrid algorithm combining the traits of PS and a greedy scheduler is also proposed. The performance and complexity of these algorithms are evaluated for a heterogeneous system model employing different precoding methods. Assessing the algorithms' performance via computer simulations, the effectiveness of the proposed schemes in terms of sum rate is demonstrated, yielding performance very close to that of the exhaustive search at much lower complexity. For each precoding method used in the employed system model, the best corresponding approach to scheduling from our proposed algorithms is determined. [ABSTRACT FROM AUTHOR]

Given the context of the challenge of exponentially increasing data traffic on communication networks brought by the 5G era, this paper focuses on how to apply deep reinforcement learning (DRL) techniques to solve the problem of optimizing the energy efficiency of D2D communications in heterogeneous cellular network environments. We propose a joint resource allocation scheme based on multi-intelligent deep reinforcement learning, which enables D2D devices to intelligently switch between license-free and optimal license bands and adjust the transmit power in real time to maximize the energy efficiency improvement. In this work, a multi-intelligent deep reinforcement learning framework is designed to enable D2D users in heterogeneous networks to make collaborative decisions and dynamically adjust their communication strategies according to real-time network status and environmental changes. In this paper, a deep Q-network model with a graph attention network (GAT) as the core structure is constructed; this model can cope with the complexity and diversity of network states and learn and execute optimal resource allocation strategies. In this paper, we propose a targeted loss function design that balances the optimization goal of D2D communication energy efficiency with network stability and long-term gains. Through rigorous simulation experiments, this paper verifies that a DRL-based approach can significantly improve the energy efficiency of D2D communications in heterogeneous cellular networks in real-world scenarios while ensuring the stability of the quality of service (in terms of, e.g., rate, delay, and resource utilization).

In this paper, the tradeoff between energy efficiency and spectral efficiency in multicell heterogeneous networks is investigated. Our objective is to maximize both energy efficiency and spectral efficiency of the network, while satisfying the minimum rate requirements of the users. We define our objective function as the weighted summation of energy efficiency and spectral efficiency functions. The fractional frequency reuse (FFR) scheme is employed to suppress intercell interference. We formulate the problem as cell-center boundary selection for FFR, frequency assignment to users, and power allocation. The optimal solution of this problem requires exhaustive search over all cell-center radii, frequency assignments, and power levels. We propose a three-stage algorithm and apply it consecutively until convergence. First, we select the cell-center radius for the FFR method. Second, we assign the frequency resources to users to satisfy their rate requirements and also maximize the objective function. Third, we solve the power allocation subproblem by using the Levenberg–Marquardt method. Minimum rate requirements of users are also included in the solution by using dual decomposition techniques. Our numerical results show a Pareto-optimal solution for energy efficiency and spectral efficiency. We present energy efficiency, spectral efficiency, outage probability, and average transmit power results for different minimum rate constraints. Among other results, we show that, in a particular setting, \text13\% energy efficiency increase can be obtained in a multicell heterogeneous wireless network by sacrificing \text7\% spectral efficiency. [ABSTRACT FROM PUBLISHER]

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses. [ABSTRACT FROM AUTHOR]

Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the computationally expensive problem with the large action space, multi-agent deep RL method is proposed. Specifically, the state, action and reward function are defined for UEs, and dueling double deep Q-network (D3QN) strategy is introduced to obtain the nearly optimal policy. Through message passing, the distributed UEs can obtain the global state space with a small communication overhead. With the double-Q strategy and dueling architecture, D3QN can rapidly converge to a subgame perfect Nash equilibrium. Simulation results demonstrate that D3QN achieves the better performance than other RL approaches in solving large-scale learning problems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate physical-layer security for a heterogeneous cellular network consisting of a macro cell and a small cell in the presence of a common eavesdropper that intends to tap both the macro cell and small cell. The orthogonal spectrum sharing (OSS) and non-orthogonal spectrum sharing (NOSS) are considered for the heterogeneous cellular network. The OSS allows the macro cell and small cell to access a given spectrum band in an orthogonal manner, whereas the NOSS enables them to access the same spectrum simultaneously and mutual interference exists. We present two NOSS schemes, namely, the interference-limited NOSS (IL-NOSS) and interference-canceled NOSS (IC-NOSS), where the mutual interference is constrained below a tolerable level through power control in the IL-NOSS, and the IC-NOSS exploits a specially designed signal for canceling out the interference received a legitimate user while confusing the eavesdropper. We derive closed-form expressions for an overall secrecy outage probability of the OSS, IL-NOSS, and IC-NOSS schemes by jointly considering the secrecy of both the macro cell and small cell. Furthermore, we characterize the secrecy diversity of the OSS, IL-NOSS, and IC-NOSS schemes through the asymptotic secrecy outage analysis in the high signal-to-noise ratio (SNR) region. It is proved that the OSS and IL-NOSS schemes obtain the same secrecy diversity gain of zero only, and a higher secrecy diversity gain of one is achieved by the IC-NOSS scheme. In addition, numerical results demonstrate that secrecy outage probabilities of the IL-NOSS and IC-NOSS can be minimized through an optimization of the ratio of the SNR of small cell to the SNR of macro cell, called the small-to-macro ratio (SMR). Moreover, with an optimized SMR, the proposed IC-NOSS scheme significantly outperforms the OSS and IL-NOSS schemes in terms of individual secrecy outage probabilities of both the macro cell and small cell. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of energy efficiency maximization (EEM) for the small cells that coexist with a macro cell in an underlay heterogeneous cellular network, where a macro base station and a number of small base stations transmit signals to a macro user and small users through their shared spectrum. We propose a joint power and bandwidth allocation (JPBA) scheme for the sake of maximizing the energy efficiency (EE) of small cells under the constraint of a guaranteed quality-of-service requirement for the macro cell. Considering that our formulated EEM-based JPBA (EEM-JPBA) problem is non-convex, we convert the original fractional problem into an equivalent subtractive form by adopting the Dinkelbach’s method, which is addressed through the augmented Lagrange multiplier approach. Moreover, a new two-tier iterative algorithm is presented to obtain the optimal solution of our EEM-JPBA scheme. Simulation results demonstrate that the proposed two-tier iterative algorithm can quickly converge to the optimal EE solution. In addition, it is shown that the proposed EEM-JPBA scheme significantly outperforms the conventional power and bandwidth allocation methods in terms of their EE performance. [ABSTRACT FROM AUTHOR]

