Macrocell Users Research Articles

Robust Secrecy Energy Efficiency Optimization for Wireless Powered Heterogeneous Networks Using Distributed ADMM Algorithm

Under the case of imperfect channel state information (CSI), this paper investigates the robust secrecy energy efficiency (SEE) optimization for the heterogeneous networks (HeNets) supported by simultaneous wireless information and power transfer. Specifically, we first consider a two-tier HeNet composed of a macrocell base station (MBS) and several femtocell base stations (FBSs), where the MBS serves multiple macrocell users (MUs) while each FBS serves an information receiver (IR) and an multiple-antenna energy receiver (ER). Meanwhile, a malicious multiple-antenna eavesdropper (Eve) attempts to wiretap the downlink information of MUs and the ER acts as a potential Eve to eavesdrop the confidential information for IR in the same femtocell. To enhance the secrecy performance, artificial noise (AN) is injected into the downlink information beams of the MBS and FBSs. By considering the user fairness, the problem of SEE maximization of the whole network is formulated via a cross-tier multi-cell coordinated beamforming design. The resulting problem contains infinite constraints caused by CSI error, which is nonconvex and cannot be solved directly. To this regard, we resort to the successive convex approximation, S-procedure and semi-definite relaxation techniques to obtain a solvable form of it. Furthermore, to reduce the overhead of information exchange among coordinated BSs, we develop a distributed solution based on alternative direction multiplier method (ADMM) that can achieve a good approximation performance. Finally, simulation results verify the validity of the proposed AN-aided cross-tier multi-cell coordinated beamforming design and distributed ADMM-based design.

Tier‐aware joint subchannel and power allocation in uplink OFDMA heterogeneous networks

AbstractWe study the problem of tier‐aware subchannel and power allocation in the uplink of a two‐tier orthogonal frequency‐division multiple access heterogeneous network. We formulate the joint subchannel and power allocation problem in the macro‐tier as an optimization problem that is aware of the existence of the femto‐tier and aims to maximize the sum of tolerable interference caused by femto‐tier on its allocated subchannel(s) subject to the minimum data rate requirements of the macrocell user equipments (MUEs). The resource allocation problem for the macro‐tier is an NP‐hard mixed‐integer nonlinear programming (MINLP) problem. To address it, we reformulate and transform it to a tractable mixed‐integer linear programming (MILP) problem, which is optimally addressed with polynomial‐time complexity. We formulate the joint subchannel and power allocation problem in the femto‐tier as an optimization problem that is aware of the existence of the macro‐tier and aims to maximize the sum rate of the femtocell user equipments subject to the maximum tolerable interference caused to the MUEs. To address this MINLP problem, we transform it to a MILP problem through a linear approximation, which is solved optimally by IBM CPLEX solver. In addition, we develop a distributed and efficient algorithm that addresses this optimization problem suboptimally with a lower computational complexity. Numerical results show that our proposed algorithms outperform the existing algorithms in terms of network sum rate.

Energy Efficient and Robust Beamforming for MISO Cognitive Small Cell Networks

This paper studies a cognitive small cell downlink network, where one cognitive base station (CBS) transmits information to a cognitive user and transfers energy to energy harvesting receivers (EHRs). The spectrum sensing interval, the spectrum sensing time, and the beamforming matrices of the CBS are jointly optimized to maximize the energy efficiency (EE) of the CBS and to minimize the energy cost of the CBS under both the bounded channel state information (CSI) model and the probabilistic CSI model. The interference constraints of the macrocell users, the secrecy rate constraint, the transmit power constraint of the CBS and the energy harvesting constraints of the EHRs are all considered in this paper. All the three formulated optimization problems are nonconvex, for which semidefinite relaxation, a 2-D line search method, S-Procedure, and Bernstein-type inequalities are exploited. This paper also derives the conditions under which the EE maximization problem and the energy cost of the CBS minimization problem using the bounded CSI model have rank-one solutions. Simulation results demonstrate that the proposed algorithms have significant gain on the CBS EE under the perfect CSI and also gain on the CBS energy cost under imperfect CSI, all compared to the benchmark scheme.

Quality-of-Service Aware Game Theory-Based Uplink Power Control for 5G Heterogeneous Networks

The fifth-generation (5G) mobile communication system is expecting to support users with diverse data rate requirements by densely deploying small cells. The users attached with small cells make use of the same frequency band as the existing macro cell users, that causes severe co-channel interference and degrades the performance. To overcome this challenge, we propose a game theoretical framework for the optimal uplink power allocation for small cells, i.e., femtocell deployed underlaid macrocell. In this paper, femtocell users play a non-cooperative game to choose the optimal power to maximize the sum-rate of the system. Furthermore, an iterative quality-of-service (QoS)-aware game theory based power control (QoS-GTPC) scheme is proposed to optimize the femtocell user power taking into account macrocell user QoS requirements. Simulation results verify that the proposed QoS-GTPC scheme significantly improves the sum-rate and reduces outage and interference, as compared with conventional power control scheme.

Robust power allocation for two-tier heterogeneous networks under channel uncertainties

In this paper, the trade-off among system sum energy consumption and robustness is studied. In this regard, a robust power allocation problem is formulated for a two-tier heterogeneous network with uplink transmission mode and consideration of imperfect channel state information. The objective is to minimize the total transmit power of femtocell users (FUs), while the interference to macrocell user receiver is limited to a predefined interference level, the transmit power of each FU transmitter is kept within their power budgets, and the actual signal-to-interference-plus-noise ratio of each femtocell receiver is above a minimum threshold. Considering the uncertainties of the interference links from FUs to macrocell base stations and forward transmission links of each FU, the robust power allocation problem is formulated as a semi-infinite programming problem (SIPP). By the worst-case approach, the SIPP is transformed into a convex optimization problem solved by the Lagrange dual decomposition method. Moreover, the feasible regions of constraints, computational complexity, and sensitivity degree of the proposed robust algorithm are also analyzed. Simulation results investigate the impact of channel uncertainties and the superiority of the proposed algorithm by comparing with non-robust algorithm.

Joint Subchannel and Power Allocation in Secure Transmission Design for Femtocell Networks

In this paper, a joint subchannel and power allocation problem is investigated for a downlink two-tier femtocell network system, where there exists an eavesdropper wiretapping a legitimate macrocell user equipment. We aim at improving security of a macrocell user using the existing beneficial interferences from other users to eavesdropper in dense femtocell networks. Cochannel interference in the same subchannel is considered to improve the secure transmission performance. The purpose of the problem under investigation is to propose a secure transmission scheme with joint subchannel assignment and power allocation. In the scheme, a subchannel assignment algorithm is proposed to search optimal subchannel assignment strategies. Furthermore, a noncooperative game framework with switch utility functions is formulated based on different service requirements of femtocell networks. Transmission securities of legitimate users are improved without consuming extra transmit power. Then, iterative algorithms for the problem are given. Finally, numerical results are presented to illustrate the validity of the proposed secure transmission scheme.

Secrecy Transmission for Femtocell Networks Against External Eavesdropper

A femtocell network which is supported by a macrocell base station and some femtocell base stations provides more reliable transmission, higher wireless capacity, and broader coverage. However, it may face eavesdropping risk, which provides an eavesdropper with a chance to overhear a macrocell user’s confidential information. In this paper, we study a secrecy transmission problem for a downlink two-tier femtocell network with imperfect channel state information (CSI), where an eavesdropper wiretaps the legitimate macrocell user. More specifically, we aim to maximize the secrecy rate by jointly optimizing the power allocation and quality-of-service (QoS) requirement in terms of outage probability. We consider two types of CSI, i.e., instantaneous and statistic CSI, respectively, which can robustly guarantee the QoS of users in a complex communication environment. For the instantaneous CSI communication environment, where there exist estimated errors between instantaneous channel gains and their estimated values, we propose a novel conversion method to extract the approximate closed-form expressions of outage probability constraints. For the statistic CSI communication environment, where channel gains obey Rayleigh fading, we design a new method to obtain deterministic expressions by considering the expectations and variances of instantaneous channel gains. Then, the uncertainty and non-convexity of objective function are solved with the aid of variable substitution and Taylor expansion. Moreover, two iterative algorithms are proposed to derive the optimal transmission powers. Finally, we evaluate the proposed algorithms using large scale simulations, and present extensive evaluation results to demonstrate the effectiveness of our proposed algorithms.

Effects of Base-Station Spatial Interdependence on Interference Correlation and Network Performance

The spatial-and-temporal correlation of interference has been well-studied in Poisson networks, where the interfering base stations (BSs) are independent of each other. However, there exists spatial interdependence including attraction and repulsion among the BSs in practical wireless networks, affecting the interference distribution and hence the network performance. In view of this, by modeling the network as a Poisson clustered process, we quantify the effects of spatial interdependence among BSs on the interference correlation and analytically prove that BS clustering increases the level of interference correlation. In particular, it is shown that the level is a monotone-increasing function of the mean number of BSs in each cluster and a monotone-decreasing function of cluster radius, but is independent of the locations of the clusters. Furthermore, we study the effects of spatial interdependence among BSs on network performance with Type-I HARQ retransmission scheme via considering heterogeneous cellular networks in which small-cell BSs exhibit a clustered topology in practice. We derive the numerically integrable expressions and their bounds for the joint success probabilities, defined as the success probability in multiple successive transmissions, for macro-cell users and small-cell users. It is shown that BS clustering improves the performance of macro-cell users. Further, the level is enhanced by the repulsion between the BSs from different tiers.

