Channel Access Research Articles

Peak Age of Information Optimization of Slotted Aloha: FCFS Versus LCFS

This paper aims at optimizing the Peak Age of Information (PAoI) performance of slotted Aloha networks with two representative data queue service disciplines, i.e., first-come first-served (FCFS) and last-come first-served (LCFS). Specifically, by assuming a unit-size buffer and Bernoulli packet arrivals, the average PAoI are derived and minimized by properly tuning the packet arrival rate and the channel access probability of each sensor. Depending on whether the packet arrival rate can be tuned or not, the individual optimization and joint optimization are both considered. The optimal system parameters and corresponding minimum PAoI are explicitly characterized. The analysis shows that in the joint optimization case, the PAoI linearly increases with the network scale and the minimum PAoI with LCFS is 16.8% lower than that with FCFS in the massive access scenario. Yet, for achieving such performance gain over FCFS, each sensor with LCFS should generate a new sample in each time slot, indicating soaring energy consumption due to the sampling operation. The analysis shows that the energy efficiency with LCFS is always lower than that with FCFS, and the gap keeps increasing with the number of sensors, which reveals a clear age-energy tradeoff in terms of service disciplines.

Reinforcement Learning-based Wi-Fi Contention Window Optimization

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the increase of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. In our proposed approach, we assess two different observation metrics, the average of the normalized level of the transmission queue of all associated stations and the probability of collisions. The overall network’s throughput is defined as the reward. The action is the contention window (CW) value that maximizes throughput while minimizing the number of collisions. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for both static and dynamic scenarios, regardless of the number of stations. Additionally, our results show that observations based on the average of the normalized level of the transmission queues have a slightly better performance than observations based on the collision probability. Moreover, the performance difference with BEB is amplified as the number of stations increases, with DQN and DDPG showing a 45.52% increase in throughput with 50 stations.

PRACH in Self-Powered Satellite Internet of Things: Low-Cubic Metric Preamble Design and Detection

This paper focuses on the energy-efficient physical random access channel (PRACH) design for self-powered sensors integrated satellite internet of things (S-IoT), where two novel preamble design schemes are proposed by jointly considering cubic metric (CM) reduction, coverage enhancement, resistance to carrier frequency offset (CFO), and low-complexity detection. Specifically, both of schemes possess a uniform preamble format by repeatedly cascading a series of conjugate Zadoff-Chu sequence pairs in the frequency domain, which can effectively mitigate the adverse effect of large CFO under the premise of ensuring PRACH coverage. Furthermore, phase rotation optimization and inverse discrete Fourier transform scaling are respectively leveraged in the two schemes to cope with the problem of CM performance degradation caused by sequence repetitions. We also develop corresponding preamble detection mechanisms that are capable of performing joint timing advance and CFO estimation, and conduct mathematical analysis in terms of CM and maximum coupling loss. Simulation results verify the feasibility of our schemes in self-powered S-IoT, and demonstrate that with the increment of the number of sequence repetitions, the proposed schemes can maintain relatively lower CM values in comparison to the state-of-the-art ones, while having significantly enhanced PRACH coverage and detection performance.

Remote Monitoring of Two-State Markov Sources via Random Access Channels: An Information Freshness vs. State Estimation Entropy Perspective

Remote Monitoring of Two-State Markov Sources via Random Access Channels: An Information Freshness vs. State Estimation Entropy Perspective