Caching at small base stations (SBSs) has demonstrated significant benefits in alleviating the backhaul requirement in heterogeneous cellular networks (HetNets). While many existing works focus on what contents to cache at each SBS, an equally important problem is what contents to deliver so as to satisfy dynamic user demands given the cache status. In this paper, we study optimal content delivery in cache-enabled HetNets by considering the inherent multicast capability of wireless medium. We consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint. We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). By using relative value iteration and special properties of the request queue dynamics, we characterize some properties of the value function of the MDP. Based on these properties, we show that the optimal multicast scheduling policy is of threshold type. Then, we propose a structure-aware optimal algorithm to obtain the optimal policy. We also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to obtain this policy. [ABSTRACT FROM PUBLISHER]

Recent results have elucidated the benefits of using improper Gaussian signaling (IGS) as compared with conventional proper Gaussian signaling (PGS) in terms of achievable rate for interference-limited conditions. This paper exploits majorization theory tools to formally quantify the gains of IGS along with widely linear transceivers for multiple-input multiple-output (MIMO) systems in interference-limited scenarios. The MIMO point-to-point channel with interference is analyzed, assuming that received interference can be either proper or improper, and we demonstrate that the use of the optimal IGS when received interference is improper strictly outperforms (in terms of achievable rate and mean square error) the use of the optimal PGS when interference is proper. Then, these results are extended to two practical situations. First, the MIMO Z-interference channel (Z-IC) is investigated, where a trade-off arises: with IGS, we could increase the achievable rate of the interfered user while gracefully degrading the rate of the non-interfered user. Second, these concepts are applied to a two-tier heterogeneous cellular network where macrocells and small cells coexist and multiple MIMO Z-ICs appear. [ABSTRACT FROM AUTHOR]

The forthcoming 5G cellular network is expected to overlay millimeter-wave (mmW) transmissions with the incumbent micro-wave (\mu\textW) architecture. The overall mm-\mu\textW resource management should, therefore, harmonize with each other. This paper aims at maximizing the overall downlink (DL) rate with a minimum uplink (UL) rate constraint, and concludes: mmW tends to focus more on DL transmissions while \mu\textW has high priority for complementing UL, under time-division duplex (TDD) mmW operations. Such UL dedication of \mu\textW results from the limited use of mmW UL bandwidth due to excessive power consumption and/or high peak-to-average power ratio (PAPR) at mobile users. To further relieve this UL bottleneck, we propose mmW UL decoupling that allows each legacy \mu\textW base station (BS) to receive mmW signals. Its impact on mm-\mu\textW resource management is provided in a tractable way by virtue of a novel closed-form mm-\mu\textW spectral efficiency (SE) derivation. In an ultra-dense cellular network (UDN), our derivation verifies mmW (or \mu\textW) SE is a logarithmic function of BS-to-user density ratio. This strikingly simple yet practically valid analysis is enabled by exploiting stochastic geometry in conjunction with real three-dimensional (3-D) building blockage statistics in Seoul, South Korea. [ABSTRACT FROM PUBLISHER]

When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization. [ABSTRACT FROM AUTHOR]

This paper addresses the cell association problem in the downlink of a multi-tier heterogeneous network (HetNet), where base stations (BSs) have finite number of resource blocks (RBs) available to distribute among their associated users. Two problems are defined and treated in this paper: sum utility of long term rate maximization with long term rate quality of service (QoS) constraints and global outage probability minimization with outage QoS constraints. The first problem is well-suited for slow fading environments, while the second problem provides a framework to deal with environments with fast fading. The defined optimization problems in this paper are solved in two phases: cell association phase followed by the optional RB distribution phase. We show that the cell association phase of both problems have the same structure. Based on this similarity, we propose a unified distributed algorithm with low levels of message passing for the cell association phase. This distributed algorithm is derived by relaxing the association constraints and using Lagrange dual decomposition method. In the RB distribution phase, the remaining RBs after the cell association phase are distributed among the users. Simulation results show that our distributed cell association scheme outperforms schemes that are based on maximum signal to interference plus noise ratio (SINR). [ABSTRACT FROM PUBLISHER]

This paper explores the co-existence of a macro cell and a small cell for heterogeneous cellular networks, where a macro base station (MBS) and small base station (SBS) transmit to respective macro user (MU) and small user (SU) through their shared spectrum in the face of a common eavesdropper. We consider two spectrum sharing mechanisms, namely the overlay spectrum sharing (OSS) and underlay spectrum sharing (USS). In the OSS, the MBS and the SBS take turns to access their shared spectrum. By contrast, the USS allows the MBS and SBS to simultaneously transmit over the shared spectrum with the aid of power control for limiting their mutual interference, thus called interference-limited USS (IL-USS). In order to take advantage of mutual interference in confusing the eavesdropper without causing adverse effect on the MU, we propose an interference-canceled USS (IC-USS) scheme, where a sophisticatedly designed signal is emitted at the MBS to cancel out the interference received at the MU, which is also beneficial in terms of defending the common eavesdropper. Closed-form expressions of overall outage probability and intercept probability are derived for OSS, IL-USS, and IC-USS schemes by taking into account both MBS-MU and SBS-SU transmissions. The secrecy diversity analysis is also carried out by characterizing an asymptotic behavior of the overall outage probability with a given intercept probability in the high signal-to-noise ratio region. It is shown that the secrecy diversity gains of conventional OSS and IL-USS are zero, whereas the proposed IC-USS achieves a higher secrecy diversity gain of one. This implies that with an arbitrarily low overall intercept probability, the conventional OSS and IL-USS methods converge to their respective outage probability floors; however, the proposed IC-USS scheme can make the overall outage probability asymptotically decrease to zero by simply increasing the transmit power. In addition, numerical results demonstrate an obvious advantage of the proposed IC-USS over OSS and IL-USS against eavesdropping. [ABSTRACT FROM AUTHOR]

