Macro-femtocell Networks Research Articles

A Trade-Off Between Energy Efficiency and Spectral Efficiency in Macro-Femtocell Networks

Obtaining large spectral efficiency (SE) and energy efficiency (EE) subject to quality of experience (QoE) is one of the prime concerns for the wireless next generation networks, however a major confrontation with its trade-off which is becoming apparent while optimizing both SE and EE parameters concurrently. In this work, an analytical framework for a cognitive-femtocell network is proposed to be dealt with and overcome the situations regarded as unwelcome. Here, the conflict of SE-EE trade-off in downlink (DL) transmission is expressed methodically by Pareto Optimal Set (POS) based on a multi-empirical most effective use of a resource scheme as a function of femto base station (FBS) and macro base station (MBS) transmit power and base station (BS) density, respectively. Then, SE and EE are formulated in a utility function by applying Cobb-Douglas production function to transform the multi-empirical difficulty into the single-empirical optimization case. Besides, it is analytically shown that the SE-EE trade-off can be optimize through a distinctive universal optimum among the Pareto optimal by fine-tuning the weighting metric other than BS transmit power and density, respectively. Simulation results validate that it is possible to obtain the EE-SE trade-off with SINR threshold at different weighting factor.

LFMTCN: A Green Ultra-Dense Multi-tier Small Cell Network Using Leader–follower Strategy

Design of energy and spectrum-efficient multi-tier small cell network is an emerging research area. This paper has addressed the challenges of power optimization and interference mitigation in small cell based multi-tier network. We propose a macro–pico–femtocell and micro–pico–femtocell based leader–follower network, referred as leader follower based multi-tier cellular network, where picocells and femtocells form groups inside the macrocell and microcell. The picocell serves as leader and femtocells as followers in each pico–femtocell group. Frequency allocation for this network is also proposed. Simulation results demonstrate that the proposed network has up to 3.58% less power consumption than the macro–pico–femtocell network. Simulation results also illustrate that using the proposed multi-tier network the signal-to-interference-plus-noise ratio for femtocell user is improved by 53.9–60.64% and 23.18–28.16% approximately than the macro–femtocell network and micro–femtocell network respectively.

A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications

Cellular vehicle-to-everything (C-V2X) communication facilitates the improved safety, comfort, and efficiency of vehicles and mobility by exchanging information between vehicles and other entities. In general, only the macrocell or only the femtocell is the communication infrastructure for C-V2X. Currently, a macro-femtocell network is used as the new C-V2X networking architecture. However, there are two unresolved problems for C-V2X in macro-femtocell networks. Firstly, vehicle mobility requires the frequent switching of connections between different base stations; invalid switching results in worse communication quality. Secondly, unintelligent base station selections cause network congestion and network-load imbalance. To address the above challenges, this paper proposes a base station selection strategy based on a Markov decision policy for a vehicle in a macro-femtocell system. Firstly, we present a mechanism to predict received signal strength (RSS) for base station selection. Secondly, a comparing Markov decision policy algorithm is presented in C-V2X. To the best of our knowledge, this is the first attempt to achieve predicted RSS based on a Markov decision policy in C-V2X technology. To validate the proposed mechanism, we simulated the traditional base station selection and our proposal when the vehicle moved at different speeds. This demonstrates that the effectiveness of a traditional base station selection policy is obvious only at high speeds, and this weakness can be resolved by our proposal. Then, we compare our solution with the traditional base station selection policy. The simulation results show that our solution is effective at switching connections between base stations, and it can effectively prevent the overloading of network resources.

Bio-inspired innovative green fault recovery modelling for macro-femtocell mobile network

In an overlay macro-femtocell mobile network, the femtocells are allocated within the macrocell coverage in order to provide good quality of service at indoor environment. Within this network, when a femtocell gets damaged, the adjacent lightly loaded femtocells are searched and the users of the damaged cell are handed over to those lightly loaded adjacent femtocells. However, this increases latency, power consumption and the probability of call dropping. To overcome these difficulties, we propose a red blood cell life cycle-based fault recovery management method which utilises femtocell-to-macrocell and then macrocell-to-femtocell handover to reduce the probability of call dropping, power consumption and latency. We introduce a new database, femtoDB which is stored inside the cloud to be used as a potential repertoire capable to store the femtocell IDs with their users' information in an overlay macro-femtocell network. Based on the information maintained in femtoDB, the macrocell decides to which femtocell the users of the damaged femtocell to be handed over. Simulation results illustrate that the proposed recovery management method reduces the probability of call dropping, power consumption and latency by approximately ~3%, ~75% and ~60% respectively.

