This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose a multi-agent deep reinforcement learning (MADRL) algorithm that enables each access point (AP) to autonomously control antenna reconfiguration and advanced sleep mode (ASM) selection. After the training process, the proposed framework operates in a fully distributed manner, eliminating the need for centralized control and allowing each AP to dynamically adjust to real-time traffic fluctuations. Simulation results show that the proposed algorithm reduces power consumption (PC) by 56.23% compared to systems without any energy-saving scheme and by 30.12% relative to a non-learning mechanism that only utilizes the lightest sleep mode, with only a slight increase in drop ratio. Moreover, compared to the widely used deep Q-network (DQN) algorithm, it achieves a similar PC level but with a significantly lower drop ratio.

In the design of a metasurface-assisted system for indoor environments, it is essential to take into account not only the performance gains and coverage extension provided by the metasurface but also the operating costs brought by its reconfigurability, such as powering and cabling. These costs can present challenges, particularly in indoor dense spaces (IDSs). A self-sustainable reconfigurable intelligent surface (ssRIS), which retains reconfigurability unlike a static metasurface (SMS), achieves a lower operating cost than a reconfigurable intelligent surface (RIS) by being self-sustainable through power harvesting. In this paper, in order to find a better trade-off between metasurface gain, coverage, and operating cost, the design and performance of an ssRIS-assisted indoor mmWave communication system are investigated. We simplify the use of the ssRIS by considering a preset-based element splitting scheme for maintaining self-sustainability and the formation of coverage groups by associating ssRISs with the closest user equipments (UEs). We propose a two-stage iterative algorithm to maximize the minimum data rate by jointly deciding the association between the UEs and the ssRISs, the phase shifts of the ssRISs, and allocating time resources for each UE. The non-convex optimization problem is tackled using the feasible point pursuit successive convex approximation method. To understand the best scenario for using ssRIS, the resulting performance is compared with that achieved with RIS and SMS. Our numerical results indicate that ssRISs are best utilized in a small environment where self-sustainability is easier to achieve when the budget for operating costs is tight.

Unmanned aerial vehicles (UAVs) have emerged as an important tool for communication research in recent years. However, they introduce new challenges for modern urban management. Conventional UAV detection methods, which rely on either vision-based systems or dedicated sensor networks, incur significant deployment complexity and high maintenance costs. To address these challenges, this paper proposes a communication signal-based UAV detection system. Given the poor signal quality passively scattered by the UAV, we employ signal processing techniques to enhance the feature extraction, while implementing customized modifications to the model architecture to accommodate the characteristics of complex-valued inputs. To validate our approach, we conducted comprehensive tests using a software-defined radio transceiver system constructed by USRP-2974 devices. Experimental results demonstrate that the proposed method outperforms other methods and achieves a detection accuracy surpassing 99% in real-world environments.

Reconfigurable intelligent surfaces (RISs) can greatly improve the signal quality of future communication systems by reflecting transmitted signals toward the receiver. However, even when the base station (BS) has perfect channel knowledge and can compute the optimal RIS phase-shift configuration, implementing this configuration requires feedback signaling over a control channel from the BS to the RIS. This feedback must be kept minimal, as it is transmitted wirelessly every time the channel changes. In this paper, we examine how the feedback load, measured in bits, affects the performance of an RIS-aided system. Specifically, we investigate the trade-offs between codebook-based and element-wise feedback schemes, and how these influence the signal-to-noise ratio (SNR). We propose a novel quantization codebook tailored for line-of-sight (LoS) that guarantees a minimal SNR loss using a number of feedback bits that scale logarithmically with the number of RIS elements. We demonstrate the codebook's usefulness over Rician fading channels and how to extend it to handle a non-zero static path. Numerical simulations and analytical analysis are performed to quantify the performance degradation that results from a reduced feedback load, shedding light on how efficiently RIS configurations can be fed back in practical systems.

In this paper, we study the task-offloading problem for Internet of Remote Things (IoRT) devices. In the considered scenario, the Low Earth Orbit (LEO) satellite nearest to the devices collects their generated tasks and makes offloading decisions for each task, including selecting the offloading destination and allocating computational resources. Moreover, we consider the motion of satellites within a constellation and assume that a random fraction of computing resources on each edge server is occupied during each time slot. Our objective is to minimize the total longterm latency for all IoRT devices. To address this problem, we propose a Proximal Policy Optimization (PPO)-based algorithm to learn the near-optimal policy. The simulation results demonstrate that our proposed algorithm reduces latency by an average of 21.49% and 50.79% compared to the two benchmark algorithms, perspectively.

In recent years, drones, or unmanned aerial vehicles (UAVs), have become widely used across various applications, from aerial photography and videography to the delivery of packages and medical supplies. However, their increasing presence has raised concerns about physical safety and privacy, highlighting the need for effective drone detection and monitoring solutions. To address this, we utilize the fact that most commercial drones use the Zadoff-Chu (ZC) sequence as the synchronization sequence in their communications, making it a useful feature for detection. Yet, detecting the ZC sequence blindly is challenging, as the transmitter's frequency is unknown to the receiver. While existing studies on ZC sequence detection with different frequency offsets focus largely on Long Term Evolution (LTE) scenarios, the ZC sequence structure and length used by drones differ, leading to unique detection challenges. In this paper, we analyze the autocorrelation properties of the specific ZC sequence used by drones under various center frequency offsets. We further propose a blind detection and identification algorithm that can detect and identify multiple drones utilizing ZC sequences in their video transmission protocols and autocorrelation properties. We study the performance of the proposed algorithm with extensive simulations and field tests. Even in low signal-to-noise ratio (SNR) conditions, with an SNR as low as -14 dB, our algorithm achieves a detection rate exceeding 99 %.

This paper investigates multi-target detection in an integrated sensing and communication (ISAC) system within a cell-free massive MIMO (CF-mMIMO) framework. We adopt a user-centric approach for communication user equipments (UEs) and a distributed sensing approach for multi-target detection. A heuristic access point (AP) mode selection algorithm and a channel-aware distributed sensing scheme are proposed, where local measurements at receive APs (RX-APs) are weighted based on the received signal’s signal-to-interference ratio (SIR). A maximum a posteriori ratio test (MAPRT) detector is applied under two awareness levels at RX-APs. To balance the communication-sensing trade-off, we develop a power allocation algorithm to jointly maximize the minimum detection probability and communication signal-to-interference-plus-noise ratio (SINR) while satisfying power constraints. The proposed scheme outperforms non-weighted methods. Adding test statistics from more RX-APs can degrade sensing performance due to weaker channels, but this effect can be mitigated by optimizing the weighting exponent. Additionally, assigning more sensing RX-APs to a sensing area results in approximately 10dB loss in minimum communication SINR due to limited communication resources.

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.

The integration of unmanned aerial vehicles (UAVs) into cellular networks presents significant mobility management challenges, primarily due to frequent handovers caused by probabilistic line-of-sight conditions with multiple ground base stations (BSs). To tackle these challenges, reinforcement learning (RL)-based methods, particularly deep Q-networks (DQN), have been employed to optimize handover decisions dynamically. However, a major drawback of these learning-based approaches is their black box nature, which limits interpretability in the decision making process. This paper introduces an explainable AI (XAI) framework that incorporates Shapley Additive Explanations (SHAP) to provide deeper insights into how various state parameters influence handover decisions in a DQN-based mobility management system. By quantifying the impact of key features such as reference signal received power (RSRP), reference signal received quality (RSRQ), buffer status, and UAV position, our approach enhances the interpretability and reliability of RL-based handover solutions. To validate and compare our framework, we utilize real-world network performance data collected from UAV flight trials. Simulation results show that our method provides intuitive explanations for policy decisions, effectively bridging the gap between AI-driven models and human decision-makers.

Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we propose a cell-free massive MIMO (multiple-input multiple-output)-ISAC framework to detect unauthorized drones while simultaneously ensuring communication requirements. We develop a detector to identify passive aerial targets by analyzing signals from distributed access points (APs). In addition to the precision of the sensing, timeliness of the sensing information is also crucial due to the risk of drones leaving the area before the sensing procedure is finished. We introduce the age of sensing (AoS) and sensing coverage as our sensing performance metrics and propose a joint sensing blocklength and power optimization algorithm to minimize AoS and maximize sensing coverage while meeting communication requirements. Moreover, we propose an adaptive weight selection algorithm based on concave-convex procedure to balance the inherent tradeoff between AoS and sensing coverage. Our numerical results show that increasing the communication requirements would significantly reduce both the sensing coverage and the timeliness of the sensing. Furthermore, the proposed adaptive weight selection algorithm can provide high sensing coverage and reduce the AoS by 45% compared to the fixed weights, demonstrating efficient utilization of both power and sensing blocklength,

Massive multiple-input multiple-output (mMIMO) has been the core of 5G due to its ability to improve spectral efficiency and spatial multiplexing significantly; however, cell-edge users still experience performance degradation due to intercell interference and uneven signal distribution. While cell-free mMIMO (cfmMIMO) addresses this issue by providing uniform coverage through distributed antennas, it requires significantly more deployment cost due to the fronthaul and tight synchronization requirements. Alternatively, repeater-assisted massive MIMO (RA-MIMO) has recently been proposed to extend the coverage of cellular mMIMO by densely deploying low-cost single-antenna repeaters capable of amplifying and forwarding signals. In this work, we investigate amplification control for the repeaters for two different goals: (i) providing a fair performance among users, and (ii) reducing the extra energy consumption by the deployed repeaters. We propose a max-min amplification control algorithm using the convex-concave procedure for fairness and a joint sleep mode and amplification control algorithm for energy efficiency, comparing long- and short-term strategies. Numerical results show that RA-MIMO, with maximum amplification, improves signal-to-interference-plus-noise ratio (SINR) by over 20 dB compared to mMIMO and performs within 1 dB of cfmMIMO when deploying the same number of repeaters as access points in cfmMIMO. Additionally, our majority-rule-based long-term sleep mechanism reduces repeater power consumption by 70 % while maintaining less than 1 % spectral efficiency outage.

Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimization problem for a mmWave cell-free massive MIMO network considering indoor environments. The objective is to minimize the number of deployed access points (APs) for a given environment, bandwidth, AP cooperation, and precoding scheme while guaranteeing the rate requirements of the user equipments (UEs). Considering coherent joint transmission (C-JT) and non-coherent joint transmission (NC-JT), we solve the problem of AP placement, UE-AP association, and power allocation among the UEs and resource blocks jointly. For numerical analysis, we model a mid-sized airplane cabin in ray-tracing as an exemplary case for IDS. Results demonstrate that a minimum data rate of 1Gbps can be guaranteed with less than 10 APs with C-JT. From a holistic network design perspective, we analyze the trade-off between the required fronthaul capacity and the processing capacity per AP, under different network functional split options. We observe an above 600Gbps fronthaul rate requirement, once all network operations are centralized, which can be reduced to 200Gbps under physical layer functional splits.

Low Earth orbit (LEO) Mega-constellations have emerged as a transformative approach to realize enhanced system capacity and improved coverage to satisfy the ever-increasing global demand for data services. Subsequently, the high density of satellites in a confined orbital region poses challenges, including potential interference among neighboring satellites. Further, it is vital to adequately address the impacts of safety distances in satellite communication systems on ensuring proper operation, collision avoidance, and interference management. Inspired by these observations, this work proposes a novel analysis tool, the Poisson arc hardcore point process (PAHPP), by extending the traditional Poisson line hardcore point process to characterize the unique orbiting properties of the satellites in LEO mega-constellations, accounting for factors such as the orbit, the satellite density, and spatial distribution. Specifically, this paper presents the PAHPP by enforcing a minimum separation between satellites operating in the same circular orbit to reflect the practical LEO mega-constellations. The imposed minimum inter-satellite separation in the proposed PAHPP model has also been applied to multi-orbit multi-satellite communication cases. Moreover, the discretization approximation technique is employed to analyze system performance, focusing on serving distance and outage probability. Numerical results provide valuable insights and conclusions for uncovering and recognizing LEO mega-constellations.

In this paper, we examine the energy efficiency (EE) of a base station (BS) with multiple antennas. We use a state-of-the-art power consumption model, taking into account the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number of antennas as the optimization variables. We provide novel closed-form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna. We present a novel algorithm that jointly optimizes these variables to achieve maximum EE, while fulfilling constraints on the variable ranges. We also discover a new relationship between the radiated power and the passive transceiver power consumption. We provide analytical insight into whether using maximum power or bandwidth is optimal and how many antennas a BS should utilize.

This paper studies integrated sensing and communication (ISAC) in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with multi-static sensing and ultra-reliable low-latency communication (URLLC) users. We propose a successive convex approximation-based power allocation algorithm that maximizes energy efficiency while satisfying the sensing and URLLC requirements. In addition, we provide a new definition for network availability, which accounts for both sensing and URLLC requirements. The impact of blocklength, sensing requirement, and required reliability as a function of decoding error probability on network availability and energy ef-ficiency is investigated. The proposed power allocation algorithm is compared to a communication-centric approach where only the URLLC requirement is considered. It is shown that the URLLC-only approach is incapable of meeting sensing requirements, while the proposed ISAC algorithm fulfills both sensing and URLLC requirements, albeit with an associated increase in energy consumption. This increment can be reduced up to 75% by utilizing additional symbols for sensing. It is also demonstrated that larger blocklengths enhance network availability and offer greater robustness against stringent reliability requirements.

In this paper, we investigate how metasurfaces can be deployed to deliver high data rates in a millimeter-wave (mmWave) indoor dense space with many blocking objects. These surfaces can either be static metasurfaces (SMSs) that reflect with fixed phase-shifts or reconfigurable intelligent surfaces (RISs) that can reconfigure their phase-shifts to the currently served user. The latter comes with an increased power, cabling, and signaling cost. To see how reconfigurability affects the network performance, we propose an iterative algorithm based on the feasible point pursuit successive convex approximation method. We jointly optimize the types and phase-shifts of the surfaces and the time portion allocated to each user equipment to maximize the minimum data rate achieved by the network. Our numerical results demonstrate that the minimum data rate improves as more RISs are introduced but the gain diminishes after some point. Therefore, introducing more reconfigurability is not always necessary. Another result shows that to reach the same data rate achieved by using 22 SMSs, at least 18 RISs are needed. This suggests that when it is costly to deploy many RISs, as an inexpensive alternative solution, one can reach the same data rate just by densely deploying more SMSs.

For the energy-efficient deployment of cell-free massive MIMO functionality in a practical wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is essential. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture. We compare two different functional split options and different resource orchestration mechanisms. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: 1) “radio-only” where radio resources are optimized independently from the cloud; and 2) “local cloud coordination” where orchestration is only allowed among a local cluster of radio units. We develop several algorithms to solve the end-to-end power minimization and sum spectral efficiency maximization problems. The numerical results demonstrate that end-to-end resource allocation with fully virtualized fronthaul and cloud resources provides a substantial additional power saving than the other resource orchestration schemes.