This paper investigates the downlink performance of dense $K$ -tier heterogeneous cellular networks (HCNs) under general settings. First, Gaussian approximation bounds for the standardized aggregate wireless interference (AWI) in dense $K$ -tier HCNs are obtained for when base stations (BSs) in each tier are distributed over the plane according to a spatial and general Poisson point process. The Kolmogorov–Smirnov (KS) distance is used to measure deviations of the distribution of the standardized AWI from the standard normal distribution. An explicit and analytical expression bounding the KS distance between these two distributions is obtained as a function of a broad range of network parameters, such as per-tier transmission power levels, per-tier BS intensity, BS locations, general fading statistics, and general bounded path-loss models. Bounds achieve a good statistical match between the standardized AWI distribution and its normal approximation even for moderately dense HCNs. Second, various spatial performance metrics of interest, such as outage capacity, ergodic capacity, and area spectral efficiency in the downlink of $K$ -tier HCNs for general signal propagation models are investigated by making use of the derived distribution approximation results. Considering two specific BS association policies, it is shown that the derived performance bounds track the actual performance metrics reasonably well for a wide range of BS intensities, with the gap among them becoming negligibly small for denser HCN deployments. Finally, both analytical and numerical results on the area spectral efficiency reveal a non-linear growth trend with diminishing returns of HCN performance. Hence, the ${\sf SIR}$ invariance property does not hold under bounded path-loss models, which is a critical finding from the HCN design perspective. In particular, it points out a critical BS density beyond which the HCN performance starts to decline due to excessive wireless interference. [ABSTRACT FROM AUTHOR]

In this paper, the tradeoff among the energy efficiency, fairness, harvested energy, and system sum rate is studied. In this regard, various fairness methods, namely, max–min fairness, proportional fairness, and minimum delay potential fairness in power-domain non-orthogonal multiple access-based heterogeneous cellular networks are investigated. In order to perform successive interference cancellation (SIC), we use two ordering approaches and compare their performance. To this end, we propose joint subcarrier and power allocation algorithms to achieve fair energy efficient resource allocation for each fairness method and SIC ordering. Since the proposed optimization problems are non-convex and intractable, the existing methods to solve the convex problems could not be directly used. To overcome this difficulty, an iterative algorithm based on successive convex approximation is used. Moreover, to show the optimality gap of the proposed solution method, an optimal approach based on the monotonic optimization is applied in which we first transform each of the proposed optimization problems into a monotonic optimization problem of canonical form, and then, we obtain the optimal solution of each problem, which coincides with the optimal solution of the original non-convex problem. We finally study the performance of the proposed schemes using simulations for different values of the system parameters. [ABSTRACT FROM PUBLISHER]

With the advance of the photovoltaic panel technology, the infrastructure equipment of cellular networks with solar energy sources has attracted extensive interests. It is expected that base stations (BSs) and relay stations (RSs) with renewable energy will play important roles in reducing the power consumption from electrical grids in future green cellular networks. Inspired by this technology trend, this paper considers a heterogeneous green cellular network, in which a part of RSs is powered by solar energy sources to represent the incremental deployment for the infrastructure with renewable energy in the near future. We formulate a new relay selection and power allocation problem with Mixed Integer Linear Programming to select solar-powered RSs and grid-powered RSs, such that the total grid power consumption is effectively minimized in decode-and-forward cooperative communication networks. We design an algorithm to derive the optimal solution, and also devise an efficient distributed algorithm to reduce the computational cost. The simulation results demonstrate that the algorithms can effectively reduce the total grid power consumption with different channel conditions, different deployments of RSs in a network, and different available power levels in solar-powered RSs. In addition, the problem model and the proposed algorithms can be extended to support hybrid energy sources. [ABSTRACT FROM AUTHOR]

In this paper, we derive the expression of the cache hitting probability with random caching policy and propose the joint optimization algorithm based on difference of convex algorithm (DCA) in the three-tier caching heterogeneous cellular network assisted by macro base stations, helpers and users. Under the constraint of the caching capacity of caching devices, we establish the optimization problem to maximize the cache hitting probability of the network. In order to solve this problem, a convex function is introduced to convert the nonconvex problem to a difference of convex (DC) problem and then we utilize DCA to obtain the optimal caching probability of macro base stations, helpers and users for each content respectively. Simulation results show that when the density of caching devices is relatively low, popular contents should be cached to achieve a good performance. However, when the density of caching devices is relatively high, each content ought to be cached evenly. The algorithm proposed in this paper can achieve the higher cache hitting probability with the same density. [ABSTRACT FROM AUTHOR]

In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small cells are combined to offer better Quality of Service to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) joint transmissions are used to further improve performance. In this paper, we address the average delay minimization problem by first formulating it as a static optimization problem. Even though the problem is NP-hard we are able to solve it via an efficient algorithm that guarantees a 1/2-approximation ratio. We then proceed to propose two fully distributed and dynamic caching policies for the same problem. The first one asymptotically converges to the static optimal solution under the Independent Reference Model (IRM). The second one provides better results in practice under real (non-stationary) request processes. Our online policies outperform existing dynamic solutions that are PHY-unaware. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the average packet queueing delay of a slow-moving user in a macro–femto two-tier heterogeneous cellular network. We model the user as a queue that experiences vertical handover between macro and femto base stations. We define the queue states progression at each handover interval as a 2-D discrete time Markov chain. We determine the Markov chain's steady-state probability distribution so that the average packet delay can be calculated using the Little's Theorem. With the model, we can analyze the effects on packet delay as caused by variations in coverage area dwell time, packet service rate, and probability of entering a femtocell. We have verified the analytical model against results from random-event simulations. We find that the average packet delay can be lowered by deploying more femtocells only if the time a user stays in a femtocell is longer than a certain value. The model can help us in avoiding unnecessary vertical handovers, as well as in deciding the number and size of femtocells to achieve a desired packet delay. [ABSTRACT FROM PUBLISHER]

