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Energy-Efficient Wireless Communication Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (25 July 2021) | Viewed by 21639

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Special Issue Editors

Dr. Telmo Cunha

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Department of Electronics, Telecommunications and Informatics, Universidade de Aveiro—Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Interests: application of system-level modeling and system identification techniques for improving the performance of wireless transmitters
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Thomas Eriksson

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Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
Interests: statistical signal processing and communication

Dr. Pere L. Gilabert

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Castelldefels School of Telecommunications and Aerospace Engineering (EETAC), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
Interests: linearization techniques and digital signal processing solutions for highly efficient transmitter architectures

Special Issue Information

Dear Colleagues,

Wireless telecommunication systems are stepping through significant evolutionary modifications, driven as always by application needs which constantly demand higher bitrates. One key factor is now deserving particular attention: energy saving. More than ever, energy efficiency specifications and figures are in the spotlight of the processes determining the evolution of telecom systems, in line with the worldwide social and economic awareness towards energy. Cellular networks are fast moving toward solutions where energy beams are directed towards handset units, instead of widespread energy in all directions; power amplifiers are driven into new architectures and design techniques that aim to reduce the dissipation loss while maintaining the desired delivered power; signal processing techniques are also evolving to permit energy-efficient transmitter operation within the specifications; sensor networks foresee the deployment of very-low-power transmitter/receiver units, supporting an ever-growing range of applications; and in many other fields of wireless communications we see a trend driven by energy saving.

This Special Issue is therefore focused on topics related to energy efficiency in wireless communication systems, including but not limited to:

Transmitter architecture design for efficiency improvement;
Power-efficient amplifier design techniques;
MIMO and mMIMO system innovation;
Power reutilization and harvesting;
Techniques for maintaining QoS in energy-efficient communication networks;
Signal processing techniques to enhance the performance of power-efficient transmitters;
Network mechanisms driven for energy conservation;
5G and beyond;
Very-low-power sensor networks;
Modulation techniques and algorithms for enhancing energy efficiency;
…

Dr. Telmo Cunha
Guest Editor

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Published Papers (7 papers)

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Research

13 pages, 1977 KiB

Open AccessArticle

A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters

by Carlos Crespo-Cadenas, María J. Madero-Ayora and Juan A. Becerra

Sensors 2021, 21(17), 5897; https://doi.org/10.3390/s21175897 - 2 Sep 2021

Cited by 4 | Viewed by 2225

Abstract

The operation of the power amplifier (PA) in wireless transmitters presents a trade-off between linearity and power efficiency, being more efficient when the device exhibits the highest nonlinearity. Its modeling and linearization performance depend on the quality of the underlying Volterra models that are characterized by the presence of relevant terms amongst the enormous amount of regressors that these models generate. The presence of PA mechanisms that generate an internal state variable motivates the adoption of a bivariate Volterra series perspective with the aim of enhancing modeling capabilities through the inclussion of beneficial terms. In this paper, the conventional Volterra-based models are enhanced by the addition of terms, including cross products of the input signal and the new internal variable. The bivariate versions of the general full Volterra (FV) model and one of its pruned versions, referred to as the circuit-knowledge based Volterra (CKV) model, are derived by considering the signal envelope as the internal variable and applying the proposed methodology to the univariate models. A comparative assessment of the bivariate models versus their conventional counterparts is experimentally performed for the modeling of two PAs driven by a 30 MHz 5G New Radio signal: a class AB PA and a class J PA. The results for the digital predistortion of the class AB PA under a direct learning architecture reveal the benefits in linearization performance produced by the bivariate CKV model structure compared to that of the univariate CKV model. Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Communication Systems)

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28 pages, 3614 KiB

Open AccessArticle

Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization

by Abdoul Barry, Wantao Li, Juan A. Becerra and Pere L. Gilabert

Sensors 2021, 21(17), 5772; https://doi.org/10.3390/s21175772 - 27 Aug 2021

Cited by 13 | Viewed by 3339

Abstract

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction. Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Communication Systems)

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14 pages, 579 KiB

Open AccessArticle

Low Spatial Peak-to-Average Power Ratio Transmission for Improved Energy Efficiency in Massive MIMO Systems

by Sina Rezaei Aghdam and Thomas Eriksson

Sensors 2021, 21(16), 5534; https://doi.org/10.3390/s21165534 - 17 Aug 2021

Viewed by 2027

Abstract

A significant portion of the operating power of a base station is consumed by power amplifiers (PAs). Much of this power is dissipated in the form of heat, as the overall efficiency of currently deployed PAs is typically very low. This is because the structure of conventional precoding techniques typically results in a relatively high variation in output power at different antennas in the array, and many PAs are operated well below saturation to avoid distortion of the transmitted signals. In this work, we use a realistic model for power consumption in PAs and study the impact of power variation across antennas in the array on the energy efficiency of a massive MIMO downlink system. We introduce a family of linear precoding matrices that allow us to control the spatial peak-to-average power ratio by projecting a fraction of the transmitted power onto the null space of the channel. These precoding matrices preserve the structure of conventional precoders; e.g., they suppress multiuser interference when used together with zeroforcing precoding and bring advantages over these precoders by operating PAs in a more power-efficient region and reducing the total radiated distortion. Our numerical results show that by controlling the power variations between antennas in the array and incorporating the nonlinearity properties of PA into the precoder optimization, significant gains in energy efficiency can be achieved over conventional precoding techniques. Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Communication Systems)

