On the traffic offloading in Wi-Fi supported heterogeneous wireless networks

Heterogeneous small cell networks (HetSNet) comprise several low power, low cost (SBSa), (D2D) enabled links wireless-fidelity (Wi-Fi) access points (APs) to support the existing macrocell infrastructure, decrease over the air signaling and energy consumption, and increase network capacity, data rate and coverage. This paper presents an active user dependent path loss (PL) based traffic offloading (TO) strategy for HetSNets and a comparative study on two techniques to offload the traffic from macrocell to (SBSs) for indoor environments: PL and signal-to-interference ratio (SIR) based strategies. To quantify the improvements, the PL based strategy against the SIR based strategy is compared while considering various macrocell and (SBS) coverage areas and traffic–types. On the other hand, offloading in a dense urban setting may result in overcrowding the (SBSs). Therefore, hybrid traffic–type driven offloading technologies such as (WiFi) and (D2D) were proposed to en route the delay tolerant applications through (WiFi) (APs) and (D2D) links. It is necessary to illustrate the impact of daily user traffic profile, (SBSs) access schemes and traffic–type while deciding how much of the traffic should be offloaded to (SBSs). In this context, (AUPF) is introduced to account for the population of active small cells which depends on the variable traffic load due to the active users

Coexistence of Wi-Fi and Heterogeneous Small Cell Networks Sharing Unlicensed Spectrum

As two major players in terrestrial wireless communications, Wi-Fi systems and cellular networks have different origins and have largely evolved separately. Motivated by the exponentially increasing wireless data demand, cellular networks are evolving towards a heterogeneous and small cell network architecture, wherein small cells are expected to provide very high capacity. However, due to the limited licensed spectrum for cellular networks, any effort to achieve capacity growth through network densification will face the challenge of severe inter-cell interference. In view of this, recent standardization developments have started to consider the opportunities for cellular networks to use the unlicensed spectrum bands, including the 2.4 GHz and 5 GHz bands that are currently used by Wi-Fi, Zigbee and some other communication systems. In this article, we look into the coexistence of Wi-Fi and 4G cellular networks sharing the unlicensed spectrum. We introduce a network architecture where small cells use the same unlicensed spectrum that Wi-Fi systems operate in without affecting the performance of Wi-Fi systems. We present an almost blank subframe (ABS) scheme without priority to mitigate the co-channel interference from small cells to Wi-Fi systems, and propose an interference avoidance scheme based on small cells estimating the density of nearby Wi-Fi access points to facilitate their coexistence while sharing the same unlicensed spectrum. Simulation results show that the proposed network architecture and interference avoidance schemes can significantly increase the capacity of 4G heterogeneous cellular networks while maintaining the service quality of Wi-Fi systems

Heterogeneous Wireless Networks: Traffic Offloading, Resource Allocation and Coverage Analysis

Unlike 2G systems where the radius of macro base station (MBS) could reach several kilometers, the cell radius of LTE-Advanced and next generation wireless networks (NGWNs) such as 5G networks would be random and up to a few hundred meters in order to overcome the radio signal propagation impairments. Heterogeneous wireless networks (HetNets) are becoming an integral part of the NGWNs especially 5G networks, where small cell base stations (SBSs), wireless-fidelity (WiFi) access points (APs), cellular BSs and device-to-device (D2D) enabled links coexist together. HetNets represent novel approaches for the mobile data offloading, resource allocation and coverage probability problems that help to optimize the network traffic. However, heterogeneity and interworking among different radio access technologies bring new challenges such as bandwidth resource allocation, user/cell association, traffic offloading based on the user activity and coverage probability in HetNets. This dissertation attempts to address three key research areas: traffic offloading, bandwidth resource allocation and coverage probability problems in HetNets. In the first part of this dissertation, we derive the mathematical framework to calculate the required active user population factor (AUPF) of small cells based on the probabilistic traffic models. The number of total mobile users and number of active mobile users have different probabilistic distributions such as different combinations of Binomial and Poisson distributions. Furthermore, AUPF is utilized to investigate the downlink BS and backhaul power consumption of HetNets. In the second part, we investigate two different traffic offloading (TO) schemes (a) Path loss (PL) and (b) Signal-to-Interference ratio (SIR) based strategies. In this context, a comparative study on two techniques to offload the traffic from macrocell to small cell is studied. Additionally, the AUPF, small cell access scheme and traffic type are included into a PL based TO strategy to minimize the congested macrocell traffic. In the third part, the joint user assignment and bandwidth resource allocation problem is formulated as a mixed integer non-linear programming (MINLP). Due to its intractability and computational complexity, the MINLP problem is transformed into a convex optimization problem via a binary variable relaxation approach. Based on the mathematical analysis of the problem, a heuristic algorithm for joint user assignment and bandwidth allocation is presented. The proposed solution achieves a near optimal user assignment and bandwidth allocation at reduced computational complexity. Lastly, we investigate the transition between traditional hexagonal BS deployment to random BS placement in HetNets. Independent Poisson Point Processes (PPPs) are used to model the random locations of BSs. Lloyds algorithm is investigated for analyzing the coverage probability in a network which functions as a bridge between random and structural BS deployments. The link distance distribution is obtained by using the Expectation-Maximization (EM) algorithm which is further utilized for calculating the coverage probability

Resource Optimization in Multi-Tier HetNets Exploiting Multi-Slope Path Loss Model

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users' quality-of-service requirements, namely users' minimum rate, and the base stations' (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network's performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model

Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks

Optimal learning paradigm and clustering for effective radio resource management in 5G HetNets

Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the $\epsilon -$ greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning

Energy Efficiency of Ultra-Dense Small Cell Radio Access Networks for 5G and Beyond

Small cell base station (BS) densification in the radio access network (RAN) is an effective solution to improve the RAN capacity. However, small cell BS densification by adding more non-zero energy-consuming BSs increases energy consumption, compromising energy efficiency, which can be mitigated by adopting sleep mode. A comprehensive evaluation framework is applied in this research to analyse the capacity, energy consumption, and energy efficiency performance of the ultra-dense small cell RANs as a complete energy efficiency assessment, which is lacking in the literature. The impact of advanced techniques millimetre wave (mmWave), antenna array beamforming, and integrated access and backhaul (IAB) on RAN energy efficiency are also investigated. MATLAB- based simulation results show that the ultra-dense small cell RANs, where the number of BSs greatly exceeds the number of active user equipment (UEs), can only be energy efficient if all the empty cells without UE association are turned off completely. Energy efficiency enhancement comes from capacity improvement and energy consumption constraint. Specifically, the ultra-dense small cell RANs can achieve maximum performance improvement of 7.56-fold and 2.35-fold regarding capacity, 3780.11-fold and 32.38-fold regarding energy consumption using the current power model, and 28591.53-fold and 75.97-fold regarding energy efficiency in homogeneous and heterogeneous infrastructures, respectively, comparing the cases with and without the sleep mode. In addition, mmWave and IAB trade energy consumption and energy efficiency for capacity improvement and backhaul cost reduction. With mmWave and IAB, dense small cell RAN can achieve a maximum of 2.55-fold and 1.70-fold for capacity improvement, 2.46-fold and 2.89-fold for energy consumption reduction using the current power model, and 6.27-fold and 8.34-fold energy efficiency enhancement for UE densities of 900 and 300 UEs/km2, respectively, comparing the cases with and without the sleep mode