Although multiple works have proposed energy-efficient resource allocation schemes for Massive Multiple-Input Multiple-Output (M-MIMO) system, most approaches overlook the potential of optimizing Power Amplifier (PA) transmission power while accounting for non-linear distortion effects. Furthermore, most M-MIMO studies assume narrow-band transmission, neglecting subcarrier intermodulations at the non-linear PA for an Orthogonal Frequency Division Multiplexing (OFDM) system. Therefore, this work investigates the energy-efficient power allocation for a single-user equipment (UE) M-MIMO downlink (DL) system employing OFDM with nonlinear PAs. Unlike prior works, we model wide-band transmission using a soft-limiter PA model and derive a closed-form expression for the signal-to-distortion-and-noise ratio (SNDR) under Rayleigh fading and Maximal Ratio Transmission (MRT) precoding. Next, the Energy Efficiency (EE) function is defined considering two PA architectures and a distorted OFDM signal. We then propose a low complexity root-finding algorithm to maximize EE by transmit power adjustment. Simulation results demonstrate significant EE gains over a fixed PA back-off baseline, with over $100\%$ improvement under both low and high path loss. Our findings reveal how the optimal operating point depends on the antenna count, the PA model, and the propagation conditions.

One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Centric Cell-Free Massive Multiple-Input-Multiple-Output (UCCF MMMIMO), which allows for simultaneously serving a single user by multiple Base Stations (BSs). From this perspective, the key challenge is to decide which BSs should serve a given user, known as the Serving Cluster Formulation (SCF). In this paper, we propose to deal with this problem by using an Artificial Intelligence (AI) agent based on a Large Language Model (LLM), targeting improvement of EE. We evaluated the proposed AI agent using a complex, 3D Ray Tracer-based, cellular network simulator, comparing a few GPT models and state-of-the-art algorithms. The results show up to 32% gain in EE of the proposed AI agent compared to the baseline.

Real-world wireless transmitter frontends exhibit certain nonlinear behavior, e.g., signal clipping by a Power Amplifier (PA). Although many resource allocation solutions do not consider this for simplicity, it leads to inaccurate results or a reduced number of degrees of freedom, not achieving the global performance. In this work, we propose an optimal PA distortion-aware power allocation strategy in a downlink orthogonal frequency division multiplex (OFDM) based massive multiple-input multiple-output (M-MIMO) system. Assuming a soft-limiter PA model, where the transmission occurs under small-scale independent and identically distributed (i.i.d) Rayleigh fading channel, we derive the wideband signal-to-noise-and-distortion ratio (SNDR) and formulate the power allocation problem. Most interestingly, the distortion introduced by the PA leads to an SNDR-efficient operating point without explicit transmit power constraints. While the optimization problem is non-convex, we decouple it into a non-convex total power allocation problem and a convex power distribution problem among the users (UEs). We propose an alternating optimization algorithm to find the optimum solution. Our simulation results show significant sum-rate gains over existing distortion-neglecting solutions, e.g., a median 4 times increase and a median 50% increase for a 64-antenna and 512-antenna base station serving 60 users, respectively.

The Single Carrier- Frequency Division Multiple Access (SC-FDMA) is a transmission technique used in the uplink of Long Term Evolution (LTE) and 5G systems, as it is characterized by reduced transmitted signal envelope fluctuations in comparison to Orthogonal Frequency Division Multiplexing (OFDM) technique used in the downlink. This allows for higher energy efficiency of User Equipments (UEs) while maintaining sufficient signal quality, measured by Er-ror Vector Magnitude (EVM), at the transmitter. This paper proposes to model a nonlinear Power Amplifier (PA) influence while optimizing the transmit power in order to maximize the Signal to Noise and Distortion power Ratio (SNDR) at the receiver, removing the transmitter-based EVM constraint. An analytic model of SNDR for the OFDM system and a semi-analytical model for the SC-FDMA system are provided. Numerical investigations show that the proposed transmit power optimization allows for improved signal quality at the receiver for both OFDM and SC-FDMA systems. However, SC-FDMA still outperforms OFDM in this matter. Such a power amplifier-aware wireless transmitter optimization should be considered to boost the performance and sustainability of next-generation wireless systems, including Internet of Things (IoT) ones.