We develop a framework for downlink heterogeneous cellular networks with line-of-sight (LoS) and non-LoS transmissions. Using stochastic geometry, we derive tight approximation of average downlink rate that enables us to compare the performance between densifying small cells and expanding base station (BS) antenna arrays. Interestingly, we find that adding small cells into the network improves the downlink rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spatial densification will lose its benefits and further impair the network capacity. To this end, we provide the optimal small cell density that maximizes the rate via numerical results for practical deployment guidance. In contrast, expanding macro BS antenna array can always benefit the capacity until an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from the deployment of small cells. Furthermore, we find that allocating part of antennas to distributed small cell BSs works better than centralizing all antennas at the macro BS, and the optimal allocation proportion is also given numerically for practical configuration reference. In summary, this paper provides a further understanding on how to leverage small cells and massive MIMO in future heterogeneous cellular networks deployment. [ABSTRACT FROM PUBLISHER]

Device to device (D2D) communication is considered as an effective technology for enhancing the spectral efficiency and network throughput of existing cellular networks. However, enabling it in an underlay fashion poses a significant challenge pertaining to interference management. In this paper, mode selection and resource allocation for an underlay D2D network is studied while simultaneously providing interference management. The problem is formulated as a combinatorial optimization problem whose objective is to maximize the utility of all D2D pairs. To solve this problem, a learning framework is proposed based on a problem-specific Markov chain. From the local balance equation of the designed Markov chain, the transition probabilities are derived for distributed implementation. Then, a novel two phase algorithm is developed to perform mode selection and resource allocation in the respective phases. This algorithm is then shown to converge to a near optimal solution. Moreover, to reduce the computation in the learning framework, two resource allocation algorithms based on matching theory are proposed to output a specific and deterministic solution. The first algorithm employs the one-to-one matching game approach whereas in the second algorithm, the one-to many matching game with externalities and dynamic quota is employed. Simulation results show that the proposed framework converges to a near optimal solution under all scenarios with probability one. Moreover, our results show that the proposed matching game with externalities achieves a performance gain of up to 35 percent in terms of the average utility compared to a classical matching scheme with no externalities. [ABSTRACT FROM AUTHOR]

For a wireless multi-tier heterogeneous network with orthogonal spectrum allocation across tiers, we optimize the association probability and the fraction of spectrum allocated to each tier so as to maximize rate coverage. In practice, the association probability can be controlled using a biased received signal power. The optimization problem is non-convex and we are forced to explore locally optimal solutions. We make two contributions in this paper: first, we show that there exists a relation between the first derivatives of the objective function with respect to each of the optimization variables. This can be used to simplify numerical solutions to the optimization problem. Second, we explore the optimality of the intuitive solution that the fraction of spectrum allocated to each tier should be equal to the tier association probability. We show that, in this case, a closed-form solution exists. Importantly, our numerical results show that there is essentially zero performance loss. The results also illustrate the significant gains possible by jointly optimizing the user association and the resource allocation. [ABSTRACT FROM PUBLISHER]

The burgeoning network traffic imposes a huge burden on the network backbone. Caching popular files at the wireless network edge is promising to address the problem. In practice, file popularity is very unlikely to know in advance. Online learning algorithms are effective to learn this uncertainty in a sequential way. In each slot, the learning agent generates a caching policy (i.e., the to-be-cached files) and can observe users’ feedback about the caching policy within the same slot. This method implicitly requires that all of the users are able to provide feedback promptly. However, in practice, the availability of each individual user is affected by many factors, e.g., users are moving out of the service area temporarily, or they may still consume files in the previous slots, which may result in the feedback delay. In this paper, we propose a delay-tolerant wireless caching system that takes both the feedback delay and users’ availability into consideration. We frame the content caching problem as a stochastic combinatorial multi-armed bandit problem with delayed feedback and forced-to-sleep arms, and devise an intelligent caching algorithm called CFAUD to solve the problem. Also, we show that CFAUD is effective and efficient both theoretically and practically. Finally, experiments are conducted to compare the performance of the proposed algorithm with other well-known algorithms. [ABSTRACT FROM AUTHOR]

Summary: Optimizing cell locations of cellular networks is one of the most fundamental problems of network design. However, in order to meet a growing appetite for mobile data services, a large number of base stations are being deployed, which leads to tremendous energy consumption in cellular networks. This augmentation increases not only the system's capital and operational expenditure (CAPEX/OPEX) for mobile operators but also CO2 emissions. Besides the issue of meeting overwhelming traffic demands, network operators around the world now realize the importance of managing their cellular networks in an energy‐efficient manner. In this paper, we develop a self‐organizing framework for energy saving in orthogonal frequency‐division multiple‐access–based cellular access networks. We consider three different objectives, namely, coverage maximization, overlap minimization, and power consumption minimization, which is different from all existing works on energy saving in cellular networks. [ABSTRACT FROM AUTHOR]