Bio-inspired innovative green fault recovery modelling for macro-femtocell mobile network

In an overlay macro-femtocell mobile network, the femtocells are allocated within the macrocell coverage in order to provide good quality of service at indoor environment. Within this network, when a femtocell gets damaged, the adjacent lightly loaded femtocells are searched and the users of the damaged cell are handed over to those lightly loaded adjacent femtocells. However, this increases latency, power consumption and the probability of call dropping. To overcome these difficulties, we propose a red blood cell life cycle-based fault recovery management method which utilises femtocell-to-macrocell and then macrocell-to-femtocell handover to reduce the probability of call dropping, power consumption and latency. We introduce a new database, femtoDB which is stored inside the cloud to be used as a potential repertoire capable to store the femtocell IDs with their users' information in an overlay macro-femtocell network. Based on the information maintained in femtoDB, the macrocell decides to which femtocell the users of the damaged femtocell to be handed over. Simulation results illustrate that the proposed recovery management method reduces the probability of call dropping, power consumption and latency by approximately ~3%, ~75% and ~60% respectively.

Stable femtocells cluster formation and resource allocation based on cooperative game theory

In this paper, we address the problem of forming stable groups of femtocells that can reduce the complexity of the resource management and enhance the subscribers’ satisfaction while guaranteeing the service to nearby public users. In a macro-femtocell network, the resource management becomes a very challenging task as the number of deployed femtocells increases. Several strategies of clustering have been proposed to allocate resources in a distributed manner. However, forming stable clusters of femtocells is yet to be addressed. We propose a distributed cluster-based resource allocation framework that consists of three components: (1) a base station selection algorithm for public users that guarantees them a high data rate, (2) a coalition game, where femtocells are grouped into stable clusters to reduce the resource allocation complexity, and (3) a fair resource allocation using the Shapley value to compute the payoff of each cluster member based on Particle Swarm Optimization algorithm. The ε-core concept from game theory is used as the stability criteria to form the clusters. A performance comparison is carried out between the proposed solution and two benchmark models: a centralized approach and a distributed approach with non-stable group formation. Simulation results show that our framework indeed increases the network throughput, provides higher subscribers satisfaction, and higher Jain fairness index for the distribution of resources among the existing users in the femto-tier.

Dynamic Spectrum Sharing for Hybrid Access in OFDMA-Based Cognitive Femtocell Networks

Cognitive femtocell has emerged as one of the promising ways to solve the indoor coverage and spectrum shortage problems in cellular networks. However, it is a challenge to motivate both macro base station ( $\text{MBS}$ ) and femtocell access points ( $\text{FAPs}$ ) to adopt this hybrid access policy. In this paper, we propose a systematic dynamic spectrum sharing framework for hybrid access in orthogonal frequency-division multiple-access-based cognitive macro-femtocell networks. In this framework, the $\text{MBS}$ offloads some macro user equipments ( $\text{MUEs}$ ) to $\text{FAP}$ to improve the transmission performance and save energy. In return, the MBS leases a portion of its spectrum to $\text{FAP}$ . Then, the $\text{FAP}$ can use a part of spectrum for its own transmission and use another part to serve $\text{MUEs}$ . We design an auction-based approach where each $\text{FAP}$ receives additional $\text{MUE}$ as bidders, while MBS acts as auctioneer. We formulate the dynamic resource sharing as a sum utility maximization problem. Numerical results demonstrate that both the macro and femtocell could get benefit from our framework.

