Internet of Things (IoT) has triggered a rapid increase in the number of connected devices and new use cases of wireless communications. To meet the new demands, the fifth generation (5G) of wireless communication systems features native machine type communication (MTC) services in addition to traditional human type communication (HTC) services. Some of the main challenges are the heterogeneous requirements and the sporadic traffic of massive MTC (mMTC), which makes the orthogonal allocation of resources infeasible. To overcome this problem, grant free non-orthogonal multiple access schemes have been proposed alongside with sparse signal recovery algorithms. While most of the related works have considered only homogeneous networks, we focus on a scenario where an enhanced mobile broadband (eMBB) device and multiple MTC devices share the same radio resources. We exploit the approximate message passing (AMP) algorithm for joint device activity detection and channel estimation of MTC devices in the presence of interference from eMBB, and evaluate the system performance in terms of receiver operating characteristics (ROC) and channel estimation errors. Moreover, we also propose two new pilot sequence generation strategies which improve the detection capabilities of the MTC receiver without affecting the eMBB service.
The 5G systems feature three generic services: enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC) and Ultra-Reliable and Low-Latency Communications (URLLC). The diverse requirements of these services in terms of data-rates, number of connected devices, latency and reliability can lead to a sub-optimal use of the 5G network, thus network slicing is proposed as a solution that creates customized slices of the network specifically designed to meet the requirements of each service. Under the network slicing, the radio resources can be shared in orthogonal and non-orthogonal schemes. Motivated by Industrial Internet of Things (IIoT) scenarios where a large number of sensors may require connectivity with stringent requirements of latency and reliability, we propose the use of Non-Orthogonal Multiple Access (NOMA) to improve the number of URLLC devices that are connected in the uplink to the same base station (BS), for both orthogonal and non-orthogonal network slicing with eMBB devices. The multiple URLLC devices transmit simultaneously and across multiple frequency channels. We set the reliability requirements for the two services and evaluate the pairs of achievable sum rates. We show that, even with overlapping transmissions from multiple eMBB and URLLC devices, the use of NOMA techniques allows us to guarantee the reliability requirements for both services.
In this work, we study the coexistence in the same Radio Access Network (RAN) of two use cases present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC). eMBB services are requested for applications that demand extremely high data rates and moderate requirements on latency and reliability, whereas mMTC enables applications for connecting a massive number of low-power and low-complexity devices. The coexistence of both services is enabled by means of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under the orthogonal slicing, the radio resources are exclusively allocated to each service, while in the non-orthogonal slicing the traffics from both services overlap in the same radio resources. We evaluate the uplink performance of both services in a scenario with a multi-antenna Base Station (BS). Our simulation results show that the performance gains obtained through multiple receive antennas are more accentuated for the non-orthogonal slicing than for the orthogonal allocation of resources, such that the non-orthogonal slicing outperforms its orthogonal counterpart in terms of achievable data rates or number of connected devices as the number of receive antennas increases.
The Fifth Generation (5G) of wireless networks introduced support to Machine-Type Communications (MTC), which is the wireless connectivity solution for Internet of Things (IoT) applications. MTC is split into two different categories: massive MTC (mMTC) and critical MTC (cMTC). Current 5G standards and technologies are not capable of fully satisfying the requirements of both mMTC and cMTC use cases, thus industry and academia have already started developing solutions for MTC in beyond-5G and 6G networks. In some mMTC use cases, receivers might not be equipped with a large number of antennas owing to cost, size or power limitations, thus the number of active devices in a time slot may surpass the number of antennas. Due to the limited spatial multiplexing capabilities, only multi-antenna techniques are not enough to provide connectivity to a massive number of devices in such scenarios. In this paper, we propose and evaluate the performance of iterative linear receivers that can address this issue. By combining Multiple-Input Multiple-Output (MIMO) techniques with Non-Orthogonal Multiple Access (NOMA) exploiting Successive Interference Cancellation (SIC) or Parallel Interference Cancellation (PIC) decoding, the proposed novel receivers are capable of performing dynamic ordering SIC/PIC decoding of multiple overlapping signals even when the number of active devices surpasses that of receive antennas. The performance of the receivers is studied in terms of outage probability and computational complexity. Simulation results show that, among all the receivers studied in this paper, the PIC-based Minimum Mean Square Error (MMSE) receiver presents the best performance while at the same time reducing the number of complex signal operations such as matrix inversions.
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