This paper addresses the problem of providing internet connectivity to aircraft flying above the ocean without using satellite connectivity given the lack of ground network infrastructure in the relevant oceanic areas. Is it possible to guarantee a minimum flow rate to each aircraft flying over an ocean by forming an aeronautical ad hoc network and connecting that network to internet via a set of limited number of ground base stations at the coast as anchor points? We formulated the problem as mixed-integer-linear programming (MILP) to maximize the number of aircraft with flow data rate above a certain threshold. Since this multi-commodity flow problem is at least NP-complete, we propose a two-phase heuristic algorithm to efficiently form topology and assign flows to each aircraft by maximizing the minimum flow. The performance of the heuristic algorithm is evaluated over the North Atlantic Corridor, heuristic performs only 8% less than the optimal result with low densities. In high network densities, the connectivity percentage changes from 70% to 40% under 75 Mbps data rate threshold. Furthermore, the connectivity percentage is investigated for different network parameters such as altitude and compared to upper and lower bounds and a baseline algorithm.

Mobility management for terrestrial users is mostly concerned with avoiding radio link failure for the edge users where the cell boundaries are defined. The problem becomes interesting for an aerial user experiencing fragmented coverage in the sky and line-of-sight conditions with multiple ground base stations (BSs). For aerial users, mobility management is not only concerned with avoiding link failures but also avoiding unnecessary handovers while maintaining extended service availability, especially in up-link communication. The line of sight conditions from an Unmanned Aerial Vehicle (UAV) to multiple neighboring BSs make it more prone to frequent handovers, leading to control packet overheads and delays in the communication service. Depending on the use cases, UAVs require a certain level of service availability, which makes their mobility management a critical task. The current mobility robustness optimization (MRO) procedure that adaptively manages handover parameters to avoid unnecessary handovers is optimized only for terrestrial users. It needs to be updated to capture the unique mobility challenges of aerial users. In this work, we propose two approaches to accomplish this: 1) A model based service availability-aware MRO where handover control parameters, such as handover margin and time to trigger are tuned to maintain high service availability with a minimum number of handovers, and, 2) A deep Q-network based model free approach for decreasing unnecessary handovers while maintaining high service availability. Simulation results demonstrate that both the proposed algorithms converge promptly and increase the service availability by more than 40% while the number of handovers is reduced by more than 50% as compared to traditional approaches.

Aerial vehicles (AVs) such as electric vertical take-off and landing (eVTOL) aircraft make aerial passenger transportation a reality in urban environments. However, their communication connectivity is still under research to realize their safe and full-scale operation. This paper envisages a multi-connectivity (MC) enabled aerial network to provide ubiquitous and reliable service to AVs. Vertical heterogeneous networks with direct air-to-ground (DA2G) and air-to-air (A2A) communication, high altitude platforms (HAPs), and low Earth orbit (LEO) satellites are considered. We evaluate the end-to-end (E2E) multi-hop reliability and network availability of the downlink of AVs for remote piloting scenarios, and control/telemetry traffic. Command and control (C2) connectivity service requires ultra-reliable and low-latency communication (URLLC), therefore we analyse E2E reliability and latency under the finite blocklength (FBL) regime. We explore how different MC options satisfy the demanding E2E connectivity requirements taking into account antenna radiation patterns and unreliable backhaul links. Since providing seamless connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antennas, we use coordinated multi-point (CoMP) among ground BSs to alleviate the inter-cell interference. Furthermore, we solve an optimization problem to select the best MC path under the quality of service (QoS) constraints. We maximize spectral efficiency (SE) to specify the optimum MC path with the minimum number of required links. Based on the simulation results, we find out that even with very efficient interference mitigation, MC is the key enabler for safe remote piloting operations.

This paper studies an integrated sensing and communication (ISAC) system within a centralized cell-free massive MIMO (multiple-input multiple-output) network for target detection. ISAC transmit access points serve the user equipments in the downlink and optionally steer a beam toward the target in a multi-static sensing framework. A maximum a posteriori ratio test detector is developed for target detection in the presence of clutter, so-called target-free signals. Additionally, sensing spectral efficiency (SE) is introduced as a key metric, capturing the impact of resource utilization in ISAC. A power allocation algorithm is proposed to maximize the sensing signal-to-interference-plus-noise ratio while ensuring minimum communication requirements. Two ISAC configurations are studied: utilizing existing communication beams for sensing and using additional sensing beams. The proposed algorithm’s efficiency is investigated in realistic and idealistic scenarios, corresponding to the presence and absence of the target-free channels, respectively. Despite performance degradation in the presence of target-free channels, the proposed algorithm outperforms the interference-unaware benchmark, leveraging clutter statistics. Comparisons with a fully communication-centric algorithm reveal superior performance in both cluttered and clutter-free environments. The incorporation of an extra sensing beam enhances detection performance for lower radar cross-section variances. Moreover, the results demonstrate the effectiveness of the integrated operation of sensing and communication compared to an orthogonal resource-sharing approach.

This paper examines how to minimize the energy consumption of a user equipment (UE) when transmitting short data payloads. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS), which requires additional pilot signals to be configured, to improve the channel conditions. The challenge is that the pilot signals increase the energy consumption and must be balanced against energy savings during data transmission. We derive a formula for the energy consumption, including both pilot and data transmission powers and the effects of imperfect channel state information and discrete phase-shifts. To shorten the pilot length, we propose dividing the RIS into subarrays of multiple elements using the same reflection coefficient. The pilot power and subarray size are tuned to the payload length to minimize the energy consumption. Analytical results show that there exists a unique energy-minimizing solution. For small payloads and when the direct path loss between the BS and UE is weak compared to the path loss via the RIS, the solution is using subarrays with many elements and low pilot power and vice versa. The optimal percentage of energy spent on pilot signaling is in the order of 10-40%.

Aerial base stations (ABSs) have emerged as a promising solution to meet the high traffic demands of future wireless networks. Nevertheless, their practical implementation requires efficient utilization of limited payload and onboard energy. Understanding the power consumption streams, such as mechanical and communication power, and their relationship to the payload is crucial for analyzing its feasibility. Specifically, we focus on rotary-wing drones (RWDs), fixed-wing drones (FWDs), and high-altitude platforms (HAPs), analyzing their energy consumption models and key performance metrics such as power consumption, energy harvested-to-consumption ratio, and service time with varying wingspans, battery capacities, and regions. Our findings indicate that FWDs have longer service times and HAPs have energy harvested-to-consumption ratios greater than one, indicating theoretically infinite service time, especially when deployed in near-equator regions or have a large wingspan. Additionally, we investigate the case study of RWD-BS deployment, assessing aerial network dimensioning aspects such as ABS coverage radius based on altitude, environment, and frequency of operation. Our findings provide valuable insights for researchers and telecom operators, facilitating effective cost planning by determining the number of ABSs and backup batteries required for uninterrupted operations.

In this work, we consider the deployment of reconfigurable intelligent surfaces (RISs) to extend the coverage of a millimeter-wave (mmWave) network in indoor dense spaces. We first integrate RIS into ray-tracing simulations to realistically capture the propagation characteristics, then formulate a non-convex optimization problem that minimizes the number of RISs under rate constraints. We propose a feasible point pursuit and successive convex approximation-based algorithm, which solves the problem by jointly selecting the RIS locations, optimizing the RIS phase-shifts, and allocating time resources to user equipments (UEs). The numerical results demonstrate substantial coverage extension by using at least four RISs, and a data rate of 130 Mbit/s is guaranteed for UEs in the considered area of an airplane cabin.

In this paper, we formulate and solve the optimization problem for joint access point placement and resource allocation for indoor mmWave communications with static users, with a particular focus on airplanes. The proposed scheme obtains the required number of access points (APs) and their locations, for a given data rate threshold and given radio resources such as bandwidth, antenna numbers, and AP co-operation. We first build an airplane cabin environment in a ray-tracing tool to realistically capture the propagation effects. Then, we cast optimal deployment problems considering the performance of different AP cooperation schemes, namely coordinated scheduling (CS), non-coherent joint transmission (NC-JT), and coherent joint transmission (C-JT). The results indicate that full cooperation among the APs with C-JT requires fewer APs, especially under high data rate requirements. Comparing the network deployments in the mmWave and sub-6GHz bands, we observe 9 times higher data rates in mmWave although more APs are required.

