In this article, we present a new framework called Survivor Functions with Probabilistic Constraints (SFPC) to address the challenges of energy-efficient resource allocation in heterogeneous wireless networks. SFPC combines statistical knowledge of context parameters involving wireless channels, computational demands, task offloading sizes, and MEH’s CPU availability to create a unified approach for managing resources in these networks.
Context
To understand the importance of SFPC, let’s consider a scenario where multiple devices are connected to different types of wireless channels, such as Wi-Fi or LTE. Each channel has its own unique properties and demands, making it difficult to manage resources efficiently across all channels. Additionally, there are constraints on the CPU availability of the device, which further complicates the resource allocation process.
Figure 3: Survivor Function with Probabilistic Constraints
The SFPC framework addresses these challenges by using a survivor function to model the probability of each channel being used successfully. The survivor function takes into account the statistical knowledge of context parameters, such as wireless channels, computational demands, task offloading sizes, and MEH’s CPU availability. This allows SFPC to create a unified approach for managing resources in heterogeneous wireless networks.
Feasibility Constraints
The SFPC framework includes feasibility constraints that ensure the allocation of resources is feasible and satisfies the constraints. These constraints include:
- The parameter Sn,t is chosen between 0 and 1, for each element of the RIS;
- The resonance frequency fn,t is chosen from a discrete set Ω of candidate values;
- The quality factor χn,t is chosen from a discrete set X of candidate values;
- The amplitude response of each RIS’s element cannot exceed 1 across all subcarriers;
- The transmit power allocated on each bin is non-negative;
- The total transmit power is chosen between a minimum value pmin and a maximum value pmax;
- The local CPU frequency is chosen between a minimum value f l
max.
Instantaneous Feasibility Constraints
The instantaneous feasibility constraints are essential for ensuring that the allocation of resources is feasible in the short term. These constraints ensure that the system can operate effectively and efficiently, even when there are uncertainties in the environment.
Complexity of Problem (15)
The problem of allocating resources in heterogeneous wireless networks is complex due to the absence of statistical knowledge of context parameters involving wireless channels, computational demands, task offloading sizes, and MEH’s CPU availability. Additionally, the discrete nature of several involved variables makes it hardly tractable over a long-term horizon.
Work of F. Costanzo et al.
The work of F. Costanzo, K. D. Katsanos, G. C. Alexandropoulos, and P. Di Lorenzo aimed to develop a new framework for energy-efficient resource allocation in heterogeneous wireless networks. Their approach, SFPC, combines statistical knowledge of context parameters to create a unified approach for managing resources in these networks.
Conclusion
In conclusion, the SFPC framework provides a novel approach for energy-efficient resource allocation in heterogeneous wireless networks. By combining statistical knowledge of context parameters, SFPC creates a unified framework that can handle complex resource allocation problems with probabilistic constraints. This approach has significant potential to improve the performance and efficiency of wireless networks, enabling them to provide better services to users.
