Optimization In Heterogeneous Networks Research Articles

Energy Efficiency and Load Optimization in Heterogeneous Networks through Dynamic Sleep Strategies: A Constraint-Based Optimization Approach

Energy Efficiency and Load Optimization in Heterogeneous Networks through Dynamic Sleep Strategies: A Constraint-Based Optimization Approach

A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks

Mobility robustness optimization (MRO) is a fundamental issue regarding self-optimization network (SON). In long-term evolution-advanced (LTE-A), handover (HO) optimization in heterogeneous networks (HetNets) is an urgent need to improve system performance. This improvement is in terms of immunity against unnecessary HO (UHO) such as ping-pong HO (PPHO) and against failed HOs in the sense of radio link failure (RLF) such as too-early HO (TEHO) and too-late HO (TLHO), that is, RLF HO. The occurrence of these undesired HOs increases the consumption of network resources and decreases the quality of service (QoS). In this study, we propose a robust algorithm to reduce the number of PPHO, TLHO, and TEHO events to a minimum by an innovative mechanism that adaptively sets the HO control parameters (HCPs). This reduction is obtained without the need for unjustified techniques that assume certain thresholds for the ratio of PPHOs or the ratio of RLF HOs relative to the total number of HOs, as proposed in the most recent literature. We also present the importance of thinking about the existence of a direct relationship between the hysteresis margin (HM) and time-to-trigger (TTT). We invest this relationship in determining adaptive HCPs. Simulation results show that RLF HOs and PPHOs are minimal, almost zero, compared to the literature and the classical method. This study opens a new avenue for research on mobility management. It reorients research axes toward thinking about the possibility of a correlation between HM and TTT. This is what the research community has not yet realized.

QoS controlled capacity offload optimization in heterogeneous networks

An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap. User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

Heterogeneous network optimization using robust power-and-resource based algorithm

<p>In order to meet the increasing mobile data-traffic, spatial densification of network with several low-power nodes, the high-power macro BS and HetNet are the major key enabling solution. However, the HetNet is unplanned in nature, causes irregularities and interferences that without any user association rules. The appropriate deployment of the femto-cell in HetNet can provide effective traffic offloading, where the alleviate mobbing in the macro-cells can decrease the power consumption therefore it optimizes the user experience. Moreover, the protection is also important for the macro and femto cell users in a network through maintaining the min-max level of interferences. In this paper, we proposed RPRA that comprises two robust approach such as robust power-controller and the robust channel-allocation approach, which can improve the spectral efficiency and user experiences at lower network coverage areas via eliminating the week coverage zones. Also provide high user rate connection by effective interference in an efficient spectrum, lowering in transmission power and cost-effectiveness via less time delay. To show the effectiveness of our proposed model we have compared with several existing techniques and we got significant improvement in throughput, also reduction in time delay and transmission power.</p>

Secrecy energy efficiency optimization in heterogeneous networks with simultaneous wireless information and power transfer

In this paper, we investigate the secrecy energy efficiency (SEE) optimization for a heterogeneous network with simultaneous wireless information and power transfer, where multiple femtocell base stations (FBSs) are deployed under the coverage of one macrocell base station (MBS). Meanwhile, the MBS serves multiple macrocell users (MUs) in the presence of a malicious multiple-antenna eavesdropper (Eve) while each FBS serves a pair of information receiver (IR) and energy receiver (ER), where the ER with multiple antennas acts as a potential Eve to wiretap the information transmitted to IR in the same femtocell. Then, to promote the secrecy performance, artificial noise (AN) is injected into the downlink transmit signal at the MBS and FBSs. Furthermore, with the aim of achieving green and secure communications in 5G, the problem of maximizing average SEE performance of the whole network is reformulated while considering cross-tier multi-cell transmit beamforming (TBF) design. To obtain the optimal design, we resort to the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques to reformulate this nonconvex max–min fractional program problem into a series of solvable forms. Subsequently, an iterative algorithm is proposed to arrive at provably convergent design. Finally, simulation results validate the effectiveness and security of the proposed AN-aided TBF design.

Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks

We consider expected risk minimization in multi-agent systems comprised of distinct subsets of agents operating without a common time scale. Each individual in the network is charged with minimizing the global objective function, which is an average of sum of the statistical average loss function of each agent in the network. Since agents are not assumed to observe data from identical distributions, the hypothesis that all agents seek a common action is violated, and thus the hypothesis upon that consensus constraints are formulated is violated. Thus, we consider nonlinear network proximity constraints, which incentivize nearby nodes to make decisions that are close to one another but do not necessarily coincide. Moreover, agents are not assumed to receive their sequentially arriving observations on a common time index, and thus seek to learn in an asynchronous manner. An asynchronous stochastic variant of the Arrow–Hurwicz saddle point method is proposed to solve this problem that operates by alternating primal stochastic descent steps and Lagrange multiplier updates that penalize the discrepancies between agents. This tool leads to an implementation that allows for each agent to operate asynchronously with local information only and message passing with neighbors. Our main result establishes that the proposed method yields convergence in expectation both in terms of the primal sub-optimality and constraint violation to radii of sizes ${\mathcal O}(\sqrt{T})$ and ${\mathcal O}(T^{3/4})$ , respectively. Empirical evaluation on an asynchronously operating wireless network that manages user channel interference through an adaptive communications pricing mechanism demonstrates that our theoretical results translates well to practice.

Caching in Heterogeneous Networks With Per-File Rate Constraints

We study the problem of caching optimization in heterogeneous networks with mutual interference and per-file rate constraints from an energy efficiency perspective. A setup is considered in which two cache-enabled transmitter nodes and a coordinator node serve two users. We analyze and compare two approaches: 1) a cooperative approach where each of the transmitters might serve either of the users and 2) a non-cooperative approach in which each transmitter serves only the respective user. We formulate the cache allocation optimization problem so that the overall system power consumption is minimized while the use of the link from the master node to the end users is spared whenever possible. We also propose a low-complexity optimization algorithm and show that it outperforms the considered benchmark strategies. Our results indicate that significant gains both in terms of power saving and sparing of master node's resources can be obtained when full cooperation between the transmitters is in place. Interestingly, we show that in some cases storing the most popular files is not the best solution from a power efficiency perspective.

Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC.

Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.

Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks With Mobile Edge Computing

In this paper, we propose a distributed joint computation offloading and resource allocation optimization (JCORAO) scheme in heterogeneous networks with mobile edge computing. An optimization problem is formulated to provide the optimal computation offloading strategy policy, uplink subchannel allocation, uplink transmission power allocation, and computation resource scheduling. The optimization problem is decomposed into two sub-problems due to the NP-hard property. In order to analyze the offloading strategy, a sub-algorithm named distributed potential game is built. The existence of Nash equilibrium is proved. To jointly allocate uplink subchannel, uplink transmission power, and computation resource for the offloading mobile terminals, a sub-algorithm named cloud and wireless resource allocation algorithm is designed. The solutions for subchannel allocation consist of uniform zero frequency reuse method without interference and fractional frequency reuse method based on Hungarian and graph coloring with interference. A distributed JCORAO scheme is proposed to solve the optimization problem by the mutual iteration of the two sub-algorithms. Simulation results show that the distributed JCORAO scheme can effectively decrease the energy consumption and task completion time with lower complexity.

Utility optimization in heterogeneous networks via CSMA-based algorithms

We study algorithms for carrier and rate allocation in cellular systems with distributed components such as a heterogeneous LTE system with macrocells and femtocells. Existing work on LTE systems often involves centralized techniques or requires significant signaling, and is therefore not always applicable in the presence of femtocells. More distributed CSMA-based algorithms (carrier-sense multiple access) were developed in the context of 802.11 systems and have been proven to be utility optimal. However, the proof typically assumes a single transmission rate on each carrier. Further, it relies on the CSMA collision detection mechanisms to know whether a transmission is feasible. In this paper we present a framework for LTE scheduling that is based on CSMA techniques. In particular we first prove that CSMA-based algorithms can be generalized to handle multiple transmission rates in a multi-carrier setting while maintaining utility optimality. We then show how such an algorithm can be implemented in a heterogeneous LTE system where the existing Channel Quality Indication (CQI) mechanism is used to decide transmission feasibility.