The ubiquitous cellular network is a strong candidate for providing UAVs' wireless connectivity. Due to the maneuverability advantage and higher altitude, UAVs could have line-of-sight (LoS) connectivity with more base station (BS) candidates than terrestrial users. However, the LoS connectivity could also enhance the propagation of up-link interference caused by UAVs over co-existing terrestrial users. In addition, UAVs would perform more handovers than terrestrial users when moving due to the extensive overlap in the coverage areas of many BS candidates. The solution is to bypass the overlapping coverage areas by designing the UAVs' trajectory and to reduce interference by optimizing radio resource allocation through handover management. This paper studies the joint optimization of a UAV's trajectory design and handover management to minimize the weighted sum of three key performance indicators (KPIs): delay, up-link interference, and handover numbers. A dueling double deep Q-network (D3QN) based reinforcement learning algorithm is proposed to solve the optimization problem. Results show that the proposed approach can reduce the handover numbers by 90% and the interference by 18% at the cost of a small increment in transmission delay when compared with the benchmark scheme, which controls the UAV to move along the shortest path and perform handover based on received signal strength. Finally, we verify the advantage of introducing trajectory design, which can reduce the interference by 29% and eliminate the handover numbers by 33% when compared to the D3QN-based policy without trajectory design.

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.

This paper studies a joint communication and sensing (JCAS) system with downlink communication and multi-static sensing for single-target detection in a cloud radio access network architecture. A centralized operation of cell-free massive MIMO is considered for communication and sensing purposes. The JCAS transmit access points (APs) jointly serve the user equipments (UEs) and optionally steer a beam towards the target. A maximum a posteriori ratio test detector is derived to detect the target using signals received at distributed APs. We propose a power allocation algorithm to maximize the sensing signal-to-noise ratio under the condition that a minimal signal-to-interference-plus-noise ratio value for each UE is guaranteed. Nu-merical results show that, compared to the fully communication-centric power allocation, the detection probability under a certain false alarm probability can be increased significantly by the proposed algorithm for both JCAS setups: i) using additional sensing symbols or ii) using only existing communication symbols.

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz).

In this paper, we examine the energy consumption of a user equipment (UE) when it transmits a finite-sized data packet. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, if additional pilot signals are transmitted to configure the RIS. We derive a formula for the energy consumption taking both the pilot and data transmission powers into account. By dividing the RIS into subarrays consisting of multiple RIS elements using the same reflection coefficient, the pilot overhead can be tuned to minimize the energy consumption while maintaining parts of the aperture gain. Our analytical results show that there exists an energy-minimizing subarray size. For small data blocks and when the channel conditions between the BS and UE are favorable compared to the path to the RIS, the energy consumption is minimized using large subarrays. When the channel conditions to the RIS are better and the data blocks are large, it is preferable to use fewer elements per subarray and potentially configure the elements individually.

In this paper, the indoor dense space (IDS) channel at 28 GHz is characterized through extensive Ray-Tracing (RT) simulations. We consider IDS as a specific type of indoor environment with confined geometry and packed with humans, such as aircraft cabins and train wagons. Based on RT simulations, we characterize path loss, shadow fading, root-mean-square delay spread, Rician K-factor, azimuth/elevation angular spread of arrival/departure considering different RT simulation scenarios of the fuselage geometry, material, and human presence. While the large-scale fading parameters are similar to the state-of-the-art channel models, the small-scale fading parameters demonstrate richer multipath scattering in IDS, resulting in poorer bit error rate performance in comparison to the 3GPP indoor channel model.

Network traffic prediction in cellular networks improves reliability and efficiency of network resource use via proactive network management schemes. To this end, future traffic arrivals are anticipated via machine learning (ML)-based network traffic predictions based on historical network traffic data. Current literature on ML-based network traffic predictions employs warm-up periods, which are the required duration traffic flows are observed to make meaningful predictions. However, most flows are shorter than the warm-up period. This paper proposes a residual neural network (ResNet) architecture for individual network flow predictions, based on a deep-learning approach that removes the required warm-up period seen in other proposed methods. The ResNet architecture demonstrates the ability to accurately predict the magnitude of packet count, size, and duration of flows using only the information available at the arrival of the first packet such as IP addresses and utilized transport-layer protocols. The results indicate that the proposed method is able to predict the order of magnitude of individual flow characteristics with over 80% accuracy, outperforming traditional ML methods such as linear regression and decision trees.

Feasibility of using unlicensed spectrum for ultra reliable low latency communications (URLLC) is still a question for beyond 5G wireless networks. Low latency access to the channel and efficiently sharing spectrum among the multiple users are the main requirements for exploiting unlicensed spectrum for URLLC. Listen before talk and back-off procedures implemented to avoid the collisions in channel access hinder the low latency communication. In this paper, we propose a novel low-latency medium access control (MAC) scheme based on the collision resolution for a pairwise random wireless network. We use geometric sequence decomposition for collision resolution among the competing users. This enables the system to tackle collisions and thus removing the need for carrier sensing and back-off procedures. This saves time in obtaining access to the channel and improves the efficiency of the system. We implement our approach in the synchronized time slotted system and show that it yields significant improvement over existing MAC schemes.

Radio resource adaptation (RRA) is an effective strategy to reduce the energy consumption (EC) of a base station (BS) under variable input traffic demand. By combining RRA with advanced sleep modes (ASMs), one could achieve relatively higher energy savings (ES) during the low traffic hours of the day while managing to meet the quality of service (QoS) requirements of the user equipments (UEs). However, identifying appropriate resources for a certain period is challenging as different resources (i.e., the bandwidth and the antenna array size) have a varying impact on the instantaneous power consumption (PC) and activity of the BS. Various works have looked into the potential of RRA and ASMs in reducing the EC of a BS when implemented independently. In this work, we combine RRA with ASMs and propose a dynamic Q-learning algorithm that adapts a BS’s resources according to the traffic demand. The algorithm also takes into account the sleep modes (SMs) that the BS can switch to during the idle periods. Through simulations, we show the convergence of our algorithm and the impact of combining RRA with ASMs on the overall ES as we observe up to 16% additional savings in a super-dense urban (SDU) deployment scenario by combining these techniques as compared to the baseline scenario using only ASMs.

Low Latency Low Loss Scalable Throughput (L4S) is a technology intended to reduce queue delay problems, ensuring low latency to Internet Protocol flows with a high throughput performance. To reach this goal, it relies on Explicit Congestion Notification (ECN), a mechanism that marks packets to signal congestion in the network avoiding packets to be dropped. The congestion signals are managed at the sender and receiver sides thanks to scalable congestion control algorithms. In this paper, the challenges to implement L4S in a 5G network are analyzed. Using a proprietary state-of-the-art network simulator, the L4S marking strategy has been implemented at the Packed Data Convergence Protocol layer. To evaluate the benefits of the implementation, L4S has been adopted to support Augmented Reality (AR) video gaming traffic while using the IETF experimental standard Self-Clocked Rate Adaptation for Multimedia (SCReAM) for the congestion control. The results show that the video gaming traffic experiences lower delay when supported by L4S. Moreover, in all the cases analyzed, L4S provides an average application layer throughput above the minimum requirements of a high-rate latency-critical application, even at high system loads. Furthermore, the packet loss rate has been significantly reduced thanks to L4S. If it is used in a combination with a Delay Based Scheduler (DBS), a packet loss rate very close to zero has been reached.

