Millimeter-wave and terahertz systems rely on beam-forming/combining codebooks to determine the best beam directions during the initial access and data transmission. Existing approaches suffer from large codebook sizes and high beam searching overhead in the presence of mobile devices. To address this issue, we utilize the similarity of the channel in adjacent locations to divide the user trajectory into a set of separate regions and maintain a set of candidate beams for each region in a database. Due to the tradeoff between the number of regions and the signalling overhead, i.e., the greater number of regions results in a higher signal-to-noise ratio (SNR) but also a larger signalling overhead for the database, we propose an optimization framework to find the minimum number of regions based on the trajectory of a mobile device. Using a ray tracing tool, we demonstrate that the proposed method provides high SNR while being more robust to the location information accuracy in comparison to the lookup table baseline and fixed size region baseline.

Base stations (BSs) are the most energy-consuming segment of mobile networks. To reduce the energy consumption of BSs, inactive components of BSs, with a certain activation/deactivation time, can sleep. In this study, we model the problem of BS energy saving utilizing multiple sleep modes as a sequential MDP and propose an online traffic-aware deep Q-learning approach to maximize the long-term energy saving. However, there is a risk that BS is not sleeping at the right time and incurs delays to the users. To tackle this issue, we propose to use a digital twin model to encapsulate the dynamics underlying the investigated system and estimate the risk of decision-making (RDM) in advance. We define a novel metric to quantify RDM and predict the performance degradation. Mobile operators can compare the RDM with a tolerable threshold to decide on deactivating SMs, re-training, or activating SMs. We trained an agent using real traffic, obtained from an operator's BS in Stockholm. The data-set contains data rate information with very coarse-grained time granularity. Thus, we propose a scheme to generate a new data-set using the real network data-set which 1) has finer-grained time granularity and 2) considers the bursty behavior of traffic data. Simulation results show that using proposed methods, considerable energy saving is obtained at cost of negligible number of delayed users. Moreover, the proposed digital twin model can predict the performance of the DQN proactively in terms of RDM hence preventing the performance degradation in the network in anomalous situations.

In this paper, we develop an algorithm for jointhandover and beam tracking in millimeter-wave (mmWave)networks. The aim is to provide a reliable connection in terms ofthe achieved throughput along the trajectory of the mobile userwhile preventing frequent handovers. We model the associationproblem as an optimization problem and propose a reinforcementlearning-based solution. Our approach learns whether and whenbeam tracking and handover should be performed and choosesthe target base stations. In the case of beam tracking, wepropose a tracking algorithm based on measuring a small spatialneighbourhood of the optimal beams in the previous time slot.Simulation results in an outdoor environment show the superiorperformance of our proposed solution in achievable throughputand the number of handovers needed in comparison to a multiconnectivity baseline and a learning-based handover baseline.

Network slicing is a promising technology for realizing the vision of supporting a wide range of services with diverse and heterogeneous service requirements. With network slicing, the network is partitioned into multiple individual dedicated networks tailored and customized for specific services. However, this causes extra energy consumption to reserve resources for each slice. On the other hand, minimizing the energy consumption in radio access network (RAN) may result in increasing the energy consumption in the cloud and the fronthaul due to higher required processing and data transport. Therefore, the energy should be evaluated from an end-to-end perspective. In this study, we address the problem of minimizing the end-to-end energy consumption of a network with network slicing by jointly reserving communication and computation resources among slices. First, we propose an end-to-end delay and energy model for each slice. We take into account the delays and energy consumption of the radio site, midhaul/fronthaul transport, and the cloud site in an Ethernet-based cloud RAN (C-RAN). Then, we formulate a non-convex optimization problem to minimize the total energy consumption of the network by jointly allocating transmission bandwidth and processing resources in the digital unit pool of the cloud, respectively, to each slice. The constraints of the optimization problem are the total delay requirement of each slice, the maximum allowable bandwidth at each radio unit, the maximum rate limitation of the Ethernet links, and the total processing limit of the cloud. To solve the problem optimally, we transform it into a convex quadratic programming problem. The simulation results show that end-to-end network slicing can decrease the total energy consumption of the network compared to only RAN slicing. We also investigate the impact of the 5G numerology on the allocated resources to each slice and the total energy consumption. We show that using mixed numerology depending on the slice type, we can interplay between delay and energy consumption for each slice.

