A Statistical Model of Uplink Inter-Cell Interference with Slow and Fast Power Control Mechanisms

Abstract

Uplink power control is in essence an interference mitigation technique that aims at minimizing the inter-cell interference (ICI) in cellular networks by reducing the transmit power levels of the mobile users while maintaining their target received signal quality levels at base stations. Power control mechanisms directly impact the interference dynamics and, thus, affect the overall achievable capacity and consumed power in cellular networks. Due to the stochastic nature of wireless channels and mobile users' locations, it is important to derive theoretical models for ICI that can capture the impact of design alternatives related to power control mechanisms. To this end, we derive and verify a novel statistical model for uplink ICI in Generalized-K composite fading environments as a function of various slow and fast power control mechanisms. The derived expressions are then utilized to quantify numerically key network performance metrics that include average resource fairness, average reduction in power consumption, and ergodic capacity. The accuracy of the derived expressions is validated via MonteCarlo simulations. Results are generated for multiple network scenarios, and insights are extracted to assess various power control mechanisms as a function of system parameters.

On Stochastic Geometry Modeling of Cellular Uplink Transmission With Truncated Channel Inversion Power Control

“…It is worth mentioning that the truncated channel inversion power control mechanism is a realistic power control scheme for code-division multiple access (CDMA) networks to eliminate the near-far effect. Moreover, for orthogonal frequencydivision multiple access (OFDMA) networks, it has been shown in [20] that if the edge users (i.e., users who do not have sufficient power for their channel inversions) are allowed to transmit with their maximum power, the interference in the system increases significantly. Consequently, the network performance deteriorates without much improvement in the cell edge user performance.…”

On the Deployment of Energy Sources in Wireless-Powered Cellular Networks

“…Thus, for analytical tractability and to avoid the complexity of M correlated random variables, we discretize the cellular region into Y circular zones of equal width and W equal angular intervals (other discretization approaches can also be used here as mentioned in [23]). Conditioning on the location of a user at the circular ring of radius r y and angle ✓ w , its distance from a given PB i can be calculated by using the cosine law as q r 2 y + ⇢ 2 2r y ⇢cos(✓ w ✓ i ) in a deterministic way.…”

Uplink power control modeling for dense OFDMA-based heterogeneous networks

“…More recently, models for OFDMA-based systems and heterogeneous networks have been also proposed. For example, the impact of Inter-Cell interference (ICI) is considered in [14] to obtain network performance metrics. On the other hand, the authors of [15] exploit stochastic geometry to define a mathematical framework for system-level analysis, which might help in the design of uplink heterogeneous networks.…”