Caching popular contents at base stations (BSs) of a heterogeneous cellular network (HCN) avoids frequent information passage from content providers to the network edge, thereby reducing latency and alleviating traffic congestion in backhaul links. The potential of caching at the network edge for tackling 5G challenges has motivated recent studies on optimal content placement in large-scale HCNs. However, due to the complexity of the network performance analysis, the existing strategies were mostly based on approximation, heuristics, and intuition. In general, optimal strategies for content placement in HCNs remain largely unknown and deriving them forms the theme of this paper. To this end, we adopt the popular random HCN model, where $K$ tiers of BSs are modeled as independent Poisson point processes distributed in the plane with different densities. Furthermore, the random caching scheme is considered, where each of a given set of $M$ files with corresponding popularity measures is placed at each BS of a particular tier with a corresponding probability, called placement probability. The probabilities are identical for all BSs in the same tier but vary over tiers, giving the name tier-level content placement. We consider the network performance metric, hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user. Leveraging existing results on HCN performance, we maximize the hit probability over content placement probabilities, which yields the optimal tier-level placement policies. For the case of uniform received signal-to-interference (SIR) thresholds for successful transmissions for BSs in different tiers, the policy is in closed-form, where the placement probability for a particular file is proportional to the square-root of the corresponding popularity measure with an offset depending on BS caching capacities. For the general case of non-uniform SIR thresholds, the optimization problem is non-convex and a sub-optimal placement policy is designed by approximation, which has a similar structure as in the case of uniform SIR thresholds and shown by simulation to be close-to-optimal. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the energy efficiency of downlink transmissions in heterogeneous networks. Our objective is to satisfy the rate requirement of users while maximizing the energy efficiency of the network. We employ the fractional frequency reuse (FFR) scheme to increase the energy efficiency of downlink transmissions and to eliminate outages for the cell-edge users. We formulate the joint cell-center boundary selection for FFR, scheduling, and power allocation problems. This formulation gives us a mixed discrete (selection of the cell-center boundary selection for FFR and scheduling) and continuous (power allocation) optimization problem which is hard to solve jointly. In order to solve this problem, we propose a three-stage resource allocation algorithm. In the first stage, we propose a dynamic method to determine the cell-center region boundaries. In the second stage, we employ the Lagrangian-directed scheduler algorithm to incorporate the rate requirements of users. The third stage solves the power allocation subproblem using the Levenberg–Marquardt method combined with dual decomposition. In order to make the base stations further reduce intercell interference, interference pricing mechanism is applied. This scheme penalizes the utility of a base station with the interference it creates. The performance of the proposed algorithm is simulated in a long-term evolution (LTE) network simulation tool. Our numerical results reveal that significant gains in terms of energy efficiency can be achieved with the proposed algorithm. The outage probability is also significantly reduced. [ABSTRACT FROM AUTHOR]

In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of $K$ -tier heterogeneous cellular networks (HCNs) with randomly located base stations (BSs) and mobile terminals (MTs). In the DL and UL, each energy-constrained MT pairs up with its corresponding BS, which provides the maximum received power at the MT. Due to the densely located BSs and universal frequency reuse between all tiers in HCNs, the typical MT is allowed to harvest energy from the serving BS by direct beamforming as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we first derive exact expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. As the number of BS antennas goes to infinity, we further derive asymptotic expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. Our results show that the UL outage probability per MT first decreases and then increases with increasing the time allocation factor (the fraction of time allocated to the DL), and the UL outage probability, and the UL average ergodic rate per MT, can be largely improved by using the massive antenna arrays at the BSs. [ABSTRACT FROM AUTHOR]

In this paper, we consider an orthogonal frequency division multiplexing (OFDM) based heterogeneous cellular network consisting of a macro cell and a small cell, where a macro-cell base station (MBS) and a small-cell base station (SBS) communicate with respective macro-cell user (MU) and small-cell user (SU) with the existence of a common eavesdropper across different OFDM subcarriers. In order to make full use of the mutual interference between MBS and SBS against eavesdropping, an artificially designed signal is emitted at MBS to cancel out the interference received at MU as well as to interfere with the common eavesdropper at each subcarriers. For the purpose of further improving the secrecy performance, a sum secrecy rate (SSR) of OFDM-based heterogeneous cellular networks is maximized by optimizing the power allocation between MBS and SBS across different OFDM subcarriers when the eavesdropper owns the global instantaneous channel state information (ICSI), thus called ICSI based SSR maximization (ICSI-SSRM). As for the case when the ICSI of wiretap channels is unknown, we propose a statistical CSI based SSR maximization (SCSI-SSRM) scheme, where the statistical characteristics of channels from MBS and SBS to eavesdropper are employed to optimally allocate powers of MBS and SBS across different subcarriers under the constraint of MBS's total transmit power. The formulated ICSI-SSRM and SCSI-SSRM problems are all non-convex due to form of the difference between two-convex (D.C.) functions. Thus, we utilize the D.C. approximation approach to respectively convert original optimization problems into convex problems. Moreover, iterative optimal power allocation algorithms for ICSI-SSRM and SCSI-SSRM schemes are also presented to obtain their respective SSR values. Simulation results illustrate that the ICSI-SSRM and SCSI-SSRM algorithms can converge to their optimal values, which confirms the correctness and validation of the proposed algorithms. In addition, numerical results are also given to show that the ICSI-SSRM and SCSI-SSRM schemes outperform conventional power allocation methods in terms of their SSR performance. [ABSTRACT FROM AUTHOR]

This paper exhibits a primer on the use of modern system reliability techniques on several important topologies of small-cell deployment in multi-tier dense cellular networks. We synthesize a network into several subnetworks and use outage probability as a performance metric to formulate the small-cell deployment problem in the Boolean domain by means of an indicator function. The methodology employed herein includes disjointness for logically added expressions, arriving at a probability ready expression, and a transformation to the probability domain on a one-to-one basis by replacing indicator Boolean variables by their expectations and replacing logical operations by their arithmetical counterparts. This work also presents a systematic procedure of computing several reliability metrics such as the useful redundancy region, mean-time-to-failure, importance measures of the links, and quantification of uncertainty in system reliability as a function of uncertainties in link reliabilities by considering both identical and non-identical link failures. Non-linear algorithms are also tested on the optimization problem that minimizes system unreliability. We contribute to the important issue of analyzing small-cell deployment by providing exact system reliability metrics and hence open a new frontier for software defined networks controllers. [ABSTRACT FROM AUTHOR]