МАТЕМАТИЧНА МОДЕЛЬ МОНІТОРИНГУ ПОДІЙ В СИСТЕМІ УПРАВЛІНЯ РОЗПОДІЛЕНИМИ БАЗАМИ ДАНИХ НА ОСНОВІ СЕНСОРІВ ПОДІЙ

Methods of increasing the effectiveness of threat detection in distributed databases using the event monitoring system are considered in the work. It is noted that the system works on the basis of the event monitoring model of heterogeneous distributed databases. This model involves three stages of event processing, which are based on appropriate methods. It is highlighted that the mechanisms underlying the functioning of the mentioned methods should be reduced to a single data format to eliminate the possible appearance of incorrect work in future calculations. The event processing method on the monitoring server allows for processing event matrices and transferring the function to control tools, on the basis of which appropriate decisions are made to improve reliability. The article develops a modified method of analyzing and monitoring events in non-relational distributed databases. Event monitoring options for lookup operations in distributed databases are offered. To confirm the theoretical results, mathematical modeling and experimental research on monitoring parameters and data processing speed on the event monitoring server were carried out. The number of detected incidents and the number of incorrectly identified events were studied, which in turn allowed for the protection of information from unauthorized actions or changes. Also, it should be emphasized that the model proposed in the work describes detailed monitoring of requests, transactions, and stored procedures in a database with information about possible incidents in real time and prompts intrusion warnings. In addition, the features of the proposed model make it possible to investigate and monitor the emergence of new vulnerable segments of the database and promptly respond to them, eliminating threats to access channels. The paper compares the speed of data processing based on files and database tables, which allows for fully performing experiments and confirming analytical data experimentally.

A Survey on Over-the-Air Computation

Communication and computation are often viewed as separate tasks. This approach is very effective from the perspective of engineering as isolated optimizations can be performed. However, for many computation-oriented applications, the main interest is a function of the local information at the devices, rather than the local information itself. In such scenarios, information theoretical results show that harnessing the interference in a multiple access channel for computation, i.e., over-the-air computation (OAC), can provide a significantly higher achievable computation rate than separating communication and computation tasks. Moreover, the gap between OAC and separation in terms of computation rate increases with more participating nodes. Given this motivation, in this study, we provide a comprehensive survey on practical OAC methods. After outlining fundamentals related to OAC, we discuss the available OAC schemes with their pros and cons. We provide an overview of the enabling mechanisms for achieving reliable computation in the wireless channel. Finally, we summarize the potential applications of OAC and point out some future directions.

Coded Over-the-Air Computation for Model Aggregation in Federated Learning

This letter introduces a new coded transmission design for model aggregation in federated learning (FL) over Gaussian multiple access channels (MAC), named coded over-the-air computation (codedAirComp). It enjoys the optimality of analog AirComp-based uncoded transmission for fast model aggregation, but also leverages the traditional source-channel separation principle for more practical uses. Specifically, the proposed codedAirComp employs stochastic uniform quantization for local gradient compression and nested lattice coding for channel transmission. Compared with the traditional coding scheme, the proposed scheme significantly reduces the model aggregation distortion and improves the overall learning accuracy.

On the Convex Hull of the Achievable Capacity Region of the Two User FDM OMA Downlink

In multiple access channel systems, such as a mobile communication network, it is important to determine how to share the available resources (for example bandwidth and power) among the users. In recent years, one of the promising scheme is Non-Orthogonal Multiple Access (NOMA), where, unlike the traditional Orthogonal Multiple Access, OMA solution, signals for the different users overlap in some domain (power-domain NOMA, code-domain NOMA, etc). In order to evaluate the performance of any NOMA scheme, we need to compare the achievable bit rates of the users (the capacity region) to an OMA case with comparable parameters (for example, same total bandwidth, same total power and same channel conditions, etc). To make this comparison, we first need to know the capacity region for the OMA cases. Many papers make such comparison without detailing the derivation of the capacity region of the OMA case they compare to [1], [2], [3], [4]. In some cases, we have only one free parameter to choose (for example in uplink frequency division multiplexing systems for two users, it is the bandwidth ratio between the users), and the achievable capacity can be directly calculated for both users depending on the single parameter (hence the boundary of the capacity region is trivial). In other cases, such as downlink frequency division multiplexing systems, even for only two users, we have to allocate optimally two resources between the users: the bandwidth and the base station’s available power. Hence, it is far from being trivial to determine which combination is better and where the boundary of the capacity region is. In this paper, we provide a derivation for that case.

Joint Activity Detection and Channel Estimation for Massive IoT Access Based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO

The multi-panel array, as a state-of-the-art antenna-in-package technology, is very suitable for millimeter-wave (mmWave)/ terahertz (THz) systems, due to its low-cost deployment and scalable configuration. But in the context of non-uniform array structures it leads to intractable signal processing. Based on such an array structure at the base station, this paper investigates a joint active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS) for application to the massive Internet of Things (IoT). Specifically, by exploiting the structured sparsity of mmWave/THz massive IoT access channels, we firstly formulate the multi-panel massive multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a multiple measurement vector (MMV)-CS problem. Then, we harness the expectation maximization (EM) algorithm to learn the prior parameters (i.e., the noise variance and the sparsity ratio) and an orthogonal approximate message passing (OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation results verify the improved AUD and CE performance of the proposed scheme compared to conventional CS-based algorithms.

Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT

The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance.

A comparative analysis of dual-axis implants placed into maxillary anterior extraction sockets versus virtual planning with uniaxial implants: A simulated cone beam computed tomography study of implant length and diameter.

The biaxial nature of the anterior maxilla poses a surgical and restorative challenge in implant dentistry. The present study sought to investigate the apical socket perforation rate (ASPR) from a simulated uniaxial implant placement and to determine the effect of implant length and diameter on ASPR when a uniaxial implant was placed compared with the orientation of the pre-existing dual-axis implant. Cone beam computed tomography (CBCT) scans from the database of three private practices were searched for patients who received dual-axis implants within the esthetic zone in immediate tooth replacement therapy. A uniaxial implant was virtually placed using the pre-existing screw access channel of the dual-axis implant as a reference. The closest length and diameter were selected for the simulated implant. ASPR by the uniaxial implant was recorded. In addition, the affordable maximum length of a corresponding uniaxial implant that would avoid apical socket perforation was measured. Eighty-one patients with a total of 101 dual-axis dental implants were selected for analysis. A simulated virtual surgical planning with uniaxial implants revealed high ASPR (48.51%). When the length of the uniaxial implant was reduced to 11 and 9 mm, ASPR was decreased to 41.58% and 20.79%, respectively. Dual-axis implant design effectively evades anatomical challenges in the anterior maxilla (esthetic zone). Considering the current evidence, efforts should be made to carefully consider the angular disparity between the extraction socket-alveolus complex and the future restorative emergence so that a harmonious biologic-esthetic result may be more predictably and consistently obtained.

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Learning Emergent Random Access Protocol for LEO Satellite Networks

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LEO SAT networks exhibit extremely long link distances of many users under time-varying SAT network topology. This makes existing multiple access protocols, such as random access channel (RACH) based cellular protocol designed for fixed terrestrial network topology, ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH). In stark contrast to existing model-based and standardized protocols, eRACH is a model-free approach that emerges through interaction with the non-stationary network environment, using multi-agent deep reinforcement learning (MADRL). Furthermore, by exploiting known SAT orbiting patterns, eRACH does not require central coordination or additional communication across users, while training convergence is stabilized through the regular orbiting patterns. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput with around two times lower average access delay while achieving 0.989 Jain’s fairness index.

Optimization of the parameters of a special purpose infocommunication network under the conditions of the influence of random factors on the base of the theory of disaster

The problem of creating secure special-purpose information communication networks based on public access channels with a priori unknown structure and functioning algorithms optimized according to the criteria of structural and functional integrity requires further consideration and resolution. The found ways of solving the scientific problem will contribute to the construction of special-purpose protected information communication networks based on public access channels using a mathematical apparatus for determining the saddle point in the process of network optimization, which will ensure the reliability and survivability of the network in emergency situations.

Secure Transmission Over Multiple Access Wiretap Channel by Cross-Time Interference Injection

Due to the openness of wireless communication, information transmitted in the multiple access channel is vulnerable to eavesdropping by illegitimate users. Classic artificial noise-assisted schemes can improve security but are not suitable for power-constrained users. To address this problem, we propose a cross-time interference injection scheme in this paper, which improves security by introducing cross-time self-interference and inter-user interference, and does not require artificial noise. The proposed scheme exploits the properties of the Hadamard matrix and the wireless channels, so that the eavesdropping channel suffers from stronger interference than the legitimate channel. We analyze the security performance when both the legitimate receiver and the eavesdropper exploit the successive interference cancellation (SIC) scheme, including the secrecy sum rate, the secrecy capacity, and the secrecy outage probability. We further investigate the effect of the number of eavesdropper’s antennas, the number of legitimate users, and the number of time slots used by the scheme on the security performance, and obtain the corresponding relationship between these parameters when achieving a positive secrecy rate. Finally, simulation results corroborate our theoretical analysis.