Due to the large bandwidth available, millimeter-Wave (mmWave) bands are considered a viable opportunity to significantly increase the data rate in cellular and wireless networks. Nevertheless, the need for beamforming and directional communication between the transmitter and the receiver increases the complexity of the channel estimation and link establishment phase. Location-aided beamforming approaches have the potential to enable fast link establishment in mmWave networks. However, these are often very sensitive to location errors. In this work, we propose a beamforming algorithm based on tracking spatial correlation of the available strong paths between the transmitter and the receiver. We show that our method is robust to uncertainty in location information, i.e., location error and can provide a reliable connection to a moving user along a trajectory. The numerical results show that our approach outperforms benchmarks on various levels of error in the location information accuracy. The gain is more prominent in high location error scenarios.

Conventional Internet of things (IoT) devices are powered by batteries. However, batteries pose a risk of e-waste and chemical leakage to the environment. An alternative way to power remote IoT devices is to harvest ambient RF energy. It is especially beneficial when battery replacement is costly and could enable large-scale IoT deployments at net-zero energy cost. In this paper, we assume that the IoT devices periodically harvest energy from multiple surrounding base stations (BSs) and use the harvested energy to take sensor measurements and transmit related data packets. We propose an approximate method to analyze the feasibility of this approach in terms of which measurement rates can be supported. To this end, we assume a Poisson point process for the locations of the BSs. We derive mathematical expressions for the coverage probability (i.e., the probability that an IoT device harvests enough energy to operate at a given measurement rate) and the required BS site density in the presence of channel uncertainties, blockage, and harvesting nonlinearities. We derive the parameters of a Gamma distribution to approximate the distribution of the harvested power. For a coverage probability of 0.5, we derive a simplified approximate expression for the required site density that closely describes the one obtained empirically.

Wireless power transfer (WPT) is an alternative technology to conventional batteries for powering Internet of things (IoT) devices. WPT is especially beneficial in situations when battery replacement is infeasible or expensive. It can also reduce battery-related e-waste. In this paper, we analyze the limits of adopting WPT technology for remote powering of IoT devices. We assume that an IoT device periodically harvests energy from a base station (BS) and transmits a data packet related to the sensor measurement under shadow fading channel conditions. Our goal is to characterize the epsilon-coverage range, where epsilon is the probability of the coverage. Our analysis shows a tradeoff between the coverage range and the rate of sensor measurements, where the maximal epsilon-coverage range is achieved as the sensor measurement rate approaches zero. We demonstrate that the weighted sum of the sleep power consumption and the harvesting sensitivity power of an IoT device limits the maximal e-coverage range. Beyond that range, the IoT device cannot harvest enough energy to operate. The desired rate of the sensor measurements also significantly impacts the epsilon-coverage range. Our results suggest that for an IoT device designed using current technology, the maximal 0.95-coverage range is in the order of 120 m. When high measurement rates are required, the coverage range drops to 50-100 m. Compared to battery-powered IoT devices, WPT is well-suited for medium-range applications plus when battery replacement is costly.

In cellular and cellular-IoT systems, random access has the utmost importance as it is the gatekeeper that assures conflict-free access to radio resources. The main drawback of the legacy protocols lies at the collision resolution procedure as the base station (BS) is blind to the collision multiplicity and collided devices rely on the BSs for contention resolution. While contention resolution over the random access channel (RACH) is mature in the literature, most studies are following optimistic assumptions in which the full collision impact at the receiver is neglected, and a simplified model of partially overlapping packets is considered. This work is devoted to the investigation of a more realistic model of collision in cellular networks with the aim to investigate challenges and potential solutions in preamble detection in collision scenarios. The simulation results show that besides previously well-known challenges in dealing with collisions, the occurrence of collisions significantly increases the probability of false preamble detection.