Game theoretical framework for clustering and resource allocation in macro-femtocell networks

We address the femtocell clustering together with the resource allocation in macro-femtocell networks. The clustering schemes allow the implementation of distributed approaches that can run locally within each cluster. Nevertheless, several limitations should be addressed for dense femtocell deployment, such as: lack of clustering schemes that encourage femtocells to grant service to public users and to become cluster members while guaranteeing their subscriber satisfaction, inefficient bandwidth usage due to the lack of bandwidth adaptation per tier when the cluster configuration changes, and lack of power control mechanisms to reduce interference. In this paper, we propose a distributed clustering model based on a cooperative game, where femtocells are encouraged to cooperate by forming clusters and rewarded with resources from macrocell. Our solution consists of: a cluster formation based on a coalitional game among femtocells and the macrocell to determine the subcarrier distribution per tier, a base station selection for public users and a resource allocation algorithm using Particle Swarm Optimization. We compare our solution with a centralized clustering approach and our cooperative clustering model using the well-known Weighted Water Filling resource allocation algorithm. Simulation results show that our proposal obtains throughput values similar to the centralized approach, satisfies the service requirements for both types of users and reduces the interference in comparison with the benchmark models.

User velocity based hand-off prediction in micro-femtocell clustered network

This article presents a strategy for predicting the mean number of hand-offs in a micro-femtocell network. This approach considers the velocity of the mobile terminal across three-dimensions and determines the frequency of hand-off occurrences. Simulation results illustrate that the proposed method increases the satisfaction probability by approximately 55%–90% than the existing borrowing channel assignment approach. Based on the velocity and movement pattern, a new user is handed over to a femtocell within the newly entered microcell where the cooperative frequency allocation is performed. Simulation results demonstrate that the proposed micro-femtocell network reduces the power transmission in the area covered by the base stations by approximately 40% and 25% than the macro-femtocell network, and the macro-femtocell and micro-femtocell clustered network respectively. Simulation results illustrate that the proposed network increases the Signal-to-Interference-plus-Noise Ratio (SINR) by approximately 55–65% and 27–32% than the macro-femtocell network, and the macro-femtocell and micro-femtocell clustered network respectively.

Interference management in macro‐femtocell and micro‐femtocell cluster‐based long‐term evaluation‐advanced green mobile network

This study presents a frequency allocation strategy for macro-femtocell and micro-femtocell cluster-based long-term evaluation-advanced network. In the proposed approach the network is divided into a number of clusters. Each cluster contains a macrocell or a number of microcells. Femtocells are deployed within a macrocell or a microcell. In the proposed strategy fractional frequency reuse is used. Each macrocell and microcell is divided into three regions. Different frequency sets are allocated for macrocell and femtocells in a macro-femtocell cluster. Similarly in a micro-femtocell cluster, different frequency sets are provided to a microcell and the femtocells contained in that microcell. For each region inside a macrocell or a microcell, the allotted frequency band is different from the adjacent region. The path loss model for the macro-femtocell and micro-femtocell cluster-based network is developed in this study. The signal-to-interference-plus-noise ratio and spectral efficiency are determined. The simulation results show that using the proposed strategy signal-to-interference-plus-noise ratio can be increased to ∼62–92%. Simulation results present that macro-femtocell and micro-femtocell cluster-based network reduces the power transmission to ∼48% than that of a macro-femtocell network. Hence it is referred as a green mobile network.

Interference Avoidance through Pilot-Based Spectrum Sensing Algorithm in Overlaid Femtocell Networks

Co-channel interference between macro–femtocell networks is an unresolved problem, due to the frequency reuse phenomenon. To mitigate such interference, a secondary femtocell must acquire channel-state knowledge about a co-channel macrocell user and accordingly condition the maximum transmit power of femtocell user. This paper proposes a pilot-based spectrum sensing (PSS) algorithm for overlaid femtocell networks to sense the presence of a macrocell user over a channel of interest. The PSS algorithm senses the pilot tones in the received signal through the power level and the correlation metric comparisons between the received signal and the local reference pilots. On ensuring the existence of a co-channel macrocell user, the maximum transmit power of the corresponding femtocell user is optimized so as to avoid interference. Time and frequency offsets are carefully handled in our proposal. Simulation results show that the PSS algorithm outperforms existing sensing techniques, even at poor received signal quality. It requires less sensing time and provides better detection probability over existing techniques.

