Utility-Based Distributed Geographic Load Balancing in Mobile Cellular Networks (original) (raw)
Related papers
Intelligent cellular network load balancing using a cooperative negotiation approach
2003
In this paper, we investigate a novel distributed load balance scheme for cellular networks, which intelligently changes cellular coverage according to the geographic traffic distribution in real time. The performance of the whole cellular network can be improved by contracting the base station antenna pattern around a traffic "hot spot" and expanding adjacent cells coverage to fill in the loss. A cooperative negotiation approach with semi-smart antennas for cellular network coverage control is described in this paper. By the use of negotiations between adjacent base stations, optimum local coverage agreements can be achieved in the context of the whole cellular network. Results showing the advantage of this technique are also presented.
Towards intelligent geographic load balancing for mobile cellular networks
We investigate a novel geographic load-balancing scheme for cellular networks that intelligently changes cellular coverage according to the geographic traffic distribution in real time. A cooperative negotiation approach for the real-time control of cellular network coverage is described. The performance of the whole cellular network is improved by contracting and shaping the antenna radiation pattern around a traffic "hot spot" and expanding adjacent cells coverage to fill in the coverage loss. By the use of real time cooperative negotiations between base stations and associated antennas, a near optimal local coverage agreement is reached in the context of the whole cellular network. Results showing the advantage of this technique are presented. Global optimization using constrained real-coded genetic algorithms (RCGA) provides a benchmark. Convergence using penalty functions to manage the constraints was first investigated but gave poor results. A transformation of the problem space is used to remove the constraints, and a criterion that is necessary for successful transformations is explained.
Providing enhanced QoS differentiation to customers using geographic load balancing
2006 European Conference on Wireless Technologies, 2006
This paper extends recent developments in geographic load balancing techniques using semi-smart antennas for cellular mobile communication systems by investigating the potential to provide enhanced QoS to realistic 3G services traffic classes and also to provide user prioritization even in situations where non uniform demand occurs over the network. Traditional cellular CAC systems have limited capability to rectify what turns out to be poor decisions apart from simply dropping connections. With cooperative geographic load balancing, if a base station cannot provide the desired service, adjacent base stations adjust their coverage to carry some of the traffic so that further calls, and in particular high priority calls can be accepted without having to drop existing connections; thus mitigating poor decisions. Enhancement of system capacity has been demonstrated in previous work to establish the optimal wireless radiation coverage shapes over a cellular network in real time and for both uplink and downlink in WCDMA and other wireless networks. The results presented show that load balancing approach can provides capacity gains and also provide good QoS discrimination in the uplink and downlink. The algorithm described can be adapted to different wireless technologies and to different kinds of adaptive antenna; from inexpensive semi-smart systems to fully adaptive systems.
2003
A cooperative negotiation approach for the real-time control of cellular network coverage is described. The performance of the whole cellular network is improved by contracting and shaping the antenna radiation pattern around traffic "hot spots" and expanding adjacent cell coverage to fill in the coverage loss. The paper shows that the local area real time cooperative negotiation between base stations leads to a near global optimal coverage agreement which is reached in the context of the whole cellular network. Results showing the advantage of this technique are presented. Global optimization using constrained real-coded genetic algorithms provides the benchmark. In our work, instead of using a formal negotiation model of alternating offers, we create certain number of possible local hypotheses and start negotiations based on them. Some negotiation may reach agreements before their deadlines, and the system commits to the best agreement found at the end. This approach is more predictable and controllable than the formal negotiation model. Architecturally the negotiation component described is part of the planning layer of an agent system for resource management in 3G wireless networks. This has a layered architecture with both planning and reactive components. The results are allowing the development of a novel geographic load-balancing scheme for cellular networks that intelligently changes cellular coverage according to the geographic traffic distribution in real time.
Location aided energy balancing strategy in green cellular networks
2014 23rd International Conference on Computer Communication and Networks (ICCCN), 2014
Most cellular network communication strategies are focused on data traffic scenarios rather than energy balance and efficient utilization. Thus mobile users(cell phones) in hot cells may suffer from low throughput due to energy loading imbalance problem. In state-of-art cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. In this paper, we propose an energy balancing strategy in which the mobile nodes are able to dynamically select and hand over to the relay station with the highest potential energy capacity to resume communication. Key to the strategy is that each relay station merely maintains two parameters that contains the trend of its previous energy consumption and then predicts its future quantity of energy, which is defined as the relay station's potential energy capacity. Then each mobile node can select the relay station with the highest potential energy capacity. Simulations demonstrate that our approach significantly increase the aggregate throughput and the average life time of relay stations in cellular network environment.
Smart Antennas in a Mobile Cellular Network
Wireless Mobile Communication systems will be more sophisticated and wide spread in future. This growth demands not only for capacity increase, but also high quality of service and better coverage without increase in the radio frequency spectrum allocated for mobile applications. Smart antenna is one of the promising technologies for achieving efficient networks that maximize capacity and improve quality and coverage. These Smart antennas dynamically adapt to changing traffic requirements. The core of smart antenna is the selection of smart algorithms in adaptive array. Using beam forming algorithms, the weight of antenna arrays can be adjusted to form certain amount of adaptive beam to track corresponding users automatically and at the same time to minimize interference arising from other users by introducing nulls in their directions Smart antennas are usually employed at the base stations and they radiate narrow beams to serve different users. This work looked at the Smart Antenna technology and its principles of operation, and the associated benefits obtainable from its implementation in a mobile cellular network.
One major topic of research into SON technology is the coordination of SON use cases. Network operators expect a coordinated handling of the parameter and configuration changes submitted to the operating network by closed-loop SON use case implementations. Beside a published conceptual framework, SON coordination has already been treated in the literature, especially regarding the mobility load balancing (MLB) and mobility robustness optimization (MRO) use cases. In this paper, we utilize the capacity and coverage optimization (CCO) and MLB use cases. Rather than performing any heading or tailing coordination of separate CCO and MLB algorithms, in our work we concentrate on the optimization considering both use cases in a joint algorithm. Our approach introduces cell-individual loads and the joint treatment of cell selection policies and antenna tilt settings into well-known and previously reported optimization concepts. Using system simulations of a sample LTE real deployment scenario, we verify that our joint optimization of antenna tilts and cell selection rules including the notion of cell-individual loads outperforms the optimization of tilts-only (with and without the notion of cell-individual loads within the algorithm) and of the cell selection rules-only in terms of spectral and energy efficiency.
An adaptive load balance allocation strategy for small antenna based wireless networks
WSEAS TRANSACTIONS on COMMUNICATIONS archive, 2009
Technological advances and rapid development in handheld wireless terminals have facilitated the rapid growth of wireless communications. Since this tremendous growth of wireless communication requirements is expected under the constraint of limited bandwidth. The small antenna frameworks that can provide more flexible to handle the limited bandwidth will be the mainstream for wireless networks. The antenna divided a cell into several sections. Each section contains a part of the system codes used to provide wireless communications. Therefore, the system codes allocated to each section will effect the system capacity and a reasonable allocation should provide more codes to a section with heavy traffic than a section with light traffic. However, the large number of sections increases the difficult to allocate system codes to sections. Especially, when there are variations in the traffic loads among sections will lessen the traffic-carrying capacity. This study proposes an adaptive load balance allocation strategy for small antenna based wireless networks. This strategy is implemented to solve traffic-adaptation problem that can enhance the traffic-carrying capacity for variations in traffic. Furthermore, the simulation results are presented to confirm the efficiency of the proposed strategy.