Resource Allocation for 5G Heterogeneous Cloud Radio Access Networks With D2D Communication: A Matching and Coalition Approach

Device-to-Device (D2D) communication is a promising technology toward the fifth generation mobile communication. However, when D2D communication is incorporated into heterogeneous cloud radio access network (H-CRAN), the interference management between the D2D users and current users is a challenge. In this paper, we study how to assign the sub-channels of different bandwidth to multiple D2D pairs and the remote radio head users. In such a way, the sub-channels that have been pre-allocated to macro-cell users can be reused, the system performance can be maximized while the quality of service of all users can be guaranteed. Such a resource allocation problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To obtain the solution, the proposed problem is reformulated into a many-to-one matching sub-game with externality followed by a coalition sub-game. Then, a constrained deferred acceptance algorithm and a coalition formation algorithm are proposed to find solutions to these two sequential sub-games, respectively. Finally, we prove theoretically that both the proposed algorithms can convergent with a low computational complexity. Numerical results demonstrate that compared with existing resource allocation schemes, our proposed scheme can significantly improve the system performance in terms of overall throughput, the total number of admitted users and fairness.

A Physical-layer Security Scheme Based on Cross-layer Cooperation in Dense Heterogeneous Networks

In this paper, we investigate secure communication with the presence of multiple eavesdroppers (Eves) in a two-tier downlink dense heterogeneous network, wherein there is a macrocell base station (MBS) and multiple femtocell base stations (FBSs). Each base station (BS) has multiple users. And Eves attempt to wiretap a macrocell user (MU). To keep Eves ignorant of the confidential message, we propose a physical-layer security scheme based on cross-layer cooperation to exploit interference in the considered network. Under the constraints on the quality of service (QoS) of other legitimate users and transmit power, the secrecy rate of system can be maximized through jointly optimizing the beamforming vectors of MBS and cooperative FBSs. We explore the problem of maximizing secrecy rate in both non-colluding and colluding Eves scenarios, respectively. Firstly, in non-colluding Eves scenario, we approximate the original non-convex problem into a few semi-definite programs (SDPs) by employing the semi-definite relaxation (SDR) technique and conservative convex approximation under perfect channel state information (CSI) case. Furthermore, we extend the frame to imperfect CSI case and use the Lagrangian dual theory to cope with uncertain constraints on CSI. Secondly, in colluding Eves scenario, we transform the original problem into a two-tier optimization problem equivalently. Among them, the outer layer problem is a single variable optimization problem and can be solved by one-dimensional linear search. While the inner-layer optimization problem is transformed into a convex SDP problem with SDR technique and Charnes-Cooper transformation. In the perfect CSI case of both non-colluding and colluding Eves scenarios, we prove that the relaxation of SDR is tight and analyze the complexity of proposed algorithms. Finally, simulation results validate the effectiveness and robustness of proposed scheme.

Price-Based Power Allocation in Two-Tier Spectrum Sharing Heterogeneous Cellular Networks

Attributable to the using of the same spectrum resources, heterogeneous cellular networks have serious interference problems, which greatly restricts the performance of the network. In this paper, the price-based power allocation for femtocells underlaying a macrocell heterogeneous cellular network is investigated. By exploiting interference pricing mechanism, we formulate the interference management problem as a Stackelberg game and make a joint utility optimization of macrocells and femtocells. Specially, the energy consumption of macrocell users and the transmission rate utility of femtocell users are considered in this utility optimization problem. In the game model, the macrocell base station is regarded as a leader, which coordinates the interference from femtocell users to the macrocell users by pricing the interference. On the other hand, the femtocell base stations are modelled as followers. The femtocell users obtain their power allocation by pricing. After proving the existence of the Stackelberg equilibrium, the non-uniform and uniform pricing schemes are proposed, and distributed interference pricing algorithm is proposed to address uniform interference price problem. Simulation results demonstrate that the proposed schemes are effective on interference management and power allocation.