The implementation of various base station (BS) energy saving (ES) features and the widely varying network traffic demand makes it imperative to quantitatively evaluate the energy consumption (EC) of 5G BSs. An accurate evaluation is essential to understand how to adapt a BS’s resources to reduce its EC. On the other hand, modeling the variation in the power consumption (PC) of a BS with its resources considering the user equipment (UE) performance is mathematically rigorous. In this work, we present a novel analytical methodology to evaluate the EC of a 5G BS under varying traffic load. We mathematically formulate the impact of massive multiple-input and multiple-output (MIMO) arrays, vast spectral resources, and the spatial multiplexing ability of these systems on the UE performance and activity of the BS. Next, we present an updated power model to capture the PC variation of two BSs types: a 4T and a 64T BS. Our proposed analytical methodology simplifies the complex network EC evaluation. Using this methodology, we show that identifying the right BS type for a given deployment area can reduce the overall network EC by up to 60%. Furthermore, by implementing deep sleep modes (SMs) facilitated by 5G, one can gain considerable energy savings (ES), especially during the off peak hours of the day.

Sensors currently deployed on board have wired connectivity, which increases weight and maintenance costs for aircraft. Removing cables for wireless communications of sensors on board alleviates the cost, however, the powering of sensors becomes a challenge inside aircraft. Wireless power transfer (WPT) via radio-frequency (RF) signals is an emerging solution to remotely power sensors for battery-less operation with long-lived capacitors. In this article, we design a WPT system for aircraft IoT-type applications, including low data rate inside (LI) sensors by determining the number, location, and tilt angles of WPT transmitters given constraints based on the cabin geometry and duty cycle of the sensors. We formulate a robust optimization problem to address the WPT system design under channel uncertainties. We also derive an equivalent integer linear programming and solve that for an optimal deployment to satisfy the duty cycle requirements of LI sensors. We perform experiments inside the cabin to validate the wireless avionics intracommunications channel model. Our simulations demonstrate the feasibility of 90% robust design with 14 WPT transmitters for duty cycles less than 0.1% while keeping the human radiation exposure below the recommended reference value of 4.57 W/m2.

A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. Utilizing the abundant processing capabilities of the clouds, mobile edge computing enables mobile devices with limited batteries to run resource hungry applications and to save power. However, it is not always true that edge computing consumes less power compared to device computing. It may take more power for the mobile device to transmit a file to the cloud than running the task itself. This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile edge computing networks. We consider the maximum acceptable delay as QoS metric to be satisfied in our network. We formulate the problem as a mixed integer nonlinear problem which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making. Simulation results illustrate that by utilizing the proposed algorithms, considerable power savings can be achieved, e.g., about 60 percent for large bit stream size compared to local computing baseline.

In mobile networks, base stations (BSs) have the largest share in energy consumption. To reduce BS energy consumption, BS components with similar (de)activation times can be grouped and put into sleep during their times of inactivity. The deeper and the more energy saving a sleep mode (SM) is, the longer (de)activation time it takes to wake up, which incurs a proportional service interruption. Therefore, it is challenging to timely decide on the best SM, bearing in mind the daily traffic fluctuation and imposed service level constraints on delay/dropping. In this study, we leverage an online reinforcement learning technique, i.e., SARSA, and propose an algorithm to decide which SM to choose given time and BS load. We use real mobile traffic obtained from a BS in Stockholm to evaluate the performance of the proposed algorithm. Simulation results show that considerable energy saving can be achieved at the cost of acceptable delay, i.e., wake-up time until we serve users, compared to two lower/upper baselines, namely, fixed (non-adaptive) SMs and optimal non-causal solution.

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.

In-flight Internet connectivity is a necessity for aircraft passengers as well as aircraft systems. It is challenging to satisfy required quality of service (QoS) levels for flows within aircraft due to the large number of users and the highly varying air to ground (A2G) link capacities composed of satellite and direct air to ground communication (DA2GC). To represent service quality variations, we propose models for the generated traffic flows from aircraft and variations in A2G links. We present three different forwarding schemes based on priority, delay requirements and history of the dropped flows metrics. Forwarding schemes schedule the flows in real time by choosing either satellite or direct air to ground link depending on the delay and capacity requirements of flows to maximize the number of accepted flows with required QoS guarantees in terms of dropped packets and delay. Also, the effect of local caching is studied to fully satisfy the QoS requirement of flows in simulated flights. We implement the forwarding procedures and caching in ns-3 and test their performance in a current connectivity scenario of 100 Mbps capacity for both the satellite spot and ground base station in a one-hour flight. Our study shows that although the forwarding procedure based on a combination of priority and delay requirement has relatively better performance than the other schemes, which are based on priority only and weighted average of all metrics, in dropped packet percentage and delay, the current connectivity setup is not able to satisfy all QoS requirements. Furthermore, at least 0.9 cache hit rate is required to satisfy all flows for at least 50% of simulated flights.

Localization plays a key role for safe operation of UAVs enabling beyond visual line of sight applications. Compared to GPS based localization, cellular networks can reduce the positioning error and cost since cellular connectivity is becoming a prominent solution as a communication system for UAVs. As a first step towards localization, UAV needs to receive sufficient number of localization signals each having a signal to interference plus noise ratio (SINR) greater than a threshold. On the other hand, three-dimensional mobility of UAVs, altitude dependent channel characteristics between base stations (BSs) and UAVs, its line of sight and non-line of sight conditions, and resulting interference from the neighboring BSs pose challenges to receive usable signals from the required number of BSs. In this paper, we utilize a tractable approach to calculate localizability probability, which is defined as the probability of successfully receiving usable signals from at least a certain number of BSs. Localizability has an impact on overall localization performance regardless of the localization technique to be used. In our simulation study, we investigate the relation between the localizability probability with respect to the number of participating BSs, post-processing SINR requirement, air-to-ground channel characteristics, and network coordination, which are shown to be the most important factors for the localizability performance of UAVs. We observe the localizability performance is better at higher altitudes which indicates that localizability with cellular networks for UAVs is more favorable than for terrestrial users.

Providing broadband in-flight Internet connectivity to aircraft is challenging. Today's options include satellite communications (SC) and direct air-to-ground communication (DA2GC). To overcome data rate, delay and cost limitations of SC and coverage limitations of DA2GC, one can extend DA2GC with air-to-air communication (A2AC) by enabling multi-hop communication. To investigate the A2AC performance, we construct a mixed integer linear programming (MILP) problem of DA2GC and A2AC, jointly considering interference in topology formation and flow assignment. Our objective is to maximize the number of aircraft that can be connected with a given specific minimum data rate threshold. The evaluation is performed for low aircraft density scenarios over the North Atlantic. We show that in the investigated scenarios, over 90 % of aircraft can have at least 50 Mbps, some being up to 1600 kilometers away from the closest base station (BS). Furthermore, we identify antenna capabilities as an important factor for A2AC performance.

Unmanned aerial vehicles (UAVs) need to "see and be seen" by manned aircraft for their safe operation. For this purpose, using available Automatic Dependent Surveillance-Broadcast (ADS-B) for all UAVs will not only saturate the 1090 MHz spectrum but will require an extra hardware. To mitigate these issues, we propose a ground based sense and avoid (GBSAA) system to enable coexistence of low altitude UAVs and ADS-B enabled flying vehicles (FVs), i.e., manned aircraft, for avoiding collisions. UAVs transmit their location information via a cellular technology to cloud, and a GBSAA base station accesses the aggregated information to send it to the ADS-B enabled FVs via ADS-B technology. We performed analytical and simulation studies to investigate the ADS-B message collision probability for different network parameters such as number of UAVs and ADS-B enabled FVs. Our study shows that for a collision probability of 0.1 with the lowest ADS-B update interval, GBSAA system can support approximately 10 times more UAVs than ADS-B only system. The proposed GBSAA approach provides a tremendous potential to integrate UAVs into airspace in a scalable manner.

