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.

Energy harvesting can enable a reconfigurable intelligent surface (RIS) to self-sustain its operations without relying on external power sources. In this paper, we consider the problem of energy harvesting for RISs in the absence of coordination with the ambient RF source. We propose a series of sequential phase-alignment algorithms that maximize the received power based on only power measurements. We prove the convergence of the proposed algorithm to the optimal value for the noiseless scenario. However, for the noisy scenario, we propose a linear least squares estimator. We prove that within the class of linear estimators, the optimal set of measurement phases are equally-spaced phases. To evaluate the performance of the proposed method, we introduce a random phase update algorithm as a benchmark. Our simulation results show that the proposed algorithms outperform the random phase update method in terms of achieved power after convergence while requiring fewer measurements per phase update. Using simulations, we show that in a noiseless scenario with a discrete set of possible phase shifts for the RIS elements, the proposed method is sub-optimal, achieving a higher value than the random algorithm but not exactly the maximum feasible value that we obtained by exhaustive search.

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.

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).

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.

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.

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

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.

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.

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.

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%.