The concept of open radio access networks (RAN) creates numerous opportunities for developing new technology and economy branches. At the same time, a flexible and modular approach in the disaggregated RAN entails the need for careful design of the overall RAN architecture and the implementation and deployment process of new applications. It is assumed that dedicated and specialized software companies may deliver the latter. A joint effort must be guaranteed among different sectors (industry, academia, and standardization bodies) to make the whole process efficient, safe, and reliable. Here, one of the critical driving forces origins from the open-source community that often stimulates the development of a specific technology. In this paper, we address the challenges that have to be faced by third-party application developers in the context of Open RAN. Based on many implemented applications (called xApps or rApps), we compare various available solutions. We pose the most critical issues that must be tackled in the near future to stimulate the progress in open RAN development further. In particular, we compare available open platforms for xApp development and testing. We present the details of implementing four selected applications describing the problems encountered. The paper is split into two logical parts - first, we identify the key ambiguities related to the development of new xApps, which address more complicated use cases like beam management. In the second part, we present the challenges associated with detailed software implementation in existing open platforms. In the first case, we show that dedicated beam mobility management xApp can reduce beam switches and keep beam failures low. However, it requires access to detailed localization information. Similarly, the signaling storm detection xApp provides expected performance under the assumption that there is access to detailed information on, e.g., time advance resolution parameter. We conclude here that several aspects still need to be well-defined to allow smooth software implementation; these include the rules for data reporting in time, parameters available in service models, and localization features. Concerning the second logical part, related to low-level implementation, we compare the numerical results of the traffic steering and quality-of-service-based resource allocation xApps and draw conclusions related to implementation and testing. In particular, we point out problems associated with the simulator, the software, and conflicts inside. Finally, we identify the key challenges which should be treated as incentives for joint academia-industry cooperation in the field of Open RAN. Thus, the paper presents the lesson learned during the first years of xApp development.

One of the essential security threats for 5G networks is jamming. It is relatively easy to be performed utilizing cheap and publically available devices. Open Radio Access Network (O-RAN) architecture is very suitable for detecting such events thanks to the openness of interfaces and the ability to analyze wireless traffic metrics and exchange control messages in a RAN Intelligent Controller (RIC) using some dedicated xApp or rApp. This paper presents a statistical method for downlink jamming detection utilizing the link quality reports provided by User Equipments (UEs). A vision of its implementation in O-RAN is presented altogether with performance quality metrics obtained via simulations.

Massive multiple-input multiple-output (mMIMO) technology is considered a key enabler for the 5G and future wireless networks. In most wireless communication systems, mMIMO is employed together with orthogonal frequency-division multiplexing (OFDM) which exhibits a high peak-to-average-power ratio (PAPR). While passing the OFDM signal through one of the common RF front-ends of limited linearity, significant distortion of the transmitted signal can be expected. In mMIMO systems, this problem is still relevant as in some channels the distortion component is beamformed in the same directions as the desired signal. In this work, we propose a multi-antenna clipping noise cancellation (MCNC) algorithm for the downlink of the mMIMO OFDM system. Computer simulations show it can remove nonlinear distortion even under severe nonlinearity. Next, a simplified version of the algorithm is proposed. It was observed that for the direct visibility channels, its performance is only slightly degraded with respect to the MCNC algorithm.

Fog networks offer computing resources of varying capacities at different distances from end users. A Fog Node (FN) closer to the network edge may have less powerful computing resources compared to the cloud, but the processing of computational tasks in FN limits long-distance transmission. How should the tasks be distributed between fog and cloud nodes? We formulate a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem minimizing task transmission- and processing-related energy with delay constraints to answer this question. It is transformed with Successive Convex Approximation (SCA) and decomposed using the primal and dual decomposition techniques. Two practical algorithms called Energy-EFFicient Resource Allocation (EEFFRA) and Low-Complexity (LC)-EEFFRA are proposed and their effectiveness is tested for various network and traffic scenarios. Using EEFFRA/LC-EEFFRA can significantly decrease the number of computational requests with unmet delay requirements when compared with baseline solutions (from 48% to 24% for 10 MB requests). Utilizing Dynamic Voltage and Frequency Scaling (DVFS) minimizes energy consumption (by one-third) while satisfying delay requirements.

