Plamen T. Semov

Self-Resource Allocation and Scheduling Challenges for Heterogeneous Networks Deployment

A reliable solution for meeting the high demand of throughput in areas called hotspots is the heterogeneous network. Heterogeneous networks are different depending on their coverage, their type of radio access technique and the way there are connected to the core network. This paper proposes a novel algorithm for semi-coordinated resource allocation and scheduling based on mobile positioning information, game theory and reinforcement learning technique. The capabilities of such an approach to support the practical deployment of heterogeneous networks is analyzed. Further, a reasoning strategy is proposed to justify the choice of Wi-Fi versus other small cell technologies from a practical deployment viewpoint.

Use of positioning information for performance enhancement of uncoordinated heterogeneous network deployment

This paper proposes a novel algorithm for dynamic physical resource allocation based on the use of positioning information during carrier aggregation (CA) in a semi-and uncoordinated deployment of heterogeneous networks (HetNet). The algorithm uses the known Q-learning method enhanced with information but the locations of the mobile users and neighboring cells to solve the problem of dynamic physical resource assignment in uncoordinated scenario while accounting for improved allocation and scheduling. The results are compared to the performance when known scheduling algorithms are employed and show increased cell throughput, while maintaining an adequate user throughput when employing Q-learning with positioning information.

Layered routing algorithm for sustainable MANET operation

In this paper a routing algorithm for MANETs is presented, based on the formation of servicing zones and determination of nodes in these zones as default gateways. The idea behind this approach is to gain power efficiency and sustainability of the network. The proposed approach reduces the overall number of broadcasts in the network and ensures a reliable and energy efficient connection, by balancing the load among the nodes. The algorithm could be considered as a hybrid type of routing mechanism, incorporating multipath hop-by-hop distributed routing. We apply a time slot based self-organizing and self-routing approach by the application of a network model with universal basic node properties. For each node we define two functional and one generic property. The algorithm has the logic of non-cooperative routing based on the evaluation of the defined basic properties of the nodes. The major advantages of the algorithm are related to the complexity and the reduced overall routing cost, especially in higher density networks.

Improved open loop power control for LTE uplink

In this paper an improved Open Loop Power Control (OLPC) approach for LTE is proposed. Using a heuristic classification procedure sets of User Equipments (UEs) with similar properties related to their distribution, location and the required throughput in the cell are formed. In addition a heuristic generic search algorithm is applied for the realization of a dynamic power allocation scheme giving an improvement of the overall cell throughput, based on the allocation of different path loss compensation factors to each UE set. The major advantages in applying this scenario is that OLPC is applied to sets of UEs with similar properties instead on single UEs and the proposed algorithms are based on heuristic classification and search methods requiring low computational complexity and giving fast outcomes. The results from the simulation experiments show that such an approach could be an effective solution for dynamic OLPC ensuring reduction of the inter-cell interference and average cell throughput closest to the maximum possible.

Autonomous learning model for achieving multi cell load balancing capabilities in HetNet

Heterogeneous networks (HetNets) have been proposed as a capacity and coverage enabler in LTE-Advanced and beyond communication networks. Their optimal operation requires a significant degree of self-organization. Autonomic Load Balancing (ALB) has been proposed as an important self-organizing (SON) function in the LTE radio access network (RAN). In this work, distributed ALB is achieved by implementing a programmable autonomous learning model. The optimization problem (load balancing) is split into many small optimization problems and tasks, which are solved by using machine learning algorithms. The load conditions of the E-UTRAN NodeB (eNBs) and the measurement reports from the mobile terminals are used for creating a decision map for the load balancing. The simulation results show that by using ALB, the system capacity can be improved significantly.

Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous Networks

The 3GPP’s self-organizing networks (SONs) standards are a huge step towards the autonomic networking concept. They are the response to the increasing complexity and size of the mobile networks. This paper proposes a novel scheme for SONs. This scheme is based on machine learning techniques and additionally adopting the concept of abstraction and modularity. The implementation of these concepts in a machine learning scheme allows the usage of independent vendor and technology algorithms and reusability of the proposed approach for different optimization tasks in a network. The scheme is tested for solving an energy saving optimization problem in a heterogeneous network. The results from simulation experiments show that such an approach could be an appropriate solution for developing a full self-managing future network.

Risk assessment based LTE HetNet uplink power and interference control

In Long Term Evolution Advanced (LTE-A) heterogeneous networks (HetNets) the choice of power control mechanisms is decisive for ensuring the required user throughput and for controlling the inter-cell interference (ICI) to be under a predefined limit. In HetNets different mobile stations (macro, pico, femto) have different transmission powers resulting in complicated interference scenarios raising the need for application of more efficient power and interference control. In this paper we propose an uplink power and interference control mechanism for HetNets based on a generalized risk management procedure. Based on this we propose a proactive algorithm for uplink power and interference control and illustrate it with a simulation in a simplified HetNet macro - pico cell scenario. Results from simulation experiments show that such an approach could be a solution for dynamic uplink power control in HetNets ensuring reduction of the ICI and increasing the average cell throughput.

Energy efficiency vs. throughput trade-off in an LTE-A scenario

The growing demand of throughput, coverage and capacity has motivated the deployment of small cells as a cheaper option to macro base stations. Such heterogeneous scenario has also introduced novel advanced resource allocation and load balancing mechanisms for increased system performance. This paper proposes an approach to optimizing network performance by introducing an estimation of the energy consumption associated with the allocation of physical resources to users, as well as their offload from one serving base station to another (macro or pico). An algorithm for minimizing the energy consumption while optimizing resource allocation to pico-cell users in a wireless heterogeneous scenario is proposed and evaluated. The adopted model uses the information about the distance between the cells, their load and the quality of service (QoS) requirements of the mobile users together with an estimate of the associated energy saving. The results show that the overall network expenses related to power savings may be reduced while maintaining a high performance level of the network.