Dual connectivity is essential for sixth-generation (6G) and future networks, with carrier aggregation (CA) serving as the key technology that enhances data rates by dynamically allocating multiple frequency carriers to users. However, the constant change of carriers can introduce excessive handovers, leading to signaling overhead and latency issues. In this work, we propose a two-step carrier switching mechanism where the first step predicts the throughput and the number of handovers for given carrier switching decision parameters using different artificial intelligence (AI) mechanisms. The second step is to choose the policy that provides the best trade-off between the number of handovers and the median UE throughput. We utilize real-world traffic data on an industry-grade network simulator to train and test the proposed carrier switching algorithm. We compare four AI models in the first step: random forest, transformer, long-short-term memory (LSTM), and fully connected neural network. Our analysis demonstrates that random forest achieves superior prediction performance for median throughput and average handover frequency due to its robustness against dataset variations. Our results indicate that the number of handovers can be reduced by 40% with a 3% reduction in throughput compared to the handover-unaware baseline.

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Diversity techniques and power control are the main approaches to reduce latency and enhance the reliability of wireless communications at the expense of redundant transmissions and excessive resource usage. Focusing on the application layer reliability key performance indicators (KPIs), we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize these KPIs. Furthermore, to address the scalability issue that emerges in the per-device orchestration problem, we develop a new branching soft actor-critic framework in which a separate branch represents the action space of each industrial device. Our orchestrator enables near real-time control and can be implemented in the edge cloud. We test our solution with a 3GPP-compliant and realistic simulator for factory automation scenarios. Compared to the state-of-the-art, our solution offers significant scalability gains in terms of computational time and memory requirements. Our extensive experiments show significant improvements in our target KPIs, over the state-of-the-art, especially for 5th percentile user availability. To achieve these targets, our framework requires substantially less total energy or spectrum, thanks to our scalable RL solution.

Currently, there is a plethora of low-power wide-area IoT networking solutions available, each targeting a specific niche of use-cases and deployment scenarios. Existing studies on reliability evaluations of IoT solutions rely on the assumption that a single technology is deployed in the service area, or different IoT technologies operate over dedicated spectrum bands. Here, we investigate the reliability performance of IoT communications in coexisting scenarios, where multiple competing radio-access technologies share spectrum resources. Our focus is on solutions exploiting grant-free communications, which are gaining traction due to their potential to lower the energy consumption, and have been adopted in recent IoT technologies like SigFox and LoRa. We first derive an analytical model of the interference, comprising both inter- and intra-technology interference sources. We then leverage the Poisson Cluster Process for modeling distribution of devices in the service area, and derive expressions for the communication reliability, energy consumption, and battery lifetime of IoT devices. Exploiting these expressions, we study the energy-reliability trade-offs and investigate strategies to maintain or improve communication reliability, while minimizing energy consumption in coexisting scenarios by proper adjustment of communications parameters at the device side and provisioning resources at the network side. We verify the analytical results via numerical evaluations, confirming their accuracy and performing optimization in some example networking setups.

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Violation of these requirements may result in faulty behavior of the system and cause significant economic losses. Although wireless communications enable mobility and easy maintenance to industrial networks, it introduces many challenges to high-performance control systems due to interference and harsh environments (e.g., vibrations and many metallic objects). Diversity techniques and power control are powerful approaches to reduce latency and enhance reliability at the expense of excessive resource usage due to redundant transmissions. In this paper, we adopt fundamental metrics from reliability literature to wireless communications and provide critical indicators to measure reliability key performance indicators (KPIs) of cyber-physical systems. Then, we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize our reliability KPIs. Our orchestrator enables near real-time control and can be implemented on the edge cloud. We implement our framework on 3GPP compliant simulator on a factory automation scenario. Our comprehensive experiments show that, compared to the state-of-the-art, our solution can substantially improve the performance, especially for 5th percentile availability.

The main building block of Internet of Things (IoT) ecosystem is providing low-cost scalable connectivity for the radio/compute-constrained devices. This connectivity could be realized over the licensed spectrum like Narrowband-IoT (NBIoT) networks, or over the unlicensed spectrum like NBIoT-Unlicensed, SigFox and LoRa networks. In this paper, performance of IoT communications utilizing the unlicensed band, e.g. the 863–870 MHz in the Europe, in indoor use-cases like smart home, is investigated. More specifically, we focus on two scenarios for channel access management: (i) coordinated access, where the activity patterns of gateways and sensors are coordinated with neighbors, and (ii) uncoordinated access, in which each gateway and its associated nodes work independently from the neighbor ones. We further investigate a distributed coordination scheme in which, devices learn to coordinate their activity patterns leveraging tools from reinforcement learning. Closed-form expressions for capacity of the system, in terms of the number of sustained connections per gateway fulfilling a minimum quality of service (QoS) constraint are derived, and are further evaluated using simulations. Furthermore, delay-reliability and inter network interference-intra network collision performance tradeoffs offered by coordination are figured out. The simulation results highlight the impact of system and traffic parameters on the performance tradeoffs and characterize performance regions in which coordinated scheme outperforms the uncoordinated one, and vice versa. For example, for a packet loss requirement of 1%, the number of connected devices could be doubled by coordination.