Platoon-based driving is an idea that vehicles follow each other at a close distance, in order to increase road throughput and fuel savings. This requires reliable wireless communications to adjust the speeds of vehicles. Although there is a dedicated frequency band for vehicle-to-vehicle (V2V) communications, studies have shown that it is too congested to provide reliable transmission for the platoons. Additional spectrum resources, i.e., secondary spectrum channels, can be utilized when these are not occupied by other users. Characteristics of interference in these channels are usually location-dependent and can be stored in the so-called Radio Environment Maps (REMs). This paper aims to design REM, in order to support the selection of secondary spectrum channel for intra-platoon communications. We propose to assess the channel’s quality in terms of outage probability computed, with the use of estimated interference distributions stored in REM. A frequency selection algorithm that minimizes the number of channel switches along the planned platoon route is proposed. Additionally, the REM creation procedure is shown that reduces the number of database entries using (Density-Based Spatial Clustering of Applications with Noise) DBSCAN algorithm. The proposals are tested using real IQ samples captured on a real road. Application of the DBSCAN clustering to the constructed REM provided 7% reduction in its size. Utilization of the proposed channel selection algorithm resulted in a 35 times reduction of channel switches concerning channel assignment performed independently in every location.

Abstract Contemporary wireless communication transceivers can utilize their multiple antennas to improve positioning abilities. Angle of Arrival (AoA) estimation utilizes shifts in phase between the signal of interest arriving at different receiving antennas. Software Defined Radio (SDR) USRP B210 and GNU Radio implementing the Root Multiple Signal Classification (Root-MUSIC) algorithm are used in this paper. However, this setup requires the consideration of errors caused by hardware and radio environment. The impact of these effects is shown using an analytical model, assessed, mostly by measurements, and relevant solutions are proposed. The hardware distortions are caused mostly by synchronization errors. The intricacies of the most problematic phase synchronization are investigated with the wired setup showing, e.g., changes with frequency or gain. Both synchronizations emitting calibration tone from the same radio front end, resulting in cross-talk, or a separate one are tested. Recommendations enhancing the performance of the system and alleviating hardware imperfections are provided in this paper. The accuracy of AoA estimation is degraded by multipath propagation or radio interference. A signal processing scheme including filtering has been proposed. A set of over-the-air experiments assessed the performance of the system. The presented and publicly available software package, scalable to a higher number of RX antennas, enables straightforward implementation by other researchers.

In this paper, we consider the use of radio environment maps (REMs) in vehicular dynamic spectrum access (VDSA) for vehicle platooning applications. We propose an algorithm that dynamically allocates the frequency bands and transmission power in the so-called TV white spaces (TVWS) for intra-platoon messaging, intending to maximize the reliability of the communications, simultaneously keeping the interference to the primary system below the required threshold. The proposed solution is evaluated in simulations, with the results indicating a significant increase in communications reliability with VDSA.

The Massive Multiple-Input Multiple-Output (MMIMO) technique together with Heterogeneous Network (Het-Net) deployment enables high throughput of 5G and beyond networks. However, a high number of antennas and a high number of Base Stations (BSs) can result in significant power consumption. Previous studies have shown that the energy efficiency (EE) of such a network can be effectively increased by turning off some BSs depending on User Equipments (UEs) positions. Such mapping is obtained by using Reinforcement Learning. Its results are stored in a so-called Radio Environment Map (REM). However, in a real network, the number of UEs' positions patterns would go to infinity. This paper aims to determine how to match the current set of UEs' positions to the most similar pattern, i.e., providing the same optimal active BSs set, saved in REM. We compare several state-of-the-art distance metrics using a computer simulator: an accurate 3D-Ray-Tracing model of the radio channel and an advanced system-level simulator of MMIMO Het-Net. The results have shown that the so-called Sum of Minimums Distance provides the best matching between REM data and UEs' positions, enabling up to 56% EE improvement over the scenario without EE optimization.

