In 3G- and 4G-based networks, orthogonal multiple access (OMA) techniques were commonly used for resource allocation to users for improving spectral efficiency (Balyan and Saini, 2011, 2014; Balyan et al., 2018; Saini and Balyan, 2012). For 5G and beyond networks, non-orthogonal multiple access (NOMA) proves out better than the conventional orthogonal multiple access (OMA) techniques due to a large number of users with higher speed requirements. This is mainly due to the requirements of OMA to maintain orthogonality (Rabie and Adebisi, 2017). NOMA allows a single transmitter using the same frequency to send multiple signals for multiple users, the multiple signals use superposition of power, which improves overall spectrum efficiency (Balyan, 2020; Balyan and Daniels, 2020; Ding et al., 2017). Device-to-device (D2D) communication can be used to establish direct communication between users without getting processed through a base station (BS) or other backbone networks. This help reducing the transmission power of users and the traffic loads of BS (Ahmed et al., 2018; Liu et al., 2015). The D2D communication combined with NOMA got attention recently. The combination of both D2D communication and NOMA technology allows more users to be serviced at a time in the network. The BS transmits to multiple mobile users using NOMA (Pan et al., 2018). While keeping the minimum requirements condition of mobile users, the D2D users total rate is maximized. A channel allocation algorithm, which maximizes the total rate of the network, is proposed in Zhao et al. (2018), after analyzing D2D users’ rates using NOMA technology. The work in the study of Arachchillage et al. (2018) summarizes the recent advances and future research challenges of NOMA. The work also demonstrates how the inclusion of NOMA impacts D2D performance, radio frequency, energy harvesting, multiple input multiple output (MIMO), and other emerging 5G technologies. When D2D pairs and mobile users communicate in the presence of each other mutual interference exists, appropriate power control methods need to be implemented to ensure signal to interference plus noise ratio (SINR) is above a certain threshold level for guaranteed quality of service (QoS). Another factor that is influenced due to the presence of D2D pairs and mobile users simultaneously is a delay or the latency, which is an important factor for time-sensitive applications. If both physical layer and latency need to be improved together, the channel state and queuing at each device needs to be known before transmitting and receiving. For a user with a probability of higher latency due to the long queue and with a weak channel that can be used, power control and resource allocation should be in place to overcome the problem of latency and weak channel.
Some of the work in the literature addresses latency in D2D communication. The work in the study of Cui et al. (2012) uses the Large Deviation Theory, which uses equivalent rate constraints that are derived from equivalent latency constraints. The authors in the study of Cui et al. (2012) also use the Lyapunov Drift Theory for queue stabilization.
The work in the study of Cui et al. (2012) was used (Li et al., 2017) for the latency analysis of the D2D pairs, and is concluded that D2D pairs latency depends on the order of data arrival and type. Another approach named Stochastic majorization is used in Asheralieva and Miyanaga (2016) that implements the longest queue highest rate possible approach for providing a power control, which is latency aware. This perfectly works for the networks where data arrivals are consistent in type and rate. Markov Decision Process (MDP) is also used for optimal resource control for wireless systems with latency issues. Wang et al. (2015) derive an approximation of MDP for modeling the dynamic power control in D2D communication, which is latency aware. The complexity is reduced by assuming that the Medium Access Control (MAC) layer has interference filtering property.
The work in the study of Xu et al. (2020) is done to address the latency issues and to find out the trade-off between reliability and block length. The finite block length codes (FBCs) capacity approximation is adopted in place of the Shannon Capacity formula. To cope with the latency constraints and to explicitly specify the trade-off between block length (latency) and reliability, the normal approximation of the capacity of finite block length codes (FBCs) is adopted, in contrast to the classical Shannon capacity formula. NOMA is used as a transmission scheme. An interference alignment (IA) and independent component analysis (ICA) (IA–ICA)-based semi-blind scheme is proposed in Wan et al. (2020). The NOMA-based transmission provides a better symbol error rate (SER) than existing approaches in the literature with high reliability and low latency. The authors in Xin et al. (2019) develop a spatiotemporal mathematical model for analyzing the performance of the mobile network with prioritized data transmissions. For D2D users, a dynamic interference model is constructed using thinned Poisson point process to set D2D users location and buffer to store data. A priority queuing model is used for variable rate traffic arrival. The work in this paper is done to address the issue of latency when D2D pairs communicate in an underlying mobile network. NOMA-based communication is adopted for transmission hybridized with TDMA for bit and time allocation. It is less complex also.