Cellular connectivity for drones comes with a wide set of challenges as well as opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve HRRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions for different altitudes/speeds of drones have been presented, which promote a revision of the legacy handover schemes and boundaries of cells in the sky.

Interference channel, in which multiple user pairs communicate over shared resources, is a building block of communications networks. Here, the K -user interference channel (IC) aided by J instantaneous relays (IRs), i.e. relays without delay, is considered. For K IC J R networks, where \(K>2\) and \(J>1\) , the DoF performance and achievable schemes have not been investigated in literature. Here, we devise a novel achievable scheme, called restricted interference alignment (RIA), which restricts the received interference from each source at each destination in a limited sub-space, and then, aligns the restricted interferences. Furthermore, we develop an analytical tool for finding a tight upper bound on DoF of K IC J R networks by analyzing the properness of the interference alignment equations. Moreover, we develop linear beamforming design for sources and relays based on the mean square error (MSE) minimization, as an achievable scheme. The performance evaluation results show that the achievable sum DoF by using the proposed RIA scheme and the MSE-based beamforming design match well with the derived upper bounds. Furthermore, the results confirm that the achieved sum DoF using the RIA scheme outperforms the existing achievable schemes. Motivated by these promising results, we further investigate impact of IRs in providing physical layer security, i.e. achieving secure DoF in interference networks. Specifically, we derive a lower bound on the achievable secure DoF by devising an achievable scheme, called transmission in the null space (TNS). This scheme makes the cross channels rank deficient and enables sources to select their transmit filters in the constructed null spaces. Performance evaluation shows that by leveraging IRs, the secure DoF performance of interference networks could be increased significantly. The proposed analytical framework in this work for rank deficiency-powered DoF analysis is expected to also attract attention from other research areas, e.g. beamforming design for millimeter wave communications.

Traffic prediction plays a vital role in efficient planning and usage of network resources in wireless networks. While traffic prediction in wired networks is an established field, there is a lack of research on the analysis of traffic in cellular networks, especially in a content-blind manner at the user level. Here, we shed light into this problem by designing traffic prediction tools that employ either statistical, rule-based, or deep machine learning methods. First, we present an extensive experimental evaluation of the designed tools over a real traffic dataset. Within this analysis, the impact of different parameters, such as length of prediction, feature set used in analyses, and granularity of data, on accuracy of prediction are investigated. Second, regarding the coupling observed between behavior of traffic and its generating application, we extend our analysis to the blind classification of applications generating the traffic based on the statistics of traffic arrival/departure. The results demonstrate presence of a threshold number of previous observations, beyond which, deep machine learning can outperform linear statistical learning, and before which, statistical learning outperforms deep learning approaches. Further analysis of this threshold value represents a strong coupling between this threshold, the length of future prediction, and the feature set in use. Finally, through a case study, we present how the experienced delay could be decreased by traffic arrival prediction.

Leveraging grant-free radio access for enabling low-power wide-area (LPWA) Internet of Things (IoT) connectivity has attracted lots of attention in recent years. Regarding lack of research on LPWA IoT networks, this work is devoted to reliability modeling, battery-lifetime analysis, and operation-control of such networks. We derive the interplay amongst density of the access points, communication bandwidth, volume of traffic from heterogeneous sources, and quality of service (QoS) in communications. The presented analytical framework comprises modeling of interference from heterogeneous sources with correlated deployment locations and time-frequency asynchronous radio-resource usage patterns. The derived expressions represent the operation regions and rates in which, energy and cost resources of devices and the access network, respectively, could be traded to achieve a given level of QoS in communications. For example, our expressions indicate the expected increase in QoS by increasing number of transmitted replicas, transmit power, density of the access points, and communication bandwidth. Our results further shed light on scalability of such networks and figure out the bounds up to which, scaling resources can compensate the increase in traffic volume and QoS demand. Finally, we present an energy-optimized operation control policy for IoT devices. The simulation results confirm tightness of the derived analytical expressions, and indicate usefulness of them in planning and operation control of IoT networks.

