Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks

Abstract

In this paper, we focus on the radio resource planning in the uplink of licensed Orthogonal Frequency Division Multiple Access (OFDMA) based Internet of Things (IoT) networks. The average behavior of the network is considered by assuming that active sensors and collectors are distributed according to independent random Poisson Point Process (PPP) marked by channel randomness. Our objective is to statistically determine the optimal total number of Radio Resources (RRs) required for a typical cell. On one hand, the allocated bandwidth should be sufficiently large to support the traffic of the devices and to guarantee a low access delay. On the other hand, the over-dimensioning is costly from an operator point of view and induces spectrum wastage. For this sake, we propose statistical tools derived from stochastic geometry to evaluate, adjust and adapt the allocated bandwidth according to the network parameters, namely the required Quality of Service (QoS) in terms of rate and access delay, the density of the active sensors, the collector intensities, the antenna configurations and the transmission modes. The optimal total number of RRs required for a typical cell is then calculated by jointly considering the constraints of low access delay, limited power per RR, target data rate and network outage probability. Different types of networks are considered including Single Input Single Output (SISO) systems, Single Input Multiple Output (SIMO) systems using antenna selection or Maximum Ratio Combiner (MRC), and Multiuser Multiple Input Multiple Output (MU-MIMO) systems using Zero-Forcing decoder.

Keywords: LPWAN; licensed OFDMA-based IoT; resource planning; stochastic geometry.

Figure 1

Network model: typical cell and active nodes.

Figure 2

MU-MIMO: $n_u$ users are simultaneously scheduled on the same radio-resource.

Figure 3

Illustration of the scheduling with $n_u=4$. The number of RR is adjusted with respect to the furthest node in each group, to say i, situated at distance $r_i$ corresponding to the radius of the ball containing $i{n}_u-1$ nodes.Distance based multiuser scheduling scheme

Figure 4

Receiver diversity with SIMO configuration.

Figure 5

Mean number of required Radio Resources (RRs) in a typical cell considering ${\lambda }_b$ ranges from $0.3$ to $0.9$ nodes/${\mathrm{km}}^2$ with ${\lambda }_a=5.5$ nodes/${\mathrm{km}}^2$, ${\tau }_{max}=1$ ms.

Figure 6

Total number of required RRs in a typical cell versus the collector intensity ${\lambda }_b$ with ${\lambda }_a=5.5$ nodes/${\mathrm{km}}^2$, ${\tau }_{max}=1$ ms.

Figure 7

The empirical Cumulative Distribution Function (CDF) of $N_t$ total number of RRs required for the typical cell in which the maximal delay ${\tau }_{max}=1$ ms, ${\lambda }_a=5.5$ nodes/${\mathrm{km}}^2$ and ${\lambda }_b=0.5$ nodes/${\mathrm{km}}^2$.

Figure 8

Low access delay $\tau$ with statistical dimensioning in which the maximal delay ${\tau }_{max}=1$ ms, ${\lambda }_a=5.5$ nodes/${\mathrm{km}}^2$ and ${\lambda }_b=0.5$ nodes/${\mathrm{km}}^2$.

Figure 9

Comparison between theoretical and empirical $N_t$ values in which ${\lambda }_a=5.5$ nodes/${\mathrm{km}}^2$ and ${\lambda }_b=0.5$ nodes/${\mathrm{km}}^2$.

Figure 10

Total number of required RRs in a typical cell with respect to maximal access delay ${\tau }_{max}$ with ${\lambda }_a=5.5$ nodes/${\mathrm{km}}^2$ and ${\lambda }_b=0.5$ nodes/${\mathrm{km}}^2$.