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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]

Device-to-device-based heterogeneous cellular networks (HCNs) have received considerable attention recently. However, connectivity optimization is a challenging problem for the existing network architecture that remains open in the literature because it is affected by many uncertain real-life factors, such as locations of femtocell base stations (FemtoBSs), the trust relationships among user equipments (UEs), and the dynamic characteristics of spectrum. In this paper, we propose a connectivity optimization method that jointly considers the influence of these factors. In this method, we first build a new framework called the trusted device-to-device-based HCN to capture the stochastic characteristics of the above factors through three models: the dynamic FemtoBS employment architecture, the trust-based directed graph model, and the dynamic spectrum graph model. Based on this framework, we then formulate the connectivity optimization problem as the maximization of the average number of connected UEs in a given time by selecting the optimal FemtoBSs. Since the maximization problem is proven to be NP-hard, we propose three heuristic algorithms, namely, the simple greedy algorithm, the submodular greedy algorithm, and the particle swarm optimization based FemtoBSs selection algorithm to obtain the suboptimal solutions. The effectiveness of the proposed algorithms is justified through extensive simulations with differently parameterized factors. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]