An overview of vertical handover decision strategies in heterogeneous wireless networks

Ubiquitous connectivity on mobile devices will enable numerous new applications in healthcare and multimedia. We set out to check how close we are towards ubiquitous connectivity in our daily life. The findings from our recent field-collected data from an urban university population show that while network availability is decent, the energy cost of network interfaces poses a great challenge. Based on our findings, we propose to leverage the complementary strength of Wi-Fi and cellular networks by choosing wireless interfaces for data transfers based on network condition estimation. We show that an ideal selection policy can more than double the battery lifetime of a commercial mobile phone, and the improvement varies with data transfer patterns and Wi-Fi availability.We formulate the selection of wireless interfaces as a statistical decision problem. The key to attaining the potential battery improvement is to accurately estimate Wi-Fi network conditions without powering up its network interface. We explore the use of different context information, including time, history, cellular network conditions, and device motion, for this purpose. We consequently devise algorithms that can effectively learn from context information and estimate the probability distribution of Wi-Fi network conditions. Simulations based on field-collected traces show that our algorithms can improve the average battery lifetime of a commercial mobile phone for a three-channel electrocardiogram (ECG) reporting application by 39%, very close to the theoretical upper bound of 42%. Finally, our field validation of our most simple algorithm demonstrates a 35% improvement in battery lifetime.

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Context-for-wireless: context-sensitive energy-efficient wireless data transfer

Next-generation wireless systems (NGWS) integrate different wireless networks, each of which is optimized for some specific services and coverage area to provide ubiquitous communications to the mobile users. It is an important and challenging issue to support seamless handoff management in this integrated architecture. The existing handoff management protocols are not sufficient to guarantee handoff support that is transparent to the applications in NGWS. In this work, a cross-layer (layer 2 + 3) handoff management protocol, CHMP, is developed to support seamless intra and intersystem handoff management in NGWS. Cross-layer handoff management protocol uses mobile's speed and handoff signaling delay information to enhance the handoff performance of mobile IP that is proposed to support mobility management in wireless IP networks. First, the handoff performance of mobile IP is analyzed with respect to its sensitivity to the link layer (layer 2) and network layer (layer 3) parameters. Then, a cross-layer handoff management architecture is developed using the insights learnt from the analysis. Based on this architecture, the detailed design of CHMP is carried out. Finally, extensive simulation experiments are carried out to evaluate the performance of CHMP. The theoretical analysis and simulation results show that CHMP significantly enhances the performance of both intra and intersystem handoffs

A Cross-Layer (Layer 2 + 3) Handoff Management Protocol for Next-Generation Wireless Systems

Review: An overview of vertical handover techniques: Algorithms, protocols and tools

The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three specific research problems under this architecture. First, we introduce a network-slicing based architecture and explain why and where artificial intelligence (AI) should be incorporated into this architecture. Second, the motivation, research challenges, existing works, and potential future directions related to applying AI-based approaches in three research problems are described in detail, i.e., flexible radio access network slicing, automated radio access technology selection, and mobile edge caching and content delivery. In summary, this paper highlights the benefits and potentials of AI-based approaches in the research of NGWNs.

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AI-Assisted Network-Slicing Based Next-Generation Wireless Networks

This chapter contains sections titled: Product Definition for Broadband Wireless Systems Technology Drivers Evolving Wireless Broadband Market Segments Open Systems and Intelligence at the Edge Radio Network System Engineering References

Always Best Connected

Wireless Broadband: Conflict and Convergence

This chapter contains sections titled: Collision of 3G and WiMAX Standards 3GPP and Long-Term Evolution WiMAX and Mobile WiMAX 
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Wideband 3G to Broadband 4G: Collision and Convergence of Standards

This chapter contains sections titled: Where We Are How We Got Here: Reintegration of the Telecom Oligopoly and Cracks in the Walled Gardens Flexibility Comes to Wireless Spectrum The Wireless Technology Diaspora Cellular Carriers: Stuck on Stickiness Managed Network Services: The Outsourced Network Enhanced Broadband Voice Fixed Mobile Convergence Boundary Blurring References 
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Chetan Sharma

Papers

Always Best Connected

Wireless Broadband: Conflict and Convergence

Wideband 3G to Broadband 4G: Collision and Convergence of Standards

Where We are: Wireless Meets the Broadband Internet