Low powered node densification leading to dense and ultra dense heterogeneous networks is a feature of 5th generation cellular networks. With this densification, the nature of the link between the transmitter and receiver in the network requires even more accurate and reliable models. For performance analysis of such networks where the rates of signal loss over distance becomes a significant parameter, more accurate path loss models should be used when analyzing user equipment (UE) association probability, coverage probability and average spectral efficiency. In this paper, we have considered a two-tier dense heterogeneous cellular network incorporating downlink uplink decoupled technique and have derived generalized expressions for UE association probability, decoupled uplink coverage probability and decoupled uplink average spectral efficiency using multi-slope path loss model. This path loss model broadly incorporates the effects of physical environment on the distance-dependent path loss. For simulation purpose, we have compared network performance while considering single-slope and dual-slope path loss models. The derived analytical expressions have been validated through network simulations and found in good agreement. Through comparison, it has been found that the decoupled UE association probability and uplink coverage probability is higher when incorporating multi-slope path loss model as compared to single-slope path loss model while the decoupled uplink spectral efficiency is observed to be lower when incorporating dual slope path loss model. [ABSTRACT FROM AUTHOR]

Deploying Sub-6GHz networks together with millimeter wave (mmWave) is a promising solution to achieve high data rates in traffic hotspots while guaranteeing sufficient coverage, where mmWave small cells are densely deployed to provide high quality of service. In this paper, we propose an analytical framework to investigate the integrated Sub-6GHz-mmWave cellular networks, in which the Sub-6GHz base stations (BSs) are modeled as a Poisson point process, and the mmWave BSs are clustered following a Poisson cluster process in traffic hotspots. We conduct stochastic geometry-based analysis and derive the performance metrics including the association probability, signal-to-interference-plus-noise ratio coverage probability and average achievable rate, which are validated to be accurate by Monte Carlo simulations. We analyze the impact of various deployment parameters on the network performance to give insights on the network design. In particular, it is shown that deploying mmWave small cells in traffic hotspots will outperform both traditional Sub-6GHz heterogeneous network and isolated mmWave system in terms of the coverage probability. It can also be shown that extremely high and extremely small association weight for mmWave BSs will deteriorate the performance for cell edge users and cell interior users, respectively. Moreover, there exists an optimal pre-decided dispersion parameter of mmWave BSs that contributes to the maximum coverage probability. [ABSTRACT FROM AUTHOR]

The base station (BS) density configuration is a crucial factor in improving energy efficiency (EE) in cellular networks, particularly when the sleeping strategy is used to reduce energy consumption. In this paper, the optimization of BS density for enhancing EE through traffic-aware sleeping strategies in both one- and two-tier cellular networks is researched, where the BS location is modeled as a Poisson point process. In the one-tier scenario, the EE optimization objective function is proved to be quasi-concave, and the optimal BS density solution is derived, where the maximum achievable EE is demonstrated to be independent of user density. In the two-tier scenario, both the activation probability of BSs and the coverage probability are analyzed, where the EE optimization objective function is proved to be not necessarily quasi-concave. To handle this nonconvex issue, an equivalent optimization problem that jointly optimizes the ratio and weighted sum of BS densities is proposed and solved by a dynamic gradient iterative algorithm. Meanwhile, a data fitting method is utilized to quantitatively analyze EE performances, in which the relationship between the optimal BS density and the user density is approximately linear when the user density is sufficiently high. Simulation results verify the relevant derivations and reflect that the sleeping strategy can not only save energy but improve the quality of radio links as well. [ABSTRACT FROM PUBLISHER]

In emerging heterogeneous networks (HetNets), a wide range of the underlying performance evaluation problems is related to wireless interference characterization and can be reduced to investigating the distribution of a side length in a random triangle. In this paper, we address the task of calculating the side-length distribution in such a triangle determined by the known distributions of its two other sides and the distribution of the angle between them. No restrictions on the distributions of input random variables (RVs) are imposed except for their statistical independence. Our solution delivers a crucial building block for interference analysis in multitier and multiradio HetNets, including mobile device-to-device communication as one of the most extreme interference-limited cases. [ABSTRACT FROM AUTHOR]

A large number of today's cellular users are vehicular, i.e., traveling in public transports. To improve the weak received signal of vehicular users and to mitigate vehicle penetration loss, researchers have recently proposed the use of moving relays (MRs). People in vehicles can use these MRs that are mounted on top of public transports for better communication. Recent research on MRs, however, is mainly limited to providing performance analysis of vehicular users. The effect of these relays on current heterogeneous cellular networks has yet to be studied. In this paper, we develop a system model for a downlink heterogeneous cellular network, which includes MRs. We derive the coverage probability of macro users, vehicular users, and the overall network. We also show that, in addition to providing better coverage to vehicular users, MRs can also enhance coverage of nonvehicular boundary users with the help of cooperative communication. [ABSTRACT FROM AUTHOR]

This paper presents a framework for coverage and capacity optimization (CCO) in HCNs from stochastic geometry theory. Unlike conventional approaches, we propose a concept of effective capacity as the optimization objective, which involves implicit index of coverage in the form of truncation function. The main idea is to model the coverage probability, average rate and joint objective considering noise where the location of base stations are spatially distributed as a Poisson Point Process. Under an adaptive power allocation assumption, we improve iterative approach with percentile level to find an effective increasing direction of transmit power. The simulation shows that the theoretical analysis and optimization objective are feasible and the adjustment of percentile level serves as a flexible solution for CCO. [ABSTRACT FROM AUTHOR]