A Femtocell Cross-Tier Interference Mitigation Technique in OFDMA-LTE System: A Cuckoo Search based Approach

Background/Objectives: In wireless broadband access networks, most indoor environments encounter serious coverage problem. Femtocells have been introduced as an efficient solution to improve cell coverage, enhance area spectral-efficiency and provide better Quality-of-Service (QoS) to mobile users. However, cross-tier interference issues continue to be the major technical challenge associated with femtocell deployment. Methods/Statistical analysis: This study introduce a resource allocation technique-based cuckoo search algorithm RACSA for cross-tier interference mitigation in Orthogonal Frequency Division Multiple Access based Long Term Evolution (OFDMA-LTE) system. The innovative RACSA technique takes upon itself the task of maximizing the throughput of network according to a specified threshold for the interference. Cuckoo search Algorithm is extensively employed to successfully address the problem of resource optimization by finding and allocating the suitable power and bandwidth for all the users and this ultimately, leads to mitigating the cross-tier interference for OFDMA macro-femtocell networks. Results/Conclusions: The simulation results reveal that RACSA mitigate the cross-tier interference and improve the system performance. In addition, an assessment is carried out and it confirmed that RACSA gives (38%) and (21%) higher system throughput and (14%) and (35%) higher in spectral efficiency and (55%) and (33%) lower in the outage probability when comparing with results of genetic algorithm and auction algorithm respectively.

Resource Allocation Based on the Type of Handovers in Overlaid Macro-Femto Networks

In this paper we propose the resource allocation scheme for the overlaid macro-femtocell networks, which considers the type of handovers such as the inter-macrocell, macrocell-to-femtocell, femtocell-to-macrocell, or inter-femtocell in order to guarantee Quality of Service (QoS) and expand the accommodation capacity. Our proposed scheme takes into account the movement of mobile terminals, the QoS degradation, or the load control which trigger handovers in the overlaid networks, before it allocates resources dynamically. Moreover it considers QoS requirements of realtime or non-realtime mobile multimedia services such as video communication, Video on Demand (VoD) and dataa services. Simulation results show that our scheme provides better performances than the conventional one with respect to the outage probability, data transmission throughput and handover failure rate.

Co-channel Interference Avoidance Algorithm for Closed Access Femtocell Networks

ABSTRACTFemtocells play a promising role in next generation cellular networks as they provide high quality voice and data service to indoor users. However, deployment of dense, unplanned, and closed access femtocells over limited resource may induce co-tier co-channel interference (CTCCI) and cross-tier co-channel interference (XTCCI). These interferences seriously affect the quality of service (QoS) experienced by macro- and femtousers. To collectively handle such interferences, we propose an interference-free resource and power allocation (IFRPA) algorithm for macro-femtocell networks. Based on the channel quality indicator and out-of-boundary radiation, the IFRPA algorithm assigns downlink power and non-interfering downlink resource to the femtocells, thereby protecting the link quality of nearby co-channel deployed macro- and femtousers. Adopting frequency reuse phenomenon with centralized resource coordination has maximized resource utilization factor and overall service success ratio. Simulation results show that the IFRPA algorithm proposed by us makes the user to experience less XTCCI and CTCCI, which are below the tolerable limits, whereas the existing distributed random access (DRA) algorithm drowns the link quality of the users. Apart from interference reduction, QoS class identifier based service grant enables the IFRPA algorithm to offer significantly higher throughput and capacity compared with the DRA algorithm.

Realistic framework for resource allocation in macro–femtocell networks based on genetic algorithm

In this paper, we consider the problem of resource allocation in non-dense macrocell---femtocell networks. We build a comprehensive realistic framework that overcomes the limitations of previous research work such as (1) resources underutilization due to the equal transmitted power per subcarrier in macrocell, (2) lack of femtocells selection mechanism that grant access to public users without depriving their own subscribers. Orthogonal Frequency Division Multiple Access is a promising candidate for efficient spectrum sharing techniques as it eliminates intracell interference. We propose a base station selection and resource allocation model for two-tier networks that is able to: (i) maximize the overall network throughput, (ii) find the appropriate serving base station for each mobile user, and (iii) jointly assign bandwidth and power to each user. The proposed approach is based on Genetic Algorithm (GA) technique since this technique allows to find a near optimal solution and to speed up the optimization process. Simulations are conducted under realistic scenarios where user mobility and resource reservation are taken into account. The performance of the proposed approach is compared with a Mixed Integer Linear Programming (MILP) approach and the Weigthed Water Filling (WWF) algorithm.