In this paper, we investigate the concept of database supported Vehicular Dynamic Spectrum Access (VDSA) for platooning. As various researchers show that the 5.9 GHz band, devoted for Intelligent Transportation Systems, may suffer from congestion of the channel, we propose to offload part of this traffic to white-spaces with the guidance of the active database system. In our work, we describe our measurement campaign which delivered data for population of the dedicated radio environment map. Once the map is created, it was used in three proposed algorithms for VDSA: an optimal and two pragmatic approaches.

In this paper, we propose a distributed Vehicular Dynamic Spectrum Access (VDSA) framework for vehicles operating in platoon formations. Given the potential for significant congestion in licensed frequency bands for vehicular applications such as 5.9 GHz. Our approach proposes to offload part of the intra-platoon data traffic to spectral white-spaces in order to enhance vehicular connectivity in support of on-road operations. To enable VDSA, a Bumblebee-based decision making process is employed which is based on the behavioral models of animals, is employed to provide a means of distributed transmission band selection. Simulation results show the distributed VDSA framework improves the leader packets reception ratio by 5%, thus indicating its potential to increase in reliability of intra-platoon communications.

The introduction of large antenna arrays facilitating massive multiple-input-multiple output (M-MIMO), and the addition of a tier of pico and femto base stations (BS), implementing an heterogeneous network, provides means to improve network throughput and capacity in 5G networks. However, the addition of antennas and BSs implies additional hardware and is associated with higher energy consumption. Improving the energy efficiency (EE) while reducing the power consumption of such heterogeneous M-MIMO dense networks can be performed by switching off base stations that have few users to serve and redistribute those users among the active neighboring base stations. One promising solution to intelligently map user spatial distribution to the optimal set of active BSs is by utilizing radio service maps (RSM). In this paper we propose a novel approach that effectively switches off base stations by combining reinforcement learning with RSM data. The proposed approach is evaluated through computer simulations using a 3D ray tracing model. The simulation results show the benefits of RSM and machine learning use for the improvements in EE of the considered heterogeneous M-MIMO networks.

In this paper, the power consumption model of a wireless transceiver is proposed. It is based on measurements of a number of off-the-shelf WiFi cards. The power consumption was divided into an idle, associated, and transmission states. Then, the eight-coefficients power consumption model depending on the rate and path-loss has been proposed and the values of coefficients have been determined. While there are significant differences in the power consumption among considered devices, the proposed model is well aligned with the measurements for each of them. Moreover, this paper shows that models based on a single device characterization can result in significant errors if employed to assess network power consumption.

In this paper, we investigate joint subcarrier (SC) allocation, pairing and power loading for optimized energy efficiency (EE) in multiuser, multicell, multicarrier downlink decode-and-forward (DF) relay interference networks with computational awareness, i.e., taking computations-related energy into account. In order to maximize EE of the network, the transmission mode is adapted to instantaneous channel conditions. For the benefit of spectral-efficiency, both direct- and relayed transmission is allowed to use the same SCs simultaneously. Linearly rate-dependent power consumption of signal processing is considered. The formulated optimization problem is the nonconvex fractional mixed binary-integer programming problem, which has NP-hard complexity. Hence, we approximate the problem by the series of equivalent convex problems applying convex relaxation techniques such as a Successive Convex Approximation (SCA). Based on these transformations, we develop an iterative algorithm exploiting the Dinkelbach method to tackle the nonlinear fractional programming problem which maximizes EE of the system. Moreover, in our considerations, the total transmission power constraint and the minimum required rate constraints have been included. Simulation results demonstrate the effectiveness of our solution for future relay networks.

Fog computing can be used to offload computationally intensive tasks from battery powered Internet of Things (IoT) devices. Although it reduces energy required for computations in an IoT device, it uses energy for communications with the fog. This paper analyzes when usage of fog computing is more energy efficient than local computing. Detailed energy consumption models are built in both scenarios with the focus set on the relation between energy consumption and distortion introduced by a Power Amplifier (PA). Numerical results show that task offloading to a fog is the most energy efficient for short, wideband links.

We discuss the idea of waveform flexibility and resource allocation in future wireless networks as a promising tool for network slicing implementation down to the lowest layers of the OSI (Open Systems Interconnection) models. In particular, we consider the possibility of cognitively adjusting the shape of the waveform to the requirements associated with various network slices. Moreover, such an adjustment of waveform shape is realised jointly with the selection and allocation of the appropriate frequency bands to each slice. In our approach, the definition of the waveform, as well as the assignment of resources, is done based on the information about the surrounding environment and each slice requirement stored in a dedicated context-information database. In this paper, we present the key concept of waveform flexibility for network slicing, the proposed algorithm for waveform selection and resource allocation among slices, and the achieved simulation results.