In this paper, mathematical frameworks for system-level analysis and design of uplink heterogeneous cellular networks with multiple antennas at the base station (BS) are introduced. Maximum ratio combining (MRC) and optimum combining (OC) at the BSs are studied and compared. A generalized cell association criterion and fractional power control scheme are considered. The locations of all tiers of BSs are modeled as points of homogeneous and independent Poisson point processes. With the aid of stochastic geometry, coverage probability and average rate are formulated in integral but mathematically and computationally tractable expressions. Based on them, performance trends for small- and large-scale multiple-antenna BSs are discussed. Coverage and rate are shown to highly depend on several parameters, including the path-loss exponent, the fractional power control compensation factor, and the maximum transmit power of the mobile terminals. The gain of OC compared with MRC is proved to increase, if a more aggressive power control is used and if the number of BS antennas increases but is finite. For the same number of BS antennas, OC is shown to reach the noise-limited asymptote faster than MRC. All findings are validated via Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Achieving high spectrum efficiency (SE) and energy efficiency (EE) is of primary importance for the sustainability of future cellular networks, but a key challenge is on balancing the tradeoff that arises when maximizing both of these performance metrics simultaneously. This paper develops a framework for analyzing the SE and EE of a two-tier heterogeneous cellular network consisting of macrocell and femtocell base stations (BSs) operating under a shared-spectrum scenario. It is shown that both the SE and the EE can be significantly enhanced with the overlaid deployment of the femto tier. However, the performance gain achievable is found to be strongly dependent on the load level and the BS power consumption attributes. A multiobjective optimization problem that maximizes the SE and the EE subject to quality-of-service (QoS) constraints is formulated and solved to give the Pareto-optimal operational regime. The novelty of this work is the quantification of the SE–EE tradeoff as a Lebesgue measure, which is defined by the Pareto-optimal regime. The developed framework is useful for studying the impact of the load on the SE–EE tradeoff, based on a strategy that exploits the varying load conditions to achieve good balance in the SE–EE tradeoff is formulated. Numerical results show that, while the improvement achieved in minimizing the SE–EE performance gap is marginal under high-load conditions, it is feasible to significantly increase the SE and the EE during low-load conditions and satisfy the users' QoS requirements by optimally adapting the density of the femto-tier BSs accessing the shared spectrum. [ABSTRACT FROM AUTHOR]

A reliable solution for meeting the high demand of throughput in areas called hotspots is the heterogeneous network. Heterogeneous networks are different depending on their coverage, their type of radio access technique and the way there are connected to the core network. This paper proposes a novel algorithm for semi-coordinated resource allocation and scheduling based on mobile positioning information, game theory and reinforcement learning technique. The capabilities of such an approach to support the practical deployment of heterogeneous networks is analyzed. Further, a reasoning strategy is proposed to justify the choice of Wi-Fi versus other small cell technologies from a practical deployment viewpoint. [ABSTRACT FROM AUTHOR]

The current cellular networks have evolved to be more randomly, irregularly, and heterogeneously deployed to meet the exponential growth of mobile data traffic and the demand for seamless coverage, making the signal-to-interference ratio (SIR) distribution more challenging to analyze. Therefore, in this paper, we propose two simple approximative approaches to the SIR distribution of general heterogeneous cellular networks (HCNs) based on the ASAPPP method, which stands for “approximate SIR analysis based on the Poisson point process” and the MISR (mean interference-to-signal ratio)-based gain for each individual tier of the HCNs. Specifically, we first establish a per-tier ASAPPP approximation to general HCNs and then present an effective gain ASAPPP method as a further simplification when the path loss exponents are the same for all the tiers, that is, we give an explicit expression for the effective gain G\rm{ eff} of general HCNs such that the SIR distribution is obtained by scaling the SIR threshold \theta to \theta/G_{\rm{ eff}} . The asymptotic behavior for the tail of the SIR distribution is also given. Furthermore, to highlight the simplicity and effectiveness of the approximative approaches, we derive the exact distribution of the SIR in the two-tier HCNs modeled by \beta$-Ginibre and Poisson point processes and compare it with the approximate results. The results demonstrate that the proposed approaches give a simple yet excellent approximation for the SIR distribution. [ABSTRACT FROM AUTHOR]

With the foreseeable explosive growth of small cell deployment, backhaul has become the next big challenge in the next generation wireless networks. Heterogeneous backhaul deployment using different wired and wireless technologies may be a potential solution to meet this challenge. Therefore, it is of cardinal importance to evaluate and compare the performance characteristics of various backhaul technologies to understand their effect on the network aggregate performance. In this paper, we propose relevant backhaul models and study the delay performance of various backhaul technologies with different capabilities and characteristics, including fiber, xDSL, millimeter wave (mmWave), and sub–6 GHz. Using these models, we aim at optimizing the base station (BS) association so as to minimize the mean network packet delay in a macrocell network overlaid with small cells. Numerical results are presented to show the delay performance characteristics of different backhaul solutions. Comparisons between the proposed and traditional BS association policies show the significant effect of backhaul on the network performance, which demonstrates the importance of joint system design for radio access and backhaul networks. [ABSTRACT FROM PUBLISHER]

In this paper, we focus on one of the representative 5G network scenarios, namely multi-tier heterogeneous cellular networks. User association is investigated in order to reduce the down-link co-channel interference. Firstly, in order to analyze the multi-tier heterogeneous cellular networks where the base stations in different tiers usually adopt different transmission powers, we propose a Transmission Power Normalization Model (TPNM), which is able to convert a multi-tier cellular network into a single-tier network, such that all base stations have the same normalized transmission power. Then using TPNM, the signal and interference received at any point in the complex multi-tier environment can be analyzed by considering the same point in the equivalent single-tier cellular network model, thus significantly simplifying the analysis. On this basis, we propose a new user association scheme in heterogeneous cellular networks, where the base station that leads to the smallest interference to other co-channel mobile stations is chosen from a set of candidate base stations that satisfy the quality-of-service (QoS) constraint for an intended mobile station. Numerical results show that the proposed user association scheme is able to significantly reduce the down-link interference compared with existing schemes while maintaining a reasonably good QoS. [ABSTRACT FROM AUTHOR]

In this paper, we analyze a hybrid Heterogeneous Cellular Network (HCNet) framework by deploying millimeter Wave (mmWave) small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate. We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations (SBSs) are deployed in the areas with high User Equipment (UE) density. Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs. For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication. By using tools from stochastic geometry, we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association. For UE clustering we considered Thomas cluster process and derive expressions for the association probability, coverage probability, area spectral efficiency, and energy efficiency. We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions. Furthermore, we analyze the impact of mmWave operating frequency, antenna gain, small cell biasing, and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets. Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