Enabling large-scale energy-efficient Internet-of-things (IoT) connectivity is an essential step towards realization of networked society. While legacy wide-area wireless systems are highly dependent on network-side coordination, the level of consumed energy in signaling, as well as the expected increase in the number of IoT devices, makes such centralized approaches infeasible in future. Here, we address this problem by self-coordination for IoT networks through learning from past communications. To this end, we first study low-complexity distributed learning approaches applicable in IoT communications. Then, we present a learning solution to adapt communication parameters of devices to the environment for maximizing energy efficiency and reliability in data transmissions. Furthermore, leveraging tools from stochastic geometry, we evaluate the performance of proposed distributed learning solution against the centralized coordination. Finally, we analyze the interplay amongst energy efficiency, reliability of communications against noise and interference over data channel, and reliability against adversarial interference over data and feedback channels. The simulation results indicate that compared to the state of the art approaches, both energy efficiency and reliability in IoT communications could be significantly improved using the proposed learning approach. These promising results, which are achieved using lightweight learning, make our solution favorable in many low-cost low-power IoT applications.

Narrowband Internet of Things (NB-IoT) is the latest IoT connectivity solution presented by the 3rd generation partnership project (3GPP). NB-IoT introduces coverage classes and offers a significant link budget improvement by allowing repeated transmissions by nodes that experience high path loss. However, those repetitions necessarily increase the energy consumption and the latency in the whole NB-IoT system. The extent to which the whole system is affected depends on the scheduling of the uplink and downlink channels. We address this question, not treated previously, by developing a tractable model of NB-IoT access protocol operation, comprising message exchanges in random-access, control, and data channels, both in the uplink and downlink. The model is then used to analyze the impact of channel scheduling as well as the interaction of coexisting coverage classes, through derivation of the expected latency and battery lifetime for each coverage class. These results are subsequently employed in investigation of latency-energy tradeoff in NB-IoT channel scheduling as well as determining the optimized operation points. Simulations results show validity of the analysis and confirm that channel scheduling and coexistence of coverage classes significantly affect latency and battery lifetime performance of NB-IoT devices.

IoT networks with grant-free radio access, like SigFox and LoRa, offer low-cost durable communications over unlicensed band. These networks are becoming more and more popular due to the ever-increasing need for ultra durable, in terms of battery lifetime, IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this paper, we study the impact of coexisting competing radio access technologies on the system performance. Considering K technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, and experienced delay as a function of distance to the serving access point. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how introduction of a new coexisting technology may degrade the system performance in terms of success probability and battery lifetime. We further investigate solutions in which this destructive effect could be compensated, e.g., by densifying the network to a certain extent and utilizing joint reception.

Radio access management plays a vital role in delay and energy consumption of connected devices. The radio access in existing cellular networks is unable to efficiently support massive connectivity, due to its signaling overhead. In this paper, we investigate an asynchronous grant-free narrowband data transmission protocol that aims to provide low energy consumption and delay, by relaxing the synchronization/reservation requirement at the cost of sending several packet copies at the transmitter side and more complex signal processing at the receiver side. Specifically, the timing and frequency offsets, as well as sending of multiple replicas of the same packet, are exploited as form of diversities at the receiver-side to trigger successive interference cancellation. The proposed scheme is investigated by deriving closed-form expressions for key performance indicators, including reliability and battery-lifetime. The performance evaluation indicates that the scheme can be tuned to realize long battery lifetime radio access for low-complexity devices. The obtained results indicate existence of traffic load regions, where synchronous access outperforms asynchronous access and vice versa.

Performance tradeoffs in resource provisioning for mixed internet-of-things (IoT) and human-oriented-communications (HoC) services over cellular networks are investigated. First, we present a low-complexity model of cellular connectivity in the uplink direction in which both access reservation and scheduled data transmission procedures are included. This model is employed subsequently in deriving analytical expressions for energy efficiency, spectral efficiency, and experienced delay in data transmission of connected devices as well as energy consumption of base stations. The derived expressions indicate that the choice of uplink resource provisioning strategy introduces tradeoffs between battery lifetime for IoT communications, quality of service (QoS) for HoC, spectral efficiency and energy consumption for the access network. Then, the impacts of system and traffic parameters on the introduced tradeoffs are investigated. Performance analysis illustrates that improper resource provisioning for IoT traffic not only degrades QoS of high-priority services and decreases battery lifetime of IoT devices, but also increases energy consumption of the access network. The presented analytical and simulations results figure out the ways in which spectral/energy efficiency for the access network and QoS for high-priority services could be traded to prolong battery lifetimes of connected devices by compromising on the level of provisioned radio resources.