A hybrid cloud radio access network (H-CRAN) architecture has been proposed to alleviate the midhaul capacity limitation in C-RAN. In this architecture, functional splitting is utilized to distribute the processing functions between a central cloud and edge clouds. The flexibility of selecting specific split point enables the H-CRAN designer to reduce midhaul bandwidth, reduce latency, save energy, or distribute the computation task depending on equipment availability. Meanwhile, techniques for caching are proposed to reduce content delivery latency and the required bandwidth. However, caching imposes new constraints on functional splitting. In this study, considering H-CRAN, a constraint programming problem is formulated to minimize the overall power consumption by selecting the optimal functional split point and content placement, taking into account the content access delay constraint. We also investigate the trade-off between the overall power consumption and occupied midhaul bandwidth in the network. Our results demonstrate that functional splitting together with enabling caching at edge clouds reduces not only content access delays but also fronthaul bandwidth consumption and saves energy finding a compromise between these performance metrics.

In this paper, we investigate the potential benefits and challenges of using millimeter wave (mm-Wave) cellular network to carry out Beyond Visual Line of Sight (BVLOS) operations for remote piloting of Unmanned Aerial Vehicles (UAVs). We evaluate the reliability and latency of the wireless link between a UAV and a serving mm-Wave Base Station (BS) in the finite blocklength regime with the ultra reliable low latency communications (URLLC) requirements of 5G. To perform a comprehensive evaluation, we integrate several realistic models including mm-Wave antenna arrays, beamforming, mm-Wave propagation, and LOS probabilities. Our results show that cooperation and coordination among BSs are critical for UAV piloting using mm-Wave technology. We analyze the performance of mm-Wave links under three scenarios representing different levels of interference mitigation. We show that there is a certain range of message size and block length where it is possible to achieve URLLC requirements with packet error probability of 10−5and latency below 1 ms when assuming cooperation.

In this paper, we study cross-layer optimization of low-power wireless links for reliability-aware applications while considering both the constraints and the nonideal characteristics of the hardware in Internet-of-Things (IoT) devices. Specifically, we define an energy consumption (EC) model that captures the energy cost-of transceiver circuitry, power amplifier (PA), packet error statistics, packet overhead, etc.-in delivering a useful data bit. We derive the EC models for an ideal and two realistic nonlinear PA models. To incorporate packet error statistics, we develop a simple, in the form of elementary functions, and accurate closed-form packet error rate approximation in Rayleigh block-fading. Using the EC models, we derive energy-optimal yet reliability and hardware compliant conditions for limiting unconstrained optimal signal-to-noise ratio (SNR), and payload size. Together with these conditions, we develop a semi-analytic algorithm for resource-constrained IoT devices to jointly optimize parameters on physical (modulation size, SNR) and medium access control (payload size and the number of retransmissions) layers in relation to link distance. Our results show that despite reliability constraints, the common notion-higher-order M-ary modulations are energy optimal for short-range communication-prevails, and can provide up to 180% lifetime extension as compared to often used OQPSK modulation in IoT devices. However, the reliability constraints reduce both their range and the energy efficiency, while nonideal traditional PA reduces the range further by 50% and diminishes the energy gains unless a better PA is used.

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.

In this paper, we propose a model for beyond visual line of sight (BVLOS) operation for remote piloting of unmanned aerial vehicles (UAVs), which utilizes different technologies such as mobile edge computing and augmented reality. Ultra reliable low latency communication (URLLC) is a key service of 5G that enables safe BVLOS operation. Since message size of piloting signal is finite and communication channel is altitude dependent, we study reliability and latency under finite blocklength regime for different altitudes. In our numerical study, we find that for message sizes 30 and 50 bits, coded packet size, i.e., blocklength, should be in the range of 200 and 300 bits to enable BVLOS operation. We also found that minimum distance between UAVs to avoid any crash should be around 0.2 m for 15 m/s UAV speed and different altitudes ranging from 1.5 m to 120 m. According to our study, BVLOS operation of UAVs can be realized by URLLC by providing error probability in the vicinity of 10-3, and latency on the order of milliseconds for downlink communication with blocklength of tens to hundred bits.

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.

The inevitable deployment of 5G and the Internet of Things (IoT) sheds the light on the importance of the energy efficiency (EE) performance of Device-to- Device (DD) communication systems. In this work, we address a potential IoT application, where different prioritized DD system, i.e., Low-Priority (LP) and High-Priority (HP) systems, co-exist and share the spectrum. We maximize the EE of each system by proposing two schemes. The first scheme optimizes the individual transmission power and the spatial density of each system. The second scheme optimizes the transmission power ratio of both systems and the spatial density of each one. We also construct and analytically solve a multi- objective optimization problem that combines and jointly maximizes both HP and LP EE performance. Unique structures of the addressed problems are verified. Via numerical results we show that the system which dominates the overall EE (combined EEs of both HP and LP) is the system corresponding to the lowest power for low/high power ratio (between HP and LP systems). However, if the power ratio is close to one, the dominating EE corresponds to the system with higher weight.

On-board of aircraft is one of the last venues without high-speed connectivity, which makes it an important problem to address for both industry and academia. To this end, direct air-to-ground communications (DA2GC), where communication link is provided via direct link between aircraft and ground station, is a promising solution to provide high capacity and low latency backhaul capacity for aircraft. In this paper, we investigate the ground station deployment problem to provide 1.2 Gbps average backhaul capacity for each aircraft. The feasible operation points for the key network parameters: the number of ground stations, antenna array size, transmit power and bandwidth, are calculated. In addition, we propose a multi-user beamforming algorithm for dual-polarized hybrid DA2GC antenna arrays, and derive a tractable expression for the DA2GC cell throughput.

As the larger share of total energy consumed by mobile network operators (MNOs) is wasted in order to ensure coverage, three to five MNOs covering the same geographical area results in enormous energy waste. In order to cater for the data tsunami with almost zero marginal revenue, required densification of cells are not sustainable from both Capex and Opex perspective. Even with densification of networks, it is hard to satisfy the performance requirement of the cell edge users due to interference. However, the performance can be upgraded as well as energy can be saved by the offloading of the cell edge users to other MNOs if regulator and MNOs resort to appropriate mechanism. In our previous work, we proposed a double auction based energy saving market mechanism where MNOs participate in bidding to share coverage and capacity in order to save energy, especially during low to medium load. Unlike previous work, in this paper we use this mechanism that involve cell level bidding, i.e., cells bid for each user which not only allows total offloading of the cells at the low load but also offloading of cell edge users among the MNOs during high load. As a result, the energy saving potential becomes very high also at high load conditions along with improvement in performance of cell edge users.

Centralized Radio Access Network (C-RAN) has been recently proposed to increase network capacity, reduce energy consumption, and improve scalability. However, C-RAN requires an extensive modification to the current infrastructure, which results in a considerable deployment cost. In this paper, we conduct a techno-economic study to evaluate the migration cost of C-RAN, and we propose a methodology for cost and energy efficient C-RAN deployment. We exploit the concept of total cost of ownership, defined as the sum of capital and operational expenditures. We formulate a Digital Unit (DU) pool placement optimization problem as Mixed Integer Linear Programming (MILP), which minimizes the total cost of ownership. We compare the total cost of ownership of C-RAN to that of the existing infrastructure, under different deployment scenarios such as greenfield and brownfield deployment of fiber and DU pool, and different cell sizes. The results show that the optical infrastructure plays a determinant role in the migration cost of C-RAN. If greenfield fiber is assumed, the migration cost cannot be compensated in a reasonable amount of time. If brownfield fiber is assumed, the migration cost is considerably reduced, and a more feasible C-RAN deployment is achieved.