Dynamic spectrum sharing can provide many benefits to wireless networks operators. However, its efficiency requires sophisticated control mechanisms. The more context information is used by it, the higher performance of networks is expected. A facility for collecting this information, processing it, and controlling base stations managed by various network operators is a so‐called Radio Environment Map (REM) subsystem. This paper proposes REM‐based schemes for the allocation of base stations power levels in 4G/5G networks, while considering interference generated to a licensed network. It is assumed that both networks have different profiles of served users, e.g., area of their positions and movement, which opens opportunities for spectrum sharing. The proposed schemes have been evaluated by means of extensive system‐level simulations and compared with two widely adopted policy‐based spectrum sharing reference schemes. Simulation results show that dynamic schemes utilizing rich context information outperforms static, policy‐based spectrum sharing schemes.

In this paper, we investigate the energy-efficient resource allocation problem in the downlink of an Orthogonal Frequency Division Multiple Access (OFDMA) system with multiple users. A iterative algorithm is developed to maximize the overall energy efficiency of the downlink transmission by allocating the resource blocks (RBs) and the transmit power, which is constant for all RBs assigned to a given user. Moreover, we assume that all RBs assigned to the same user must use the same modulation and coding scheme (MCS), as in the LTE specification. In this form our optimization problem belongs to a broad class of problems called Mixed-Integer Nonlinear Fractional Problem (MINLFP), which is very difficult to solve analytically in the original form. Hence, in order to solve it, we propose the iterative algorithm with fast convergence based on the Dinkelbach method. In each iteration, the solution for the power and resource blocks allocation is derived by the numerical method using the Karush-Kuhn-Tucker (KKT) multipliers and KKT conditions, while modulation and coding scheme, for each user, is obtained by the iterative algorithm. Simulation results show that despite the computational simplicity of the proposed solution, it achieves better results than the solutions known from the literature.

In this paper, we deal with the small-scale spectrum aggregation, where a set of even very narrow and disjoint frequency bands closely located on the frequency axis can be utilized simultaneously. Contrarily to our previous work on that subject, we concentrate on the multiuser scenario in which the 5G system tries to share the spectrum interleaved by multiple narrowband signals realized in the protected cellular systems, e.g., 2G/3G systems. We first present the idea of multiuser small scale sharing based on non-contiguous multicarrier (NC-MC) techniques. In our optimization problem we rely on the detailed interference model that takes into account limitations of both transmitter and receiver frequency selectivity, and which has been practically validated. Presented results confirm that proposed solution can provide high throughput even when the 5G system operates in a dense heterogeneous network.

Mobile network operators are required to support a number of users utilizing various Radio Access Technologies (RATs). While operating in a costly, licensed spectrum, the efficiency of its utilization is of crucial importance. This will promote newer RATs, supporting higher spectral efficiency. On the other hand, the quality of service for older RATs' users has to be maintained. This paper proposes a scheduling algorithm, dynamically distributing time-frequency resources between two or more RATs. Interference between RATs is modeled and considered while allocating resources. The algorithm is presented and validated by means of simulations for a coexistence of GSM and LTE systems. This solution is proved to be spectrally efficient in dynamic resource distribution, especially for traffic demand varying in time among different RATs.

New spectrum bands together with flexible spectrum management are treated as one of the key technical enablers for achievement of the so-called key-performance indicators defined for 5G wireless networks. In this paper, we deal with the small-scale spectrum aggregation and sharing, where a set of even very narrow and disjoint frequency bands closely located on the frequency axis can be utilized simultaneously. We first discuss how such a scheme can be applied to various multicarrier systems, focusing on the non-contiguous orthogonal frequency division multiplexing and non-contiguous filter-bank multicarrier technique. We propose an interference model that takes into account the limitations of both transmitter and receiver frequency selectivity, and apply it to our 5G link-optimization framework, what differentiates our work from other standard approaches to link adaptation. We present the results of hardware experiments to validate assumed theoretical interference models. Finally, we solve the optimization problem subject to the constraints of maximum interference induced to the protected legacy systems (GSM and UMTS). Results confirm that small-scale spectrum aggregation can provide high throughput even when the 5G system operates in a dense heterogeneous network.