The Random Access (RA) procedure in existing cellular networks is not capable of functioning properly during high access load conditions. For this purpose, overload control mechanisms are needed. Most proposed mechanisms in the literature offer a tradeoff between access rate and experienced delay. However, when the maximal tolerated delay and the energy spent on retransmissions are tightly bounded, the very high access rate, targeted for 5G systems, cannot be achieved. For these situations, we propose the Delay Estimation based RA (DERA)-scheme that has the potential to meet very stringent reliability requirements, even in high access load conditions. The present work shows that this goal can be achieved only in the cost of limited additional complexity. Furthermore, we also study the optimal switchover point at which the proposed scheme moves from low-load to the high-load phase. The derived tool can also be used along with other proposed RA overload control schemes, e.g. when to invoke access class barring. The performance evaluation results show that the novel DERA scheme can significantly improve the control channels' resource utilization along with the success rate in dense deployment scenarios.

Successful implementation of massive machine-type communications (MTC) service over cellular networks is an important enabler of Internet of things. As the existing cellular infrastructure has been designed and optimized for moderate traffic, the introduction of MTC in cellular networks results in several key challenges, among them, temporal starvation of initial access resources is of paramount importance. Here, we propose a new traffic model for cellular networks serving machine-type subscribers. This model is subsequently used to derive closed-form expressions for the access rate performance of the system. This closed-form expression are subsequently used for RACH resources planning in the time and frequency domains. Given a target access rate requirement, numerical analysis indicates how and when the required RACH resources in the code/frequency domain, i.e. number of preambles, can be traded with the occurrence frequency of RACH slots in the time domain.

Fundamental tradeoffs in green cellular networks with coexistence of machine-oriented and human-oriented traffic are investigated. First, we present a queuing system to model the uplink transmission of a green base station which serves two types of distinct traffic with strict requirements on delay and battery lifetime. Then, the energy-lifetime and energy-delay tradeoffs are introduced, and closed-form expressions for energy consumption of the base station, average experienced delay in data transmission, and expected battery lifetime of machine devices are derived. Furthermore, we extend the derived results to the multi-cell scenario, and investigate the impacts of system and traffic parameters on the energy-lifetime and energy-delay tradeoffs using analytical and numerical results. Numerical results show the impact of energy saving for the access network on the introduced tradeoffs, and figure out the ways in which energy could be saved by compromising on the level of performance.

In this paper, we investigate energy-efficient clustering and medium access control for cellular-based machine-to-machine (M2M) networks to minimize device energy consumption and prolong network battery lifetime. First, we present an accurate energy consumption model that considers both static and dynamic energy consumptions, and utilize this model to derive the network lifetime. Second, we find the cluster size to maximize the network lifetime and develop an energy-efficient cluster-head selection scheme. Furthermore, we find feasible regions where clustering is beneficial in enhancing network lifetime. We further investigate communications protocols for both intra- and inter-cluster communications. While inter-cluster communications use conventional cellular access schemes, we develop an energy-efficient and load-adaptive multiple access scheme, called n-phase carrier sense multiple access with collision avoidance (CSMA/CA), which provides a tunable tradeoff between energy efficiency, delay, and spectral efficiency of the network. The simulation results show that the proposed clustering, cluster-head selection, and communications protocol design outperform the others in energy saving and significantly prolong the lifetimes of both individual nodes and the whole M2M network.

In this paper the scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in LTE networks. To enable efficient scheduling for a massive number of machine-type subscribers, a novel distributed scheme is proposed to let machine nodes form local clusters and communicate with the base-station through the cluster-heads. Then, uplink scheduling and power control in LTE networks are introduced and lifetime-aware solutions are investigated to be used for the communication between cluster-heads and the base-station. Beside the exact solutions, low-complexity suboptimal solutions are presented in this work which can achieve near optimal performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended using the proposed protocols.

In this paper the uplink scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in cellular networks. A lifetime metric, based on the accurate energy consumption model for cellular-based machine devices, is provided and used to formulate the uplink scheduling and power control problems as network-lifetime maximization problems. Then, lifetime-aware uplink scheduling and power control protocols which maximize the overall network-lifetime are investigated based on the different lifetime definitions. Besides the exact solution, a low-complexity suboptimal solution is presented in this work which can achieve near optimal lifetime-performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended under proposed protocols.

This paper proposes a synchronization method with a preamble for IEEE 802.16 and other systems that use Orthogonal Frequency Division Multiplexing (OFDM). The preamble for synchronization is composed of two identical periods, which are polyphase codes, and have an autocorrelation function with one main peak and very small sub peaks. Using this signal repetition and autocorrelation, the correlation for symbol timing detection is calculated with a new metric. The proposed metric uses a filtering structure to find the accurate start point. Proposed method drastically improves the performance of timing synchronization, even with a very low SNR, in both AWGN and multipath fading Rayleigh channels.