Cloud radio access network (CRAN) has been proposed as a potential energy saving architecture and a scalable solution to increase the capacity and performance of radio networks. The original CRAN decouples the digital unit (DU) from radio unit (RU) and centralizes the DUs. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN, i.e. the network segment connecting RUs and DUs. In this study, we propose a modified CRAN architecture, namely hybrid cloud RAN (H-CRAN), where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions, i.e., central cloud level and edge cloud level, connected by a transport layer called “midhaul”. We study the interplay of energy efficiency and midhaul bandwidth consumption when baseband functions are centralized at the edge cloud vs central cloud. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying the network constraints. The addressed problem with the associated constrains are modeled as a mixed integer constraint optimization problem. Numerical results show the compromise between energy and bandwidth consumption, with the optimal placement of baseband processing functions in H-CRAN architecture.

Cloud radio access network (CRAN) has been proposed as a promising evolution of mobile network architecture where baseband processing functions of a base station are split/decoupled from the radio unit (RU) and centralized. However, rigid bandwidth and latency requirements are incurred by fronthaul, i.e., transport link, which connects RU to the central cloud. Therefore, new functional splits are discussed for CRAN with dual-site processing, which we call Hybrid-RAN (H-RAN), where some functions remain distributed while others are centralized. In this work, from a perspective of minimizing the total cost of ownership (TCO) for H-RAN, we present a techno-economic study to find the optimal functional splits for a base station (BS), with a given configuration. A configuration of a BS represents frequency layers, carrier bandwidths, and MIMO schemes, associated with different frequency bands. For each functional split, we present a model to calculate the requirement of computational resources and fronthaul bandwidth. We formulate a TCO minimization model using constraint programming. Numerical results show that the optimal functional split depends on BS configuration, fiber ownership, and data transmission direction. H-RAN with optimal functional split can achieve lower TCO than both classical Distributed RAN and CRAN.

This paper focuses on the impact of intelligent topology formation and routing on resilient mesh topologies consisting of heterogeneous devices in Internet of Things (IoT) networks. We consider a Bluetooth network comprising of low-power short-range devices that are heterogeneous in terms of energy source (e.g. the mains or coin-cell). In the first step, a resilient mesh topology is created by taking into account device characteristics such as energy sources and parameters related to neighbors. The Bluetooth devices negotiate master and slave roles, and form piconets which are then connected through multiple bridge nodes thus forming a scatternet. An energy aware algorithm is proposed to select the routing paths for packet forwarding between connected devices. The performance of topology and routing is evaluated through simulations in a large indoor office scenario based on realistic channel models and propagation characteristics. Results show that intelligent topology formation, in conjunction with the proposed energy aware routing algorithm leads to a significant gain (more than 100%) in terms of network lifetime, when compared to the baseline approaches.

Aircraft is one of the last venues with no high-speed connectivity, which makes it an important research issue to address for both industry and academia. This paper introduces seamless gate-to-gate connectivity concept so that passengers can stay connected in all phases of the flight. The backhaul capacity is provided via direct air- to-ground communications links. Passengers can utilize both LTE and Wi-Fi access technologies on-board. In order to avoid interference with licensed ground LTE network, in-cabin LTE users will be served in the unlicensed spectrum via license assisted access (LAA) functionality when the aircraft is close to the ground. In this paper, we determine the altitude threshold for switching to LAA in order to provide seamless LTE connectivity. According to our analysis the myth about the 3 km threshold for the interference with the terrestrial network is not anymore valid. In addition, throughput and user data rates for LTE and Wi-Fi networks are investigated in different flight phases. We have found out that in-cabin LTE network can serve 80-240 users between 5-15 Mbps data rates, and Wi-Fi network can provide peak data rates of 14 Mbps in the worst-case scenario.

This paper investigates the power minimization problem for the user terminals by application offloading in multi-cell multi-user OFDMA mobile cloud computing networks where some practical constraints such as backhaul capacity limitation, interference level on each channel and maximum tolerable delay as user's quality of service is taken into account. Furthermore, the mixed integer nonlinear problem is converted into a convex form using D.C. approximation. Moreover, to solve the optimization problem, we have proposed joint power allocation and decision making (J-PAD) algorithm which can make offloading decision and allocate power at the same time. Simulation results illustrate that by utilizing the J-PAD algorithm, in comparison with baselines, considerable power saving could be achieved e.g. about 30% for delays more than 100 ms.

In this paper we investigate the joint power and channel allocation problem in two-tier OFDMA femtocell networks using a multiobjective approach with focus on energy efficiency. Three main objectives are considered in our problem formulations namely, energy efficiency, spectral efficiency, and power consumption. To solve the muliobjective problems, we have utilized a non- dominated sorting genetic algorithm (NSGA-II) and an algorithm have been proposed to perform the resource allocation procedures. In this investigation, to preserve the quality service of users, we have applied a minimum data rate threshold for all users. Furthermore, we impose an interference threshold limit on each subchannel to protect the macrocell user quality of service. Finally, the simulation results figure out that we can achieve 30% better energy efficiency by trading the throughput by about 20%.

As a potential candidate architecture for 5G systems, cloud radio access network (CRAN) enhances the system's capacity by centralizing the processing and coordination at the central cloud. However, this centralization imposes stringent bandwidth and delay requirements on the fronthaul segment of the network that connects the centralized baseband processing units (BBUs) to the radio units (RUs). Hence, hybrid CRAN is proposed to alleviate the fronthaul bandwidth requirement. The concept of hybrid CRAN supports the proposal of splitting/virtualizing the BBU functions processing between the central cloud (central office that has large processing capacity and efficiency) and the edge cloud (an aggregation node which is closer to the user, but usually has less efficiency in processing). In our previous work, we have studied the impact of different split points on the system's energy and fronthaul bandwidth consumption. In this study, we analyze the delay performance of the end user's request. We propose an end-to-end (from the central cloud to the end user) delay model (per user's request) for different function split points. In this model, different delay requirements enforce different function splits, hence affect the system's energy consumption. Therefore, we propose several research directions to incorporate the proposed delay model in the problem of minimizing energy and bandwidth consumption in the network. We found that the required function split decision, to achieve minimum delay, is significantly affected by the processing power efficiency ratio between processing units of edge cloud and central cloud. High processing efficiency ratio (≈1) leads to significant delay improvement when processing more base band functions at the edge cloud.

Energy optimization in cellular networks has been studied using different perspectives in the literature: sleep patterns, network interference, association of users and base stations, resource allocation of resources (bandwidth and power), etc. All these means have been discussed individually in previous works. However, none of the existing works has succeeded in proposing an exact mathematical model that takes into account several of these parameters simultaneously. In this article, we propose a first exact modelling of several network parameters and their interaction in order to minimize the energy consumption in a LTE cellular network. The optimization model guarantees to satisfy all the users with a minimum quality of service (data rate). Its exact solution allows energy savings of up to 50% in a moderately loaded network, which leads to energy savings up to twice that of the heuristic proposed by Piunti et al., (2015). Various numerical results are presented on hexagonal and randomly generated cellular networks.

Today, mobile networks are witnessing an exponential growth of traffic volumes, linked to new services, especially for smart cities and smart-grid. The European Celtic-Plus SooGREEN project, started mid 2015, is targeting to reduce the energy consumption of the services in different mobile architectures in interaction with smart-grid. So GREEN is focused on the services energy consumption modelling and measurement, the dynamic optimization of the mobile access network and of the content delivery, the design of an Energy Efficient Virtualized and Centralized Radio Access Network (RAN), and the bi-directional interaction of the mobile network with the smart-grid. This paper presents insight into the project after its first year, and discusses research trends in green communication networks for the future.