In recent years, Full Duplex cellular systems are under investigation. Previous works found that in full duplex systems, rate does not double because of not only self interference but also due to full duplex BSs interference. In this paper, a Hybrid duplex (H) two tier Heterogeneous Cellular Network is considered: Small pico Base Stations (BSs) use full duplex mode while macro BSs run usual Half Duplex transmission to avoid high interference. Different duplexing modes and association criteria are analyzed and their performance compared in terms of coverage, binary rate and Energy Efficiency. The simulation results show that hybrid mode rate and energy efficiency are the highest compared to full or half duplex modes while coverage does not reduce much. From association criteria point of view, binary rate and energy efficiency are better under maximum receive power in average association criteria whereas coverage is better in minimum path loss association criteria. [ABSTRACT FROM AUTHOR]

In this paper, load balancing in two-tier cellular networks is investigated. The network under-study is divided into several zones. The first tier of each zone includes a heavy-loaded Macrocell (i.e., the central cell) and its neighboring cells. The second tier includes Picocells in the area of the zone. We model the load balancing problem in each zone as a Cournot game where the optimal load distribution of each cell is the Nash Equilibrium Solution (NES) of the game. Since the actual load of each cell depends on the initial placement of users and their mobility pattern, a load balancing algorithm called Weighted Distributed Heterogeneous Zone based Load Balancing (W-DHZLB) is proposed which transfers loads between over-loaded and under-loaded cells aiming at approximating the obtained NES. In order to avoid ping-pong effect during hand-overs, inner users are given a higher priority to join a Macrocell compared to the users locating on the edge of the same Macrocell. Therefore, when loads are transferred to a Picocell, it is more likely one of the internal users of the corresponding Macrocell rather than users residing in the neighboring Macrocell. The proposed algorithm reduces the risk of epidemic unbalanced load distribution in heterogeneous networks. Simulation results show that W-DHZLB outperforms a previous load balancing algorithm in the literature. [ABSTRACT FROM AUTHOR]

Due to its tractability, a multitier model of mutually independent Poisson point processes (PPPs) for heterogeneous cellular networks (HCNs) has recently been attracting much attention. However, in reality, the locations of the BSs, within each tier and across tiers, are not fully independent. Accordingly, in this paper, we propose two HCN models with inter-tier dependence (Case 1) and intra-tier dependence (Case 2), respectively. In Case 1, the macro-base station (MBS) and the pico-base station (PBS) deployments follow a Poisson point process (PPP) and a Poisson hole process (PHP), respectively. Under this setup and conditioning on a fixed serving distance (distance between a user and its nearest serving BS), we derive bounds on the outage probabilities of both macro and pico users. We also use a fitted Poisson cluster process to approximate the PHP, which is shown to provide a good approximation of the interference and outage statistics. In Case 2, the MBSs and the PBSs follow a PPP and an independent Matern cluster process, respectively. Explicit expressions of the interference and the outage probability are derived first for fixed serving distance and second with random distance, and we derive the outage performance, the per-user capacity, and the area spectral efficiency (ASE) for both cases. It turns out that the proposed Case 2 model is a more appropriate and accurate model for a HCN with hotspot regions than the multitier independent PPP model since the latter underestimates some key performance metrics, such as the per-user capacity and the ASE, by a factor of 1.5 to 2. Overall, the two models proposed provide good tradeoffs between the accuracy, tractability, and practicability. [ABSTRACT FROM PUBLISHER]

With the explosive growth of mobile data demand, there has been an increasing interest in deploying small cells of higher frequency bands underlying the conventional homogeneous macrocell network, which is usually referred to as heterogeneous cellular networks, to significantly boost the overall network capacity. With vast amounts of spectrum available in the millimeter-wave (mmWave) band, small cells at mmWave frequencies are able to provide multigigabit access data rates, whereas the wireless backhaul in the mmWave band is emerging as a cost-effective solution to provide high backhaul capacity to connect access points of the small cells. In order to operate the mobile network optimally, it is necessary to jointly design the radio access and backhaul networks. Meanwhile, direct transmissions between devices should be also considered to improve system performance and enhance user experience. In this paper, we propose a joint transmission scheduling scheme for the radio access and backhaul of small cells in the mmWave band, termed D2DMAC, where a path selection criterion is designed to enable device-to-device transmissions for performance improvement. In D2DMAC, a concurrent transmission scheduling algorithm is proposed to fully exploit spatial reuse in mmWave networks. Through extensive simulations under various traffic patterns and user deployments, we demonstrate that D2DMAC achieves near-optimal performance in some cases and outperforms other protocols significantly in terms of delay and throughput. Furthermore, we also analyze the impact of path selection on the performance improvement of D2DMAC under different selected parameters. [ABSTRACT FROM PUBLISHER]

Next generation wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented increase in the data rate demand. To address this problem, we propose a utility-based energy-efficient resource allocation algorithm for the downlink transmissions in heterogeneous networks (HetNets). We consider the fractional frequency reuse (FFR) method in order to mitigate the intra- and inter-cell interference. The proposed algorithm divides the resource allocation problem into frequency and power assignment problems and sequentially solves them. The proposed power control algorithm uses the gradient ascent method to control the transmit power of macrocell base stations (MeNBs) as most of the power in the network is consumed there. We present the optimality conditions of the resource allocation problem and the convergence of the proposed algorithm. In order to mitigate the inter-cell interference further, we study the interference pricing mechanisms and obtain an upper bound to the maximum energy efficiency problem including the inter-cell interference contributions. The performance of the proposed algorithm is studied in a Long Term Evolution (LTE) system. Our simulation results demonstrate that the proposed algorithm provides substantial improvements in the energy efficiency and throughput of the network. It is also shown that interference pricing provides only marginal improvements over the proposed algorithm. [ABSTRACT FROM PUBLISHER]