Resource Allocation for Energy Efficiency Optimization in Heterogeneous Networks

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

On the use of radio environment maps for interference management in heterogeneous networks

This article addresses the use of REMs to support interference management optimization in heterogeneous networks composed of cells of different sizes and including both cellular and non-cellular (e.g. WiFi) technologies. After presenting a general architecture for including REM databases in different network entities, the article analyzes the achievable benefits in relation to specific interference management techniques, including a discussion on practical considerations such as information exchange requirements, REM ownership, and security aspects. Finally, several research directions derived from the proposed framework are identified.

Robust Time Reversal-based Transmit Optimization for Green Heterogeneous Networks

Time reversal (TR) beamforming, considered as one of most prominent linear precoders, has showed the feasibility in conventional communications. In this paper, we propose the TR-based transmit optimization for heterogeneous networks (HetNets) where multiple femtocells apply TR precoders over frequency selective channels, with the signal-to-inference-plus-noise ratio (SINR) and interference constraints at single-antenna femtocell user devices. In multi-femtocell network environments, we exhibit that TR outperforms the well-known zero-forcing beamforming. Particularly, we focus on the practical case of imperfect channel estimation at femtocell base stations. The robust optimization methodology is hence devised by taking into account the average effect of channel estimation error (CEE) on system performance. Under the assumption of estimation error in time-varying channels, we derive the exact closed-form expressions of the desired signal, inter-symbol interference (ISI), inter-user interference (IUI), co- and cross-layer interference terms, respectively. These derivations allow us to tackle the proposed robust algorithm by convex optimization techniques. In final, numerical results are shown to confirm the validation of our proposal. DOI: http://dx.doi.org/10.5755/j01.eee.21.3.10431

Cognitive Network Management Framework and Approach for Video Streaming Optimization in Heterogeneous Networks

The future Internet will be highly heterogeneous in supporting a multitude of access technologies and networks with overlapping coverages. Optimization of network operations like management of resources, mobility or Quality of Service in order to ensure smooth network operation and high user satisfaction will be very challenging in the future networks. Cognitive network management can provide a solution of managing such complex systems. This paper studies cognitive network management in the context of optimizing video streaming performance in heterogeneous multi-access networks. The paper proposes a network management framework that relies on cognitive decision techniques in the joint optimization of network and video service performance. The proposed solution is also implemented and validated in part in a testbed environment. The results attest the feasibility of the solution as well as the benefits of cognitive decision techniques over non-learning or non-adaptive approaches.

Deployment analysis and optimization of heterogeneous networks under the spectrum underlay strategy

To meet the growing mobile traffic demand, deployment of heterogeneous networks (HetNets) is proposed as a solution to enhance network capacity. It will enable the coexistence of several systems consisting of Macro, Micro, and Femto or WiFi layers. However, the main issue is the coexistence due to mutual interference. To guarantee that HetNet performance will not be compromised due to the interference, some deployment parameters should be controlled, such as primary exclusion zones and density of users. In this context, the present work provides deployment analysis and optimization for a Macro-Femto scenario, considering interference constraints to maximize the data rate of the HetNet. To fulfill this, we characterize some spectrum underlay scenarios in which HetNets would be expected to operate, setting different signal-to-interference-plus-noise ratio (SINR) thresholds. They are evaluated using a solution procedure based on the socio-cognitive particle swarm optimization (SCPSO) algorithm. Considering the best scenarios found, we show deployment parameters to achieve maximum system throughput, such as density of femto-users and primary exclusion zones of macro-users.

Study on Resource Optimization for Heterogeneous Networks

Study on Resource Optimization for Heterogeneous Networks

Handover Outage Time Optimization in Heterogeneous Networks

Handover is a very critical parameter in Cellular Networks. The outage handover time is a drawback for Quality of Service in wireless voice applications and heterogeneous cellular wireless networks. Apart from existing handover technique, a novel approach has been developed known as Network Assisted Handover improving the outage handover time and the fluctuations of data throughput.