The work in this paper is described as follows. The system model and proposed work are given in the second section. The problem is formulated in the third section. The simulation results are demonstrated and explained in the fourth section. Finally, the paper is concluded.
In the considered cellular network, a single cell environment is taken that consists of a base station (BS), mobile user (MU), and D2D pairs denoted by
Nomenclature and abbreviations.
SINR | Signal to interference plus noise ratio |
D2D | Device to device |
MU | Mobile user |
Data rate of channel | |
Variance of Additive White Gaussian Noise (AWGN) | |
Rate and throughput | |
Data arrival at D2D transmitter | |
Queue length |
After removing SINR decoded for user 2, the decoded SINR for user 1 signal is:
The decoded SINR for user 2 signal with interference from user 1 considered as noise is:
The achieved data rate and throughput for user 1 in time slot
The achieved data rate and throughput for user 2 in time slot
The data arrival at D2D transmitter is with a rate
where
A
In uplink transmission of mobile users, the data sent by MU that is using a subchannel
If at the same time the
where
From Equations (11) and (12), the achievable rates of MU and D2D users can be calculated. In
where
In the slot, a scheduled transmitter can adjust its transmit power to achieve maximum throughput while keeping latency limitations into consideration of D2D users. The time division multiple access (TDMA) is used for scheduling in each slot for D2D users that maximizes the sum rate of the network (queuing time or latency is also considered).
To evaluate the performance with respect to latency consideration,
Latency of mobile user: The latency experience by a mobile user depends upon the latency of D2D pair that experiences maximum latency for Power efficiency: Power efficiency is defined as the mean of power consumption required for achieving needed spectral efficiency:
Cumulative distributed function (CDF) of mean total sum throughput that is equal to:
The four scenarios are: Minimum rate requirement for MU and power is variable (MRR). No minimum rate requirement for MU and power is variable (NMRR). Minimum rate requirement for MU and power is maximum (MRR-Pmax). No minimum rate requirement for MU and power is maximum (NMRR-Pmax).
Figure 1 compares the latency of MU in presence of D2D pairs. The MU experience minimum latency when minimum rate requirement is defined, i.e. For MRR and MRR-Pmax. The variable power and no minimum rate for MU (NMRR) experiences maximum latency. The power efficiency
Figure 2 compares the data rate supported in four scenarios. The data rates taken are from 0 to 12 Mbps. When the power is set to maximum, the 50% of data rates that can be supported are less than 2 Mbps. When the power is variable the supported around 65% data rate is around 5 Mbps. When data rate is below 5 Mbps, the ratio of lower data rate users is relatively on higher side that makes CDF curve to experie nce a slower increase while at higher data rate the case is exactly opposite that makes CDF to increase rapidly. The cumulative distribution for throughput sum gives poor results for variable power scenarios as compare to maximum power scenarios. Also, among variable power and maximum power scenarios, the scenario without minimum rate requirements performs better.
The work in this paper focuses on sharing of the uplink resources of mobile user with D2D pairs using both NOMA-based power allocation and TDMA-based slot sharing. The work is interference and latency driven. For four different scenarios, latency, data rate, power efficie ncy, and throughput are compared. The results show that when power is maximized and fixed, higher throughput and lower latency can be achie ved as compared to scenarios when power is variable. The advantages associated with variable power scenarios are better power efficie ncy and better support for data rates.