We consider receiver synchronization in the non-continguous orthogonal frequency division multiplexing (NC-OFDM)-based radio system in the presence of in-band interfering signal, which occupies the frequency-band between blocks of subcarriers (SCs) used by this system, i.e. in-band of NC-OFDM receiver spectrum range. This paper proposes a novel preamble-based synchronization algorithm for estimation of the time and frequency offset based on the received signal cross-correlation with the reference preamble. Contrary to the existing algorithms, it is robust against in-band interference including narrowband interference at the cost of increased complexity. Moreover, in the interference-free system, the probability of frame synchronization error is improved in comparison to all simulated algorithms.

This paper presents a new, low-complexity algorithm for time and frequency synchronization in a non-contiguous-orthogonal frequency division multiplexing (NC-OFDM) radio communication system in the presence of narrowband interference (NBI). This interference scenario is plausible for cognitive radio systems. The computational complexity of the proposed synchronization method is similar to the known Schmidl&Cox algorithm. The algorithm is partially blind, i.e., it does not require information on the NC-OFDM subcarriers used, nor on the center-frequency of the NBI. Moreover, it does not require filtering to remove the NBI. It performs best for the NBI of constant carrier frequency, but it also provides good results for the frequency-modulated interfering signal.

The synchronization at the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) based Cognitive Radio (CR) receiver is a challenge. High-power interference from spatially- and spectrally-neighboring wireless systems may disrupt CR synchronization. In the same time, spectrum shaping techniques might be applied in the CR transmitter to reduce interference generated to other wireless systems. In this paper, we show how they can be used for time synchronization. The proposed Envelope Fluctuation Detection (EFD) method uses time- varying mean sample power of the spectrally shaped NC- OFD waveform. The EFD is more robust to narrowband interference than commonly used autocorrelation-based approach.

In the context of cognitive radio it is important to obtain high spectrum utilization while limiting interference generated to licensed systems. In this paper, new method for the determination of a dynamic spectrum mask is presented that allows for higher flexibility of the cognitive radio links, systems and future standards. The solution maximizes the throughput of the secondary non-contiguous OFDM (NC-OFDM)-based system while limiting the transmission power, interference caused to the primary system and minimum power to be allocated to each OFDM subcarrier for the successful transmission. Below, we investigate coexistence of a secondary NC-OFDM system with primary wireless microphones, although our method can be easily extended to other scenarios and systems.

The paper tackles the problem of efficient reduction of the out-of-band (OOB) radiation in multi-carrier systems when considering the presence of a nonlinear power amplification. It is important to provide “green” transmitters producing less electromagnetic “pollution” out of its nominal frequency band and obtaining at the same time high energy efficiency of the used power amplifier. One of the known methods assumes appropriate modulation of dedicated subcarriers by specially calculated symbols reducing OOB radiation. The solution proposed in this paper combines two approaches, among which the first, called cancellation carriers, is used traditionally for subcarrier sidelobes minimization, and the second, called tone reservation, for peak to average power ratio reduction. The novelty of this work is in joint minimization of both aforementioned metrics in the near optimal way while keeping low computational complexity. The result is seen as a decreased power of the OOB radiation observed after signal passing through the power amplifier. The obtained results confirm that the proposed algorithm outperforms the approaches in which those metrics are minimized separately. Additionally, a number of simulations has been carried out to investigate the influence of the proposed algorithm parameters on the resultant out-of-band radiation performance.

The reduction of spurious emission is an important issue in future OFDM-based systems, especially in the context of cognitive radio, when the primary users must be protected from the interference. In this paper, flexible quasi-systematic precoder is presented for non-contiguous OFDM transmitter, which has the effect of relatively small distortion of the data symbols modulating the subcarriers. We discuss degrees of freedom in the precoding-matrix design. The quasi-systematicy allows for not only the reduction of the out-of-band emission, but also flexible reception with high performance, either with or without the knowledge of the coding-matrix.