Massive Connectivity: Non-Orthogonal Multiple Access to High Performance Random Access

Massive Connectivity Learn to support more devices and sensors in Internet of Things applications through NOMA and machine-type communication Non-orthogonal multiple access (NOMA) has held much interest due to its ability to provide a higher spectral efficiency-such as more bits per unit bandwidth in Hertz-than other, orthogonal multiple access schemes. The majority of this research focuses on the application of NOMA to downlink channels (from base station to users) in cellular systems as its use for uplink (users to base station) is somewhat circumscribed. However, NOMA has recently been employed in contention-based uplink access, which has shown an improvement in performance that allows an increase in the number of users that can be supported. As a result, NOMA is promising for machine-type communication (MTC) in 5G systems and beyond, making it a key enabler of the Internet of Things (IoT). Massive Connectivity provides an in-depth, comprehensive view of the benefits and drawbacks of uplink NOMA random access (RA) systems. This text offers a basic introduction and description of uplink NOMA RA systems before considering the possibilities for evolution of the scheme as attempts are made to derive the most benefits and overcome any weaknesses. The book further presents key performance analysis while also highlighting game-theoretic views. In essence, by describing the essential properties of stable and high-throughput yielding RA systems, the book demonstrates that uplink NOMA can fulfill these required properties. Massive Connectivity readers will also find: An extensive literature survey on RA systems and their applications since the 1970s Recent advances in random access for massive connectivity Retransmission control algorithms for NOMA random access Discussion of how uplink NOMA random access systems can be integrated into the existing long-term evolution (LTE) or upcoming 5G cellular networks Massive Connectivity is a useful reference for field engineers and academics, as well as experts for random access systems for IoT applications

Beam Selection for Two-Step Random Access in MTC With a Small Number of Antennas

In 5th generation (5G) systems, two-step random access has been introduced for machine-type communication (MTC) to lower signaling overhead. It is also shown that when a base station (BS) is equipped with a large number of antennas, the notion of massive multiple-input multiple-output (MIMO) can be exploited to improve the performance in terms of throughput and spectral efficiency. In this paper, we consider the case that a BS is equipped with a small number of antennas, in which a sufficiently high spatial selectivity cannot be obtained, and propose an approach to two-step random access based on beam selection that can perform well with a small number of antennas. In the proposed approach, spreading sequences are also used in conjunction with beam selection to mitigate interference due to limited spatial selectivity. To analyze the performance of the proposed approach, the distribution of the signal-to-interference-plus-noise ratio (SINR) is derived as a closed-form expression and the throughput is found. We compare the throughput of the proposed approach with those of conventional two-step random access approaches through analysis and simulations. While the theoretical results agree with simulation results, we can see that the proposed approach outperforms conventional ones when the number of antennas is small for a wide range of traffic intensity

Random Access-based Multiuser Computation Offloading for Devices in IoT Applications

In various Internet-of-Things (IoT) applications, a number of devices and sensors are used to collect data sets. As devices become more capable and smarter, they can not only collect data sets, but also process them locally. However, since most devices would be limited in terms of computing power and energy, they can take advantage of offloading so that their tasks can be carried out at mobile edge computing (MEC) servers. In this paper, we discuss computation offloading for devices in IoT applications. In particular, we consider users or devices with sporadic tasks, where optimizing resource allocation between offloading devices and coordinating for multiuser offloading becomes inefficient. Thus, we propose a two-stage offloading approach that is friendly to devices with sporadic tasks as it employs multichannel random access for offloading requests with low signaling overhead. The stability of the two-stage offloading approach is considered with methods to stabilize the system. We also analyze the latency outage probability as a performance index from a device perspective

A Random Access based Approach to Communication-Efficient Distributed SGD

In this paper, we study communication-efficient distributed stochastic gradient descent (SGD) with data sets of users distributed over a certain area and communicating through wireless channels. Since the time for one iteration in the proposed approach is independent of the number of users, it is well-suited to distributed SGD with a large number of users or devices. Furthermore, since the proposed approach is based on random access in machine-type communication (MTC), it can be easily employed for training models with a large number of devices in various Internet-of-Things (IoT) applications where MTC is used for their connectivity. For fading channel, we show that noncoherent combining can be used. As a result, no channel state information (CSI) estimation is required

Low complexity MAP detection using approximate LLR for CDMA iterative receivers

Low complexity MAP detection using approximate LLR for CDMA iterative receiver

Rare-Event Analysis of Packet-Level Coding for URLLC via Virtual Queue

In this paper, we propose a method to analyze the performance of ultra-reliable low-latency communication (URLLC) with packet-level coding when no feedback from a receiver is available. Unlike conventional methods of analyzing URLLC performance using average error rate, we focus on the events of burst or clustered (reception) errors1 as they can be fatal for certain real-time wireless control systems. In the proposed method, to see the impact of clustered errors on the performance, a virtual queue is considered, where the reception error sequence is regarded as the arrival process and the departure process is characterized by the target error rate. This virtual queue allows to find how often the events of clustered errors of certain sizes occur using large deviations theory. For packet-level channels modeled by an independent and identically distributed process and a two-state Markov chain, the quality-of-service exponents are derived, and the asymptotic probability of buffer overflow is obtained, which agrees with simulation results. This demonstrates that the proposed method using virtual queue is useful to find the probability of system failure due to clustered errors and allows to determine the values of key parameters of URLLC for a certain probability of system failure under given conditions

Co-Phase Over-the-Air Aggregation for Multi-Server Federated Learning with Randomized Transmissions

While federated learning is widely studied as a distributed machine learning technique that effectively uses distributed datasets for training, there are still a number of challenges. In particular, for datasets that reside on mobile devices (or mobile phone users), the limited bandwidth becomes a bottleneck as the number of devices increases, because the local gradient vectors are be transmitted over the scarce wireless channel. Thus, the notion of over-the-air (OTA) computation has been considered to avoid bandwidth expansion due to the increase in devices by exploiting the superposition property of radio channels. In this paper, we consider a generalization of OTA to the case of multiple servers, where each server has own model. Using co-phase OTA aggregation, it is shown that a shared channel can be used for all different servers/models. We also propose a digital OTA approach for multi-server federated learning with randomized transmissions

On Analysis of Network Coding-Based One-Shot Random Access for Low-Latency MTC

On Analysis of Network Coding-Based One-Shot Random Access for Low-Latency MT

On Analysis of Network Coding-Based One-Shot Random Access for Low-Latency MTC

For low-latency communication in massive machine -type communication (MTC), since re-transmissions may not be allowed, one-shot random access (OSRA) such as $K$-repetition or network coding (NC)-based OSRA can be considered. In our previous study, it was shown that NC-based OSRA can always perform better than $K$-repetition for some limited cases. In this paper, more realistic conditions are considered to derive the packet loss rate (PLR) of NC-based OSRA for a fair performance comparison in terms of PLR. Interestingly, it is shown that $K$-repetition can outperform NC-based OSRA when the traffic intensity is sufficiently low, which might be necessary to provide a high reliability in terms of PLR. For a low-medium traffic intensity, the PLR of NC-based OSRA is lower than that of $K$-repetition. Simulations are also carried out to confirm our derivations

Wireless Distributed Matrix-Vector Multiplication using Over-the-Air Computation and Analog Coding

Wireless Distributed Matrix-Vector Multiplication using Over-the-Air Computation and Analog Codin