High Power Macro Base Station Research Articles

A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

The expected massive connectivity in 5G wireless network is bound to become a challenge to service providers. Many services over the 5G network will be aligned to a particular radio access network (RAN). As a result admitting a service based user to a particular RAN will depend on the most efficient radio access technology selection (RAT). This is because 5G network will adopt multi-tier radio access networks ranging from high power macro base stations to extremely low power Bluetooth connectivity. Selection of a service oriented RAT is critical because some wireless services have superior quality of service under certain RATs. Maintaining efficient RAT selection by network operators will improve power allocation efficiency, bandwidth allocation efficiency and operation expenditure. The complexity of associating a RAT to service based user while considering network state such as service packet size, the turnaround time, the power allocation has not been fully explored. In this paper we propose a reinforcement learning approach to user admission based on efficient RAT selection considering wireless services in a cross tier wireless radio access network domain. The proposed algorithm is expected to improve RAT selection efficiency while minimizing the computation complexity. We perform extensive simulation using Python dynamic libraries and present our results alongside existing approaches

An Optimum User Association Algorithm in Heterogeneous 5G Networks Using Standard Deviation of the Load

Fifth-generation (5G) wireless networks and beyond will be heterogeneous in nature, with a mixture of macro and micro radio cells. In this scenario where high power macro base stations (MBS) coexist with low power micro base stations (mBS), it is challenging to ensure optimal usage of radio resources to serve users with a multitude of quality of service (QoS) requirements. Typical signal to interference and noise ratio (SINR)-based user allocation protocols unfairly assign more users to the high power MBS, starving mBS. There have been many attempts in the literature to forcefully assign users to mBS with limited success. In this paper, we take a different approach using second order statistics of user data, which is a better indicator of traffic fluctuations. We propose a new algorithm for user association to the appropriate base station (BS) by utilizing the standard deviation of the overall network load. This is done through an exhaustive search of the best user equipment (UE)–BS combinations that provide a global minimum to the standard deviation. This would correspond to the optimum number of UEs assigned to every BS, either macro or micro. We have also derived new expressions for coverage probability and network energy efficiency for analytical performance evaluation. Simulation results prove the validity of our proposed methods to balance the network load, improve data rate, average energy efficiency, and coverage probability with superior performance compared with other algorithms.

Analysis of decoupled association in HetNets using soft frequency reuse scheme

Meeting the huge data demands of cellular networks requires an integrated model in terms of interference mitigation technique and cell association scheme. A heterogeneous cellular network (HetNet), which comprises low power small base stations (sBSs) deployed in the coverage of high power macro base station (mBS), plays a vital role in achieving high network performance gains. Large transmit power discrepancy among various tiers of HetNet with maximum received power association scheme results in fewer number of users associated with sBSs causing inefficient utilization of resources. A fraction of users from mBS are offloaded to sBSs for performance improvements, which causes signal-to-interference-plus-noise ratio degradation due to strong mBS interference. Therefore, proactive user association along with interference management technique is essential to abate interference with efficient use of sBSs' resources. In this paper, we consider soft frequency reuse scheme together with decoupled association (DA) to mitigate interference and to achieve load balancing. Results indicate that the proposed unified model achieves enhanced coverage probability due to reduced interference and DA outperforms all other methods used in the state-of-the-art.

Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks

5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users’ priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users’ QoS.

A Review on 3GPP Femtocell Networks and its Technical Challenges

Background/Objective: The demand for the data traffic is exponentially increasing day to day because of the proliferation of smart phones, tablets and internet-connected devices. To meet this requirements network operators has to dramatically increase their network capacities. Methods: One of the possible ways to increase the system capacity is bringing the transmitter and receiver nearer to each other. It gives dual benefits higher quality and spectrum efficiency. Findings: The cost effective solution is installing the low power nodes instead of traditional high power macro base stations. Examples of these low power nodes are microcells, picocells, femtocells and distributed antenna systems. The network, which consists of different power nodes, architectures, and protocols is called Heterogeneous network. To overcome the indoor coverage and increased system capacity femtocells also called low power base stations are selected by the researchers as the suitable solution. In this paper we overview the femtocell deployment scenarios, access modes, and technical challenges briefly. Improvements: This paper gives the details about solutions available in the literature and gives future research directions to solve the technical challenges faced by femtocells.

Exploiting an Optimal User Association Strategy for Interference Management in HetNets

Heterogeneous Cellular Network consists of low power Pico Base Stations (PBS) overlaid in high power Macro Base Station's (MBS) coverage area. Edge User Equipments (UE) of PBS are victims of interference from MBS. Almost Blank Subframes (ABS) can provide interference free communication for these UEs. However, existing techniques perform UE-BS association before ABS determination, which is suboptimal with respect to UE-BS association.We formulate the joint optimization of UE-BS association and ABS computation as a combinatorial optimization problem, which is NP-Hard. We suggest the use of Hungarian matching algorithm and iterative exploration of candidate ABS densities to discover the ABS that can minimize system blocking.