Robust resource allocation for heterogeneous wireless network: a worst‐case optimisation

With the development of fifth generation wireless communication technology, how to improve system capacity and spectrum efficiency is a crucial problem in resource sharing of heterogeneous networks (HetNets). Most of existing resource allocation (RA) schemes in HetNets focus on perfect channel state information, however, exact channel information is difficult to obtain under link delay and stochastic channel condition. In order to resolve the RA issues under channel uncertainties, a robust RA algorithm is proposed to maximise the sum data rate of microcell users where the users are subjected to the individual transmission power constraint and the cross-tier interference constraint of macrocell users. The multiuser RA problem in HetNets is formulated under the consideration of bounded channel gain uncertainties where both the cross-tier channel and intra-tier channel are simultaneously considered. The non-linear optimisation problem is converted into a geometric programming problem that is solved by using Lagrange dual methods in a distributed way. Simulation results show that the proposed algorithm can well restrain the effect of channel uncertainty and achieve a good robustness.

Location-Aware Incentive Mechanism for Traffic Offloading in Heterogeneous Networks: A Stackelberg Game Approach.

This article investigates the traffic offloading problem in the heterogeneous network. The location of small cells is considered as an important factor in two aspects: the amount of resources they share for offloaded macrocell users and the performance enhancement they bring after offloading. A location-aware incentive mechanism is therefore designed to incentivize small cells to serve macrocell users. Instead of taking the amount of resources shared as the basis of the reward division, the performance promotion brought to the macro network is taken. Meanwhile, in order to ensure the superiority of small cell users, the significance of them weighs heavier than macrocell users instead of being treated equally. The offloading problem is formulated as a Stackelberg game where the macro cell base station is the leader and small cells are followers. The Stackelberg equilibrium of the game is proved to be existing and unique. It is also proved to be the optimum of the proposed problem. Simulation and numerical results verify the effectiveness of the proposed method.

Cell Selection for Load Balancing in Heterogeneous Networks

The vision of advanced long-term evolution (LTE-A) project is set to ultimate increase of network capacity in heterogeneous networks (HetNets). In HetNets with small cell configuration, a considerable majority of user devices is eventually connected to the macrocell base station (MBS), while small base stations (BSs), such as femtocell access points (FAPs), are still without any user. This results in unbalanced load and reduces the data rate of macrocell user equipment (MUE). In this paper, a method is proposed for load balancing among FAPs, while desired throughput is achieved. The proposed method uses the estimated received signal strength from different BSs and adjusted pilot signals. Under the critical signal to interference plus noise ratio (SINR) condition, a list of candidate FAPs is prepared. The updated candidate list henceforth does not include the least visited FAPs, which in turn leads to lower unnecessary handoffs. Once the BS with the highest number of free RBs and the highest pilot signal power is selected, FAP allocates the RBs with higher SINRs (qualified RBs) to user. In the case of FAP unavailability, the algorithm compels users to connect to the MBS with adequate qualified RBs. The performance of the proposed method was evaluated under a variety of FAPs density, and the number and velocity of users in terms of throughput and Jain’s fairness index. The results evidence affordable improvements in the throughput and Jain’s index in comparison with other methods.

Joint Resource Allocation and Dynamic Activation of Energy Harvesting Small Cells in OFDMA HetNets

We jointly optimize resource allocation with the dynamic activation of energy harvesting base stations in a two-tier orthogonal frequency-division multiple access-based heterogeneous network. We consider both energy harvesting constraints and interference constraints along with time-variation in channel condition, user activity, and energy arrival. We optimize the trade-off between throughput performance of the small cell (or hotspot) users and the associated power cost by maximizing the net reward, where positive reward is associated with achievable throughput of the hotspot users and negative reward with the corresponding non-renewable power consumption. Quality-of-service requirements of hotspot users as well as macrocell users are considered in the optimization problem. Assuming the availability of non-causal information, we propose offline resource allocation algorithm using discrete binary particle swarm optimization and dual decomposition technique. Assuming the availability of statistical information of future values, we propose dynamic programming-based online algorithm. Finally, we propose simple and greedy online algorithm assuming lack of any kind of future information. Numerical results demonstrate the performances of the proposed offline, dynamic programming-based online, and greedy online algorithms and highlight the scenarios, where the performance of the proposed algorithms is significantly better than the baseline schemes.