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18 pages, 2224 KiB

Open AccessArticle

Upgrading Behavioral Models for the Design of Digital Predistorters

by Carlos Crespo-Cadenas, María José Madero-Ayora and Juan A. Becerra

Sensors 2021, 21(16), 5350; https://doi.org/10.3390/s21165350 - 8 Aug 2021

Cited by 2 | Viewed by 1875

Abstract

This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD). These incomplete structures are the consequence of nonlinear order and memory depth model truncation with the purpose of reducing the demand of the limited computational resources available in standard processors. On the other hand, the alternative use of model structures pruned a priori does not guarantee that every significant term is included. To improve the limited performance of an incomplete model, a general procedure to augment its structure by incorporating significant terms is demonstrated. The sparse nature of the problem allows a successive search incorporating additional terms with higher nonlinear order and memory depth. This approach is investigated in the modeling and linearization of a commercial class AB PA operating at a compression point of about 6 dB, and a class J PA operating near saturation. Results highlight the capabilities of this upgrading procedure in the improvement of linearization capabilities of DPDs. Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Communication Systems)

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21 pages, 1382 KiB

Open AccessArticle

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework

by Juan Pablo García-Martín and Antonio Torralba

Sensors 2021, 21(11), 3718; https://doi.org/10.3390/s21113718 - 26 May 2021

Cited by 14 | Viewed by 4844

Abstract

With the development of the Internet of Things (IoT), Low Data Rate-Personal Area Networks (LR-WPAN) have been deployed for different applications. Now comes the need to integrate these networks in search of greater connectivity, performances, and geographic coverage. This integration is facilitated by the recent deployment of low power wide area networks (LPWAN) in the licensed bands, especially narrowband IoT (NB-IoT) and long-term evolution for machine-type communications (LTE-M), which are standardized technologies that will continue evolving as part of the fifth generation (5G) specifications. This paper proposes a design methodology for combined networks using LR-WPAN and LPWAN technologies. These networks are combined at the device level using a cluster-tree topology. An example is shown here, where an existing IEEE 802.15.4 network is combined with NB-IoT. To this end, new dual nodes are incorporated, acting as cluster heads. The paper discusses the different aspects of formation and operation of the combined network. A dynamic link selection (DLS) algorithm is also proposed, based on which cluster headers dynamically determine the preferred link, depending on link quality and type of traffic. Extensive simulations show that the DLS algorithm significantly increases battery life on dual nodes, which are the nodes with the highest power demands. Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Communication Systems)

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20 pages, 4218 KiB

Open AccessArticle

Machine-Learning Assisted Optimisation of Free-Parameters of a Dual-Input Power Amplifier for Wideband Applications

by Teng Wang, Wantao Li, Roberto Quaglia and Pere L. Gilabert

Sensors 2021, 21(8), 2831; https://doi.org/10.3390/s21082831 - 17 Apr 2021

Cited by 9 | Viewed by 3211

Abstract

This paper presents an auto-tuning approach for dual-input power amplifiers using a combination of global optimisation search algorithms and adaptive linearisation in the optimisation of a multiple-input power amplifier. The objective is to exploit the extra degrees of freedom provided by dual-input topologies to enhance the power efficiency figures along wide signal bandwidths and high peak-to-average power ratio values, while being compliant with the linearity requirements. By using heuristic search global optimisation algorithms, such as the simulated annealing or the adaptive Lipschitz Optimisation, it is possible to find the best parameter configuration for PA biasing, signal calibration, and digital predistortion linearisation to help mitigating the inherent trade-off between linearity and power efficiency. Experimental results using a load-modulated balanced amplifier as device-under-test showed that after properly tuning the selected free-parameters it was possible to maximise the power efficiency when considering long-term evolution signals with different bandwidths. For example, a carrier aggregated a long-term evolution signal with up to 200 MHz instantaneous bandwidth and a peak-to-average power ratio greater than 10 dB, and was amplified with a mean output power around 33 dBm and 22.2% of mean power efficiency while meeting the in-band (error vector magnitude lower than 1%) and out-of-band (adjacent channel leakage ratio lower than −45 dBc) linearity requirements. Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Communication Systems)

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18 pages, 1108 KiB

Open AccessArticle

Energy Efficiency Maximization for Multi-Cell Multi-Carrier NOMA Networks

by Abuzar B. M. Adam, Xiaoyu Wan and Zhengqiang Wang

Sensors 2020, 20(22), 6642; https://doi.org/10.3390/s20226642 - 20 Nov 2020

Cited by 10 | Viewed by 2689

Abstract

As energy efficiency (EE) is a key performance indicator for the future wireless network, it has become a significant research field in communication networks. In this paper, we consider multi-cell multi-carrier non-orthogonal multiple access (MCMC-NOMA) networks and investigate the EE maximization problem. As the EE maximization is a mixed-integer nonlinear programming NP-hard problem, it is difficult to solve directly by traditional optimization such as convex optimization. To handle the EE maximization problem, we decouple it into two subproblems. The first subproblem is user association, where we design a matching-based framework to perform the user association and the subcarriers’ assignment. The second subproblem is the power allocation problem for each user to maximize the EE of the systems. Since the EE maximization problem is still non-convex with respect to the power domain, we propose a two stage quadratic transform with both a single ratio quadratic and multidimensional quadratic transform to convert it into an equivalent convex optimization problem. The power allocation is obtained by iteratively solving the convex problem. Finally, the numerical results demonstrate that the proposed method could achieve better EE compared to existing approaches for non-orthogonal multiple access (NOMA) and considerably outperforms the fractional transmit power control (FTPC) scheme for orthogonal multiple access (OMA). Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Communication Systems)

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