Intelligent Over-the-Air Computing Environment

A key service of the sixth generation (6G) of wireless networks is envisioned to be native Artificial Intelligence, which calls for radical changes to the way the nodes communicate and perform computations, as well as the role of wireless environment. For this purpose, over-the-air computing (AirComp) is a promising technique for ultra low-latency wireless data aggregation, enabled by the waveform superposition properties of a multiple access channel. In this letter, the synergy of decentralized AirComp, reconfigurable intelligent surfaces (RISs) and machine learning is proposed, to transform the wireless environment to intelligent AirComp environment (IACE), i.e., with inherent and advanced capabilities to perform computations in a fully decentralized way at the physical layer. Specifically, we minimize the AirComp error, i.e., the average mean-square errors of devices with respect to a target function, by jointly optimizing the RIS phase-shift vector and the transmission and reception scaling factors of devices. Also, to solve this challenging problem, we propose an online deep neural network (DNN) optimization approach. Finally, simulation results validate the effectiveness of IACE and the proposed DNN approach.

Performance analysis of NOMA in Rayleigh and Nakagami Fading channel

Cellular connectivity is expanding rapidly in the modern world. The multiple access strategy is one of the highly used methods for allocating the range of users in cellular network. Spectrum allocation is a crucial element to take into account since cellular communication is becoming more and more popular. NOMA is a channel access mechanism used in 5G mobile communication. It is also known as non-orthogonal multiple access. NOMA is a potential strategy for enhancing spectral efficiency and sum rate. Using the NOMA method, we evaluated the BER versus transmitted power of two users in rayleigh and nakagami fading channels. In this NOMA setup, a single antenna is shared by two users. Two users can accept the same frequency using 5G NOMA technology, but at different power levels. The results of the MATLAB simulation show that the two user NOMA in the Nakagami channel performs better than the Rayleigh channel in terms of Bit Error Rate vs. Transmitted Power.

The Strongly Asynchronous Massive Access Channel

This paper considers the Strongly Asynchronous, Slotted, Discrete Memoryless, Massive Access Channel (SAS-DM-MAC) in which the number of users, the number of messages, and the asynchronous window length grow exponentially with the coding blocklength with their respective exponents. A joint probability of error is enforced, ensuring that all the users' identities and messages are correctly identified and decoded. Achievability bounds are derived for the case that different users have similar channels, the case that users' channels can be chosen from a set which has polynomially many elements in the blocklength, and the case with no restriction on the users' channels. A general converse bound on the capacity region and a converse bound on the maximum growth rate of the number of users are derived. It is shown that reliable transmission with an exponential number of users with an exponential asynchronous exponent with joint error probability is possible at strictly positive rates.

Социально-политические потребности современной российской молодёжи

The paper considers the factors that influence the manifestation and extent of fulfillment of social and political needs of Russian youth. Students aged 18–24 are identified as the key object of analysis as the most civically active part of the youth community. The author distinguishes the following factors which influence the fulfillment of social and political needs: place of residence, education, basic civic activity, development of Internet access channels, level of culture and physical condition.
 Based on the results of the analysis and expert survey four major groups of socio-political needs of young people were identified: basic needs, communication needs, development of youth self-government and non-profit organizations, and implementation of state youth po­licy and youth-related policy. Each need was characterized in terms of areas of its implementation in the context of practical work with young people. The author also defined motives of civil activity of youth which form the demand in this segment of society for political and social activism in its various manifestations.

Adaptive parameters selection for uplink grant-free URLLC transmission in 5G systems

Ultra-Reliable Low-Latency Communications (URLLC) is a key service of 5G systems that imposes very strict Quality of Service (QoS) requirements. Typically, URLLC applications require successful delivery of at least 99.999% of packets with a packet delivery time below 1 ms. To satisfy these requirements for uplink URLLC traffic, 5G specifications introduce a grant-free channel access method. It allows a base station (gNB) to select transmission parameters for each User Equipment (UE) in advance. The algorithm for transmission parameters selection shall take into account the following factors: (i) various UEs may have significantly different channel conditions that can change with time; (ii) if several UEs share common channel resources, their transmissions can interfere with each other; (iii) the gNB can use sophisticated decoding mechanisms, such as Successive Interference Cancellation (SIC), to mitigate the interference problem. In contrast to the existing studies that take into account only some of these factors, this paper proposes an algorithm that considers all three factors and adaptively selects the transmission parameters for each UE based on the desired signal and interference measurements available at the gNB. Numerical results obtained with NS-3 show that the developed algorithm significantly increases the network capacity for both periodic and sporadic URLLC traffic compared with the state-of-the-art algorithms.