Techniques for Interference Mitigation Using Cooperative Resource Partitioning in Multitier LTE HetNets

Almost blank subframes (ABSs) have been proposed in the literature as a means to provide interference-free resources to mobile users. The use of ABS becomes inevitable in LTE heterogeneous cellular networks because of severe interference to small cells. Picocell edge users are typically the victims because of high interference exposure from macrocell tier. In case of hotspots with high-user density, picocells cannot provide coverage to the entire hotspot, thus forming a dense ring of macrocell users around picocells. These macrocell users are highly affected by picocell tier and hence should also be considered as victim users (VUEs). We propose that to protect these macrocell VUEs, macrocells and picocells should operate in a cooperative manner, such that not only macrocells should use ABS, but picocells should also use ABS to provide interference-free resources. We also introduce the concept of universal blanking pattern to coordinate among various base stations. We propose two methods for joint ABS density calculation for macrocells and picocells—bitrate utility-based optimization and physical resource block (PRB) allocation ratio-based formulation. Users with different bitrate demands are considered generating more realistic simulation scenarios. Exhaustive simulations illustrate that the proposed schemes offer significant improvement in VUE throughput as well as overall system throughput.

Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique

The overlay integration of low-power femtocells over macrocells in a heterogeneous network (HetNet) plays an important role in dealing with the increasing demand of spectral efficiency, coverage and higher data rates, at a nominal cost to network operators. However, the downlink (DL) transmission power of an inadequately deployed femtocell causes inter-cell interference (ICI), which leads to severe degradation and sometimes link failure for nearby macrocell users. In this paper, we propose an adaptive network sensing (ANS) technique for downlink power control to obviate the ICI. The simulation results have shown that the ANS power control technique successfully decreases the cell-edge macro user’s interference and enhances the throughput performance of macro users, while also optimizing the coverage and capacity of the femtocell. When compared with the Femto User Equipment (FUE)-assisted and Macro User Equipment (MUE)-assisted power control technique, the proposed technique offers a good tradeoff in reducing interference to macro users, while maintaining the quality of service (QoS) requirement of the femtocell users.

Robust Rate Maximization for Heterogeneous Wireless Networks under Channel Uncertainties.

Heterogeneous wireless networks are a promising technology in next generation wireless communication networks, which has been shown to efficiently reduce the blind area of mobile communication and improve network coverage compared with the traditional wireless communication networks. In this paper, a robust power allocation problem for a two-tier heterogeneous wireless networks is formulated based on orthogonal frequency-division multiplexing technology. Under the consideration of imperfect channel state information (CSI), the robust sum-rate maximization problem is built while avoiding sever cross-tier interference to macrocell user and maintaining the minimum rate requirement of each femtocell user. To be practical, both of channel estimation errors from the femtocells to the macrocell and link uncertainties of each femtocell user are simultaneously considered in terms of outage probabilities of users. The optimization problem is analyzed under no CSI feedback with some cumulative distribution function and partial CSI with Gaussian distribution of channel estimation error. The robust optimization problem is converted into the convex optimization problem which is solved by using Lagrange dual theory and subgradient algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm by the impact of channel uncertainties on the system performance.

Proactive caching with content‐based pricing for cooperative femtocells in two‐tier heterogeneous networks

SummaryWith increase in the number of smart wireless devices, the demand for higher data rates also grows which puts immense pressure to the network. A vast majority of this demand comes from video files, and it is observed that only a few popular video files are requested more frequently during any specified time interval. Recent studies have shown that caching provides a better performance as it minimizes the network load by avoiding the fetching of same files multiple times from the server. In this paper, we propose to combine two ideas; proactive caching of files and content‐based pricing in macro‐femto heterogeneous networks. The femtocell access point (FAP) is allowed to manipulate its users' demand through content‐based pricing and serve the users' requests by proactively downloading suitable content into its cache memory which reduces the load of the femtocell. In addition, an incentive mechanism is also proposed which encourages the FAP to help macrocell users under its coverage zone by allowing access to its cached content and thereby reduces the macrocell load. The proposed content‐based pricing and proactive caching scheme for femtocells is modeled as a Stackelberg game among the macrocell base station and the FAP to jointly maximize both of their utilities. Performance analysis of the scheme is presented for a single femtocell scenario and compared with the conventional flat pricing‐based scheme via numerical examples. The results demonstrate significant reduction in network load using our proposed scheme.

Resource Allocation in Reverse TDD Wireless Backhaul HetNets With 3D Massive Antennas

This letter investigates resource allocation for users and small cell wireless backhaul in a reverse time division duplexing heterogenous network with 3D massive antennas at the macrocell base station. We first formulate the problem to maximize the summation of the logarithmic throughputs of all users, including the small cell users and macrocell users, considering backhaul limitation and resource allocation. To address the non-convex and non-linear optimization problem, we develop an iterative algorithm based on primal decomposition to find its solution. Simulation results demonstrate that the proposed algorithm benefits both cell edge and cell average performance.