Energy saving scheme for multicarrier HSPA + under realistic traffic fluctuation

The final publication is available at Springer via http://dx.doi.org/10.1007/s11036-015-0656-6In the near future, an increase in cellular network density is expected to be one of the main enablers to boost the system capacity. This development will lead to an increase in the network energy consumption. In this context, we propose an energy efficient dynamic scheme for HSDPA + (High Speed Downlink Packet Access-Advanced) systems aggregating several carriers and which adapts dynamically to the network traffic. The scheme evaluates whether node-B deactivation is feasible without compromising the user flow throughput. Furthermore, instead of progressive de-activation of carriers and/or node-B switch-off, we evaluate the approach where feasible combination of inter-site distance and number of carriers is searched to obtain best savings. This is done by also considering the effect of transition delays between network configuration changes. The solution exploits the fact that re-activation of carriers might permit turning off other BSs earlier at relatively higher load than existing policies. Remote electrical downtilt is also considered as a means to maximize the utilization of higher modulation and coding schemes in the extended cells. This approach promises significant energy savings when compared with existing policies - not only for low traffic hours but also for medium load scenarios.Peer ReviewedPostprint (author's final draft

Green resource allocation for OFDMA wireless cellular networks

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Context-aware multi-attribute decision multi - attribute decision making for radio access technology selection in ultra dense network

Ultra Dense Network (UDN) is the extreme densification of heterogeneous Radio Access Technology (RAT) that is deployed closely in coordinated or uncoordinated manner. The densification of RAT forms an overlapping zone of signal coverage leading to the frequent service handovers among the RAT, thus degrading overall system performance. The current RAT selection approach is biased towards network-centric criteria pertaining to signal strength. However, the paradigm shift from network-centric to user-centric approach necessitates a multi-criteria selection process, with methodology relating to both network and user preferences in the context of future generation networks. Hence, an effective selection approach is required to avoid unnecessary handovers in RAT. The main aim of this study is to propose the Context-aware Multiattribute decision making for RAT (CMRAT) selection for investigating the need to choose a new RAT and further determine the best amongst the available methods. The CMRAT consists of two mechanisms, namely the Context-aware Analytical Hierarchy Process (CAHP) and Context-aware Technique for Order Preference by Similarity to an Ideal Solution (CTOPSIS). The CAHP mechanism measures the need to switch from the current RAT, while CTOPSIS aids in decision making to choose the best target RAT. A series of experimental studies were conducted to validate the effectiveness of CMRAT for achieving improved system performance. The investigation utilises shopping mall and urban dense network scenarios to evaluate the performance of RAT selection through simulation. The findings demonstrated that the CMRAT approach reduces delay and the number of handovers leading to an improvement of throughput and packet delivery ratio when compared to that of the commonly used A2A4-RSRQ approach. The CMRAT approach is effective in the RAT selection within UDN environment, thus supporting heterogeneous RAT deployment in future 5G networks. With context-aware selection, the user-centric feature is also emphasized

A simplified optimization for resource management in cognitive radio network-based internet-of-things over 5G networks

With increasing evolution of applications and services in internet-of-things (IoT), there is an increasing concern of offering superior quality of service to its ever-increasing user base. This demand can be fulfilled by harnessing the potential of cognitive radio network (CRN) where better accessibility of services and resources can be achieved. However, existing review of literature shows that there are still open-end issues in this regard and hence, the proposed system offers a solution to address this problem. This paper presents a model which is capable of performing an optimization of resources when CRN is integrated in IoT using five generation (5G) network. The implementation uses analytical modeling to frame up the process of topology construction for IoT and optimizing the resources by introducing a simplified data transmission mechanism in IoT environment. The study outcome shows proposed system to excel better performance with respect to throughput and response time in comparison to existing schemes

Protocol for Extreme Low Latency M2M Communication Networks

As technology evolves, more Machine to Machine (M2M) deployments and mission critical services are expected to grow massively, generating new and diverse forms of data traffic, posing unprecedented challenges in requirements such as delay, reliability, energy consumption and scalability. This new paradigm vindicates a new set of stringent requirements that the current mobile networks do not support. A new generation of mobile networks is needed to attend to this innovative services and requirements - the The fifth generation of mobile networks (5G) networks. Specifically, achieving ultra-reliable low latency communication for machine to machine networks represents a major challenge, that requires a new approach to the design of the Physical (PHY) and Medium Access Control (MAC) layer to provide these novel services and handle the new heterogeneous environment in 5G. The current LTE Advanced (LTE-A) radio access network orthogonality and synchronization requirements are obstacles for this new 5G architecture, since devices in M2M generate bursty and sporadic traffic, and therefore should not be obliged to follow the synchronization of the LTE-A PHY layer. A non-orthogonal access scheme is required, that enables asynchronous access and that does not degrade the spectrum. This dissertation addresses the requirements of URLLC M2M traffic at the MAC layer. It proposes an extension of the M2M H-NDMA protocol for a multi base station scenario and a power control scheme to adapt the protocol to the requirements of URLLC. The system and power control schemes performance and the introduction of more base stations are analyzed in a system level simulator developed in MATLAB, which implements the MAC protocol and applies the power control algorithm. Results showed that with the increase in the number of base stations, delay can be significantly reduced and the protocol supports more devices without compromising delay or reliability bounds for Ultra-Reliable and Low Latency Communication (URLLC), while also increasing the throughput. The extension of the protocol will enable the study of different power control algorithms for more complex scenarios and access schemes that combine asynchronous and synchronous access

Resource management in QoS-aware wireless cellular networks

2011 Summer.Includes bibliographical references.Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost