For 5G wireless communications, the
The
On the other hand, due to the inherent behaviors of IoT applications, such as remote monitoring and reporting, IoT devices need to operate for a very long time [
In this paper, we study how to ensure the high transmission reliability to guarantee the strict QoS for devices based on the RU scheduling and repetition determination while minimizing their energy consumption in NB-IoT networks. We first model the problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient and ultra-reliable heuristic, which consists of two phases. The first phase tries to select the primary parameters which conduct the lowest energy consumption and ensure QoS requirements for uplink transmission. The second phase applies a weighting strategy to determine the precise scheduling order of uplink requests based on the scheduling emergency and inflexibility. In addition, it also adjusts the corresponding results appropriately to satisfy the strict delay constraint if needed while considering energy efficiency. Extensive simulation results show that our scheme can enlarge the number of serving devices with guaranteed QoS and decrease the packet drop ratio while saving energy.
The rest of this paper is organized as follows. Related work is discussed in Section
In the literature, the studies [
Based on the above observation, it motivates us to address the issue of considering both transmission reliability and energy efficiency by scheduling multitypes of RUs with optimal repetition in NB-IoT networks.
In this section, we first give an overview of the operation modes of NB-IoT. Then, we introduce the resource unit and the repetition mechanism used in NB-IoT. Finally, we formally define our resource allocation problem and show it to be NP-complete.
In NB-IoT, all devices connect with the centralized base station (also called the
Using the bandwidth of one
Three-operation modes of NB-IoT.
Using the bandwidth of one RB in the guard-band of LTE carrier as the access spectrum is shown in Figure
Using the bandwidth of a reframed GSM carrier as the access spectrum is shown in Figure
In NB-IoT, the resource is divided into
The types of resource units (RUs) supported in NB-IoT.
Subcarrier spacing | Number of tones (subcarriers) | Classification | Number of slots |
---|---|---|---|
15 KHz | 1 | single-tone | 16 |
| |||
15 KHz | 3 | multi-tone | 8 |
6 | 4 | ||
12 | 2 | ||
| |||
3.75 KHz | 1 | single-tone | 16 |
Multiple types of RUs.
In NB-IoT, one of the key features is the repetition mechanism, which is designed to enhance the reliability of transmission and enlarge the network coverage. According to the NB-IoT standard, the transmission RUs of each device
The example of repetitive transmissions.
Main parameters of DCI (format N0).
Parameter | Value |
---|---|
subcarrier indication ( | |
resource assignment ( | |
modulation and coding scheme ( | |
repetition number ( | |
Specifically, subcarrier indication (
Subcarrier indication and the corresponding subcarrier sets.
Subcarrier indication ( | Set of Allocated subcarriers ( |
---|---|
0–11 | |
12–15 | |
16–17 | |
18 | |
19–63 | reserved |
In this paper, we consider an NB-IoT network with a base station (eNB) serving
When scheduling, each
For each
According to Table
Note that the scheduling results will be carried by the DCI message, which is scheduled at
Now, we consider the current scheduling subframe is
Therefore, our problem can be summarized as an optimization-like problem:
Table
Summary of notations.
notation | definition |
---|---|
| number of repetitions of |
| subcarrier indication of |
| number of RUs of |
| modulation and coding scheme of |
| set of allocated subcarriers of |
| uplink request of |
| required transmission reliability of |
| delay constraint of |
| data arrival time of |
| transmit power of |
| maximum transmit power of |
| RU type of |
| received signal-to-noise ratio of |
| received power |
| transmitter gain of |
| receiver gain of |
| path loss between |
| subcarrier bandwidth of the NB-IoT (Hz) |
| noise power |
| interference perceived at the eNB |
| bit-error-rate of |
| SNR threshold (dB) |
| data rate of |
| successful probability of data transmission of |
| DCI subframe index of |
| index of current scheduling subframe (ms) |
| feasible subcarrier set |
| index of earliest available subframe of subcarrier |
| allocation start time of RU of |
| number of slots for a single RU of |
| |
notations of the proposed scheme | definition |
| |
| feasible setting pairs of RU type and MCS of |
| score value of |
| weighting factors |
| urgent level of |
| remaining time of |
| potential waste of |
| earliest available allocation subframe (ms) of RUs for subcarrier |
| best subcarrier of |
| cost ratio of |
| number of choices of RU types |
The addressed problem is NP-complete.
To simplify the proof, we consider the case of subcarrier spacing with 3.75 KHz where the UEs use the single-tone only and the modulation and coding scheme (MCS) is monotonic. So, the number of repetitions with minimal transmission power to meet required reliability of each UE is unique. Thus, the
We first show that the EUSD problem belongs to NP. Given a problem instance and a solution containing the set of repetition numbers it can be verified whether or not the solution is valid in polynomial time. Thus, this part is proved.
We then reduce the
We then construct an instance of the EUSD problem as follows. Let
Suppose that we have a solution to the EUSD problem, which is a set of repetition parameters
Conversely, let
Since the EUSD problem is NP-complete, finding the optimal solution is impractical due to the time complexity. Thus, we propose a low-complexity, energy-efficient, and high-reliable scheme to tackle this problem. This scheme consists of two phases. The first phase exploits the strategy of “
The goal of the first phase is to determine the default parameters for each UE, including the type of RUs (
For each
For each
After that we have all the feasible RU type and MCS setting pairs with each of their allowed repetition numbers
Based on the results of Steps 1 and 2, we calculate the most energy-saving repetition number
Then, reform
Then, we choose the best triplet of
Through the above steps, we can determine the best RU type
The goal of the second phase is to optimize the scheduling results of requests from UEs, including the subcarrier set of RUs (
We first define a
Now, for each
Before determining the subcarrier set of RUs, we first define a function
Then, based on (
If
Here, we try to change the type of RUs and/or MCSs of
First, we define a
Then, we choose the new pair
Through the above steps, we can determine each
Below, we give an example in Figure
Examples to schedule
The scheduling results before scheduling
The scheduling results if subcarrier set is
The scheduling results if subcarrier set is
The scheduling results if subcarrier set is
The scheduling results if subcarrier set is
In this section, we develop a simulator in C++ language to verify the efficiency of the proposed scheme (currently, the well-known simulator, such as ns-3 [
The simulation parameters [
Parameter | Value |
---|---|
maximum transmit power ( | 23 dBm |
antenna gain of transmitter ( | −4 dBi |
antenna gain of receiver ( | 18 dBi |
thermal noise density ( | −174 dBm/Hz |
path loss ( | 120.9 + 30.7 |
distance from the base station | 0 |
number of UEs ( | 3000 |
request data size ( | 50 |
delay constraint ( | 50, 100, 150, 300 (ms) |
required reliability ( | |
In this simulation, we compare our scheme
We consider five performance metrics: (i)
First, we investigate the effects of number of request UEs on system throughput. As shown in Figure
Comparisons on the system throughput of all schemes.
We also investigate the effects of distribution of request data size on system throughput. As shown in Figure
Then, Figure
Finally, we investigate the impact of the distribution of required reliability on system throughout. In Figure
Then, we investigate the effects of number of request UEs on number of serving UEs. As shown in Figure
Comparisons on the number of serving UEs of all schemes.
We also investigate the effects of distribution of request data size on number of serving UEs. As shown in Figure
In Figure
Finally, we investigate the impact of the distribution of required reliability on number of serving UEs in Figure
Then, we investigate the effects of number of request UEs on packet drop rate. As shown in Figure
Comparisons on the packet drop rate of all schemes.
We also investigate the effects of distribution of request data size on packet drop rate. As shown in Figure
Then, in Figure
Finally, we investigate the impact of the distribution of required reliability on packet drop rate in Figure
Here, we investigate the effects of number of request UEs on resource consumption. As shown in Figure
Comparisons on the resource consumption of all schemes.
Here, we also investigate the effects of distribution of request data size on resource consumption. As shown in Figure
Then, in Figure
Finally, we investigate the impact of the distribution of required reliability on resource consumption, as shown in Figure
Finally, we investigate the effects of the number of request UEs on energy consumption per UE. As shown in Figure
Comparisons on energy consumption per UE of all schemes.
In Figure
Then, in Figure
Finally, we investigate the impact of the distribution of required reliability on energy consumption per UE. Figure
In this paper, we have addressed the problem of energy saving and reliable communications in NB-IoT networks. We first model this problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient and high-reliable scheme which has two phases. The first phase leverages the strategy of minimal energy cost to choose the default scheduling parameters with least energy consumption. The second phase exploits the weighting score function to balance the emergency and inflexibility of the request transmission and then serve the UEs with least potential resource waste. Simulation results have verified that our scheme can satisfy more UEs with ultra-reliability and QoS requirement while saving their energy.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This research is cosponsored by MOST 106-2221-E-024-004-, 102-2218-E-182-008-MY3, 105-2221-E-182-051, 106-2221-E-182-015-MY3, 105-2745-8-182-001, 106-2221-E-024-004, 105-2221-E-009-100-MY3, 105-2218-E-009-029, 105-2923-E-009-001-MY2, 104-2221-E-009-113-MY3, MoE ATU Plan, Delta Electronics, ITRI, Institute for Information Industry, Academia Sinica AS-105-TP-A07, and Chang Gung Memorial Hospital, Taoyuan.