5G Radio Access Networks (RANs) are supposed to increase their capacity by 1000x to handle growing number of connected devices and increasing data rates. The concept of cloud-RAN (CRAN) has been recently proposed to decouple digital units (DUs) and radio units (RUs) of base stations (BSs), and centralize DUs into central offices. CRAN can ease the implementation of advanced radio coordination techniques, e.g., Coordinated Multi-Point (CoMP) Transmission/Reception, to enhance its system throughput. However, separating DUs and RUs, and implementing CoMP in CRAN require low-latency and high-bandwidth connectivity links, called "fronthaul". Today, consensus has not yet been achieved on how BSs, fronthaul, and central offices will be orchestrated to enhance the system throughput. In this study, we present a CRAN over Passive Optical Network (PON) architecture called virtualized-CRAN (V-CRAN). V-CRAN leverages the concept of virtualized PON (VPON) that can dynamically associate any RU to any DU so that several RUs can be coordinated by the same DU, and the concept of virtualized BS (V-BS) that can jointly transmit common signals from multiple RUs to a user. We propose a novel mathematical model based on constraint programming for joint allocation of radio, optical network, and baseband processing resources to enhance RAN throughput, and we solve it by optimally forming VPONs and V-BSs. Comprehensive simulations show that V-CRAN can enhance the system throughput and the efficiency of resource utilization.

This paper evaluates different means of reducing power consumption of macro base stations (BS) and heterogeneous mobile network deployments (HetNet) considering the time dimension. These approaches are based on the same idea of reducing the load of heavily loaded macro cells and putting them to discontinuous transmission (DTX) mode during the time of inactivity by either (1) macro cell densification or (2) offloading traffic to small cells. Activity factor of a BS is defined as the fraction of time the BS is transmitting over a fixed time period. It is shown that by macro cell layer network densification, the average daily area power consumption can be reduced by up to 73 % with the use of cell DTX. However, reducing the activity factor by macro layer densification is not cost effective, as already demonstrated in previous studies. Alternatively, by adding small cells and enabling their DTX capability, power consumption can be reduced by up to 29 %. Adding small cells is especially effective in terms of energy savings, when users are distributed around hot spots, where additional coverage and capacity is required.

The paper studies various mesh topology formation techniques that can be used to aid the development of large-scale capillary networks. The work focuses on how mesh networks can be established using Bluetooth Low Energy exploiting the heterogeneous characteristics of the devices in the network. A novel algorithm called Topology Formation considering Role Suitability (TFRS) is proposed aiming to maximize the network lifetime. The algorithm employs a newly introduced metric called role suitability metric (RSM) to assign the best role among master, relay and slave to a participating device. The RSM metric is computed from device characteristics including, but not limited to, energy, mobility and computational capability. We use system-level simulation to evaluate the performance of the proposed algorithm against a reference under a homogeneous deployment scenario consisting of heterogeneous devices. Results show that the network lifetime can be improved significantly when the topology is formed considering the device characteristics for both master role selection and relay selection. TFRS can achieve 20% to 40% higher network lifetime depending on the deployment characteristics over the reference algorithm.

Cellular network energy optimization is driven by different factors, namely transmission power and activity of the base stations. Quality of Service (QoS) of users is determined by received power, interference and bandwidth allocation and cannot be neglected. In existing studies, such drivers are treated separately, i.e., bandwidth allocation is fixed while power consumption is a variable to be optimized. This paper proposes a novel optimization framework which is aimed at minimizing the power consumption in cellular networks while affording a minimum bit rate for each mobile terminal by jointly considering energy consumption and QoS drivers. Mixed Integer Quadratic Programming (MIQP) based optimization framework solves the problems of the determination of the user association, the bandwidth allocation, the identification of the active base stations and their transmission power, guaranteeing also a requested service rate for each user. The proposed solution is shown to allow power savings of up to 60%, very close to the optimum lower bound, when the traffic is below the 35% of the maximum load.

The deployment of low cost and low power base stations has been recognized in recent years as a promising cost-efficient solution and energy-efficient strategy. In this paper the femtocell and macrocell deployment options have been compared in the context of indoor mobile broadband deployment, with focus on the effect of different backhauling solutions in power consumption and the total deployment cost. This study has been based on the deployment of mobile broadband services within an area of one square kilometers in a new densely populated business district where the different user demands, spectrum bandwidths, backhaul technologies and radio access technologies are taken into account. Moreover, various deployment scenarios reflecting the business perspectives of mobile operators have been looked into as well. The main findings reached indicate that backhaul solutions contribute differently to cost and power consumption depending on the employed deployment strategy. However, contributions to the total power consumption and to the CapEx and OpEx elements of the total deployment cost elements turned to be more significant in the case of femtocell deployment scenarios than in the case of macrocell ones. It is worthy of notice that the femtocell deployment is more cost-efficient, especially in high demand situation when new macro sites are needed to be deployed.

In 2020, mobile access networks will experience significant challenges as compared to the situation of today. Traffic volumes are expected to increase 1000 times, and the number of connected devices will be 10-100 times higher than today in a networked society with unconstrained access to information and sharing of data available anywhere and anytime to anyone and anything. One of the big challenges is to provide this 1000-fold capacity increase to billions of devices in an affordable and sustainable way. Low energy consumption is the key to achieve this. This paper takes as starting point the situation of today, and tries to pinpoint important focus areas and potential solutions when designing an energy efficient 5G mobile network architecture. These include system architecture, where a logical separation of data and control planes is seen as a promising solution; network deployment, where (heterogeneous) ultra dense layouts will have a positive effect; radio transmission, where the introduction of massive antenna configurations is identified as an important enabler; and, finally, backhauling solutions that need to be more energy efficient than today.

Energy consumption of communication networks is growing very fast due to the rapidly increasing traffic demand. Consequently, design of green communication networks gained a lot of attention. In this paper we focus on optical Wavelength Division Multiplexing (WDM) networks, able to support this growing traffic demand. Several energy-aware routing and wavelength assignment (EA-RWA) techniques have been proposed for WDM networks in order to minimize their operational cost. These techniques aim at minimizing the number of active links by packing the traffic as much as possible, thus avoiding the use of lightly loaded links. As a result, EA-RWA techniques may lead to longer routes and to a high utilization on some specific links. This has a detrimental effect on the signal quality of the optical connections, i.e., lightpaths. In this study we quantify the impact of power consumption minimization on the optical signal quality. and address this problem by proposing a combined impairment and energy-aware RWA (IEA-RWA) approach. Towards this goal we developed a complete mathematical model that incorporates both linear and non-linear physical impairments together with an energy efficiency objective. The IEA-RWA problem is formulized as a Mixed Integer Linear Programming (MILP) model where both energy efficiency and signal quality considerations are jointly optimized. By comparing the proposed IEA-RWA approach with existing RWA (IA-RWA and EA-RWA) schemes, we demonstrate that our solution allows for a reduction of energy consumption close to the one obtained by EA-RWA approaches, while still guaranteeing a sufficient level of the optical signal quality.

In recent years, energy-efficient design of optical WDM networks has become increasingly important as efforts to reduce the operational expenditure (Opex) and the carbon footprint of the internet are prioritized. In this paper we focus on energy- efficient survivable network design where backup resources are shared for efficient capacity consumption. However there is a trade-off between energy-efficiency and survivability. Survivable network design strategies lead to lightly loaded links in order to minimize the risk in case of a failure and to increase the shareability of backup resources. On the contrary, energy-efficient network design strategies tend to increase the load in a set of links as a consequence of concentrating the traffic in order to be able to switch off as much network resources as possible. In this study, we present a novel method to simultaneously minimize Capex and Opex while providing an energy-efficient, shared backup protected network, under the assumption of backup capacity in sleep mode. For the first time we propose an ILP formulation for the energy-efficient shared backup protection problem. By exploiting the sleep mode for the backup resources, we observe that the ILP solution of our mathematical model brings up to 40% gain in energy efficiency in comparison to energy-unaware shared backup protection approach.