Resource Allocation with QoS Supporting in Macro-Femtocell Networks

Resource Allocation with QoS Supporting in Macro-Femtocell Networks

Game theoretic approach for joint resource allocation in spectrum sharing femtocell networks

In this paper, we study the joint price and power allocation in spectrum sharing macro-femtocell networks. The proposed game theoretic framework is based on bi-level Stackelberg game where macro base station (MBS) works as a leader and underlaid femto base stations (FBSs) work as followers. MBS has fixed data rate and imposes interference price on FBSs for maintaining its data rate and earns revenue while FBSs jointly adjust their power for maximizing their data rates and utility functions. Since the interference from FBSs to macro user equipment is kept under a given threshold and FBSs compete against each other for power allocation, there is a need to determine a power allocation strategy which converges to Stackelberg equilibrium. We consider two cases for MBS power allocation, i.e., fixed and dynamic power. MBS can adjust its power in case of dynamic power allocation according to its minimum data rate requirement and number of FBSs willing to share the spectrum. For both cases we consider uniform and nonuniform pricing where MBS charges same price to all FBSs for uniform pricing and different price to each FBS for non-uniform pricing according to its induced interference. We obtain unique closed form solution for each case if the co-interference at FBSs is assumed fixed. And an iterative algorithm which converges rapidly is also proposed to take into account the effect of co-tier interference on interference price and power allocation strategy. The results are explained with numerical simulation examples which validate the effectiveness of our proposed solutions.

Adaptive handover algorithm in heterogeneous femtocellular networks based on received signal strength and signal‐to‐interference‐plus‐noise ratio prediction

In this study, an efficient handover algorithm based on the received signal strength (RSS) prediction is presented for two-tier macro–femtocell networks in which, because of the fading effects of channel and short coverage range of femtocells, ping-pong handovers may take place. In the proposed approach, first each mobile station (MS) uses the recursive least square algorithm for predicting the RSS from the candidate base stations (BSs) including both femtocell and macrocell BSs. Then, according to the predicted RSS values, several future values of signal-to-interference plus noise ratio (SINR) are calculated. Afterwards, the candidate list of BSs is pruned according to the estimated future SINR values and the predicted RSS of each BS. Finally, the target BS which yields the highest throughput, is opted for handover. Through extensive simulations, the effects of speed of MSs and the density of femtocell BSs on the outage probability (OP), throughput and the ping-pong rate of MSs are studied. The results show that the proposed handover algorithm outperforms the previous ones and improves the throughput of MS while it reduces the OP and the number of ping-pong handovers.

Adaptive mode configuration in two‐tier macro‐femtocell networks

Femtocells equipped with cognitive capabilities are able to efficiently utilise the empty spaces of macrocell spectrum. This makes it possible to enhance the spectral efficiency of the available spectrum for end users. In a two-tier macro-femtocell network, `extra' macrocell resources need to be carefully and efficiently utilised by the femto-tier network, without having any negative impact on the performance of an existing macrocell network. Hence to attain self-configurable features in femtocell networks, this research proposes adaptive approaches through which femtocells can manage their coverage by controlling the transmission power to improve the network performance while alleviating the cross-tier interference. Since the interference problem is a very serious issue in the deployment of closed access mode compared with open access mode, the authors propose a mode selection mechanism that enables femtocells to automatically configure their modes to resolve interference issues while meeting the minimum quality-of-service requirements for their registered users. Simulation results confirm that the proposed mechanisms improve the overall capacity and spectral efficiency while reducing the outage probability.

Base station selection and resource allocation in macro–femtocell networks under noisy scenario

In this paper, we address the problem of optimizing resources for downlink transmission in a macro–femtocell network under non-dense femtocell deployment. In the literature, some approaches perform bandwidth or power optimization depending on the air interface technology and others optimize both types of resources, but only in femtocell network. However, the following limitations can be noticed: (1) Equal distribution of transmitted power among all subcarriers, even if they are not used, leads to resource underutilization, (2) femtocell data rates are reduced in order to minimize the interference from femto base stations to macro users, and (3) the impact of noise has not been evaluated. Moreover, there is lack of optimal selection of users that can be served by femtocells. To overcome these limitations, we propose a model that finds a tradeoff between bandwidth and power to reduce the bandwidth usage per user and to minimize the impact of noise. By means of Linear Programming, our solution maximizes user satisfaction and provides optimal: serving base station, power and bandwidth for each mobile user taking into account its location and demand. Furthermore, we present a performance analysis under changes of signal to noise ratio. Simulations were conducted and a comparison with a modified version of Weighted Water Filling algorithm is presented.