Power Control Coefficients Research Articles

Phase Shift Modulation Strategy for Single Stage AC to DC Dual Active Bridge Converter

Energy exchange between AC grid and DC supply that is a part of a hybrid electric micro-grid takes place using various power converter designs. The single-phase, single-stage, AC-DC power dual active bridge converter is one option. The phase-shift modulation is used to regulate energy flow in both directions. The topology of one stage AC-DC dual active bridge converter based in bidirectional switching modules has been introduced. This paper next introduces the analysis of the AC side current considering basic modulation functions and suggests an optimum phase-shifted modulation strategy. The proposed modulation function provides minimum harmonics distortion. A simulation study is presented to compare the proposed strategy to the basic sinusoidal and triangular modulation techniques. The results show that the modified modulation reduces the average THD by about 55% and 39% compared to the standard sinusoidal and triangular modulation strategies respectively and ensures linear relationship between the transferred power and magnitude control coefficient.

Act Before Another Is Aware: Safeguarding Backscatter Systems With Covert Communications

This letter investigates and analyzes covert communications to safeguard backscatter systems, i.e., how to hide backscattering transmission for privacy and security, which is rarely a concern yet. Specifically, we consider a backscatter communication system in the presence of an eavesdropper (Eve), in which a passive tag supported by the power source (Alice) attempts to communicate reliably while simultaneously guaranteeing covertness to Eve. Furthermore, artificial noise (AN) is also injected from Alice to mislead Eve for security purposely. Our objective is to maximize the transmission rate via joint power control, AN injection, and reflection coefficient (RC) adjustment while satisfying the physical constraints on the total power budget, the circuit energy consumption, the minimum transmission rate, and the covertness requirement. To deal with this intractable non-convex problem, we reformulate the problem by utilizing the Kullback-Leibler divergence (KLD) and Lambert W function. Subsequently, an alternative iterative algorithm is designed to solve it. Besides, we derive the closed forms for RC and transmit power for more insight. Simulation results show that the proposed algorithm outperforms the benchmark algorithms in terms of both security and effectiveness.

Multi-stream receive-spatial modulation based cell-free MIMO downlink transmission method

Multi-stream receive-spatial modulation (MSR-SM) technology is applied to the cell-free multiple-input multiple-output (MIMO) downlink transmission, where both users and access points (APs) are multi-antenna, providing a new idea for green and energy-saving wireless communication. The power control coefficients of each AP are obtained using the sequential convex approximation (SCA) algorithm to optimize energy efficiency (EE) under the constraints of the highest transmitting power of the AP and the lowest signal interference-to-noise ratio of the user. This paper proposes an access point selection scheme based on a water-filling algorithm to reduce the number of unnecessary service APs and the total power consumption of the system. The simulation results show that the proposed power allocation algorithm outperforms the max-min power control algorithm in terms of energy efficiency. The MSR-SM-based downlink transmission mode is more energy efficient than the traditional space division multiplexing MIMO digital modulation mode. Furthermore, the SCA algorithm based on AP selection can improve energy efficiency performance compared with the power control algorithm without AP selection.

UAV-Aided Aerial Reconfigurable Intelligent Surface Communications With Massive MIMO System

To capture the advantages of unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies, we propose the use of multiple passive aerial RISs in a massive multiple-input multiple-output (MIMO) network. Each aerial RIS is comprised of a RIS panel attached to a UAV, the intention being to support in extending network coverage from the massive MIMO base station. Compared with stationary RISs, our proposed aerial RISs (termed as UAV-RISs) have the ability to reach more users thanks to the line-of-sight links. Our aim is to maximise the total network throughput by finding the optimal power control coefficients at the base station and the phase shifts of the multiple RISs used in the system. This is jointly solved subject to the power consumption constraints, UAV-RIS deployment, and quality-of-service required at the users. We apply zero-forcing precoding for the beamforming design at the base station, and develop an iterative algorithm based on first-order approximation, block coordinate descent, and alternating optimisation technique. Numerical results demonstrate that our proposed method exhibits low computational-complexity and outperforms benchmark schemes in terms of the total network throughput achieved and improvement for the users with worst-case throughput.

Optimized power control strategy in Massive MIMO for distributed IoT networks

We study a power control strategy for a Massive MIMO enabled IoT system to increase total throughput and to provide better differentiated levels of service to different classes of IoT devices. We use a closed-form signal-to-interference plus noise ratio (SINR) of Massive MIMO and apply the Lagrange multiplier procedure to find the optimum power control coefficients. We investigate several scenarios which include various cell radii and radiation power levels, and verify that the proposed scheme is effective in increasing total throughput and in providing different levels of throughput to different classes of IoT devices. We also apply the power control scheme when each device’s throughput is aggregated from multiple single-antenna access terminals based on the grouping of devices, and compare the cases when the grouping is conducted before and after power control. We show that grouping before power control can provide better differentiated levels of services by allocating different power to each group, while grouping after power control always provides higher total throughput by jointly considering all IoT devices.

Mitigation pilot contamination based on matching technique for uplink cell-free massive MIMO systems

In this paper, the cell-free massive multiple input multiple output (MIMO) network is affected by the pilot contamination phenomenon when a large number of users and a small number of available pilots exists, the quality of service (QoS) will deteriorate due to the low accuracy of the channel estimation because some of users will use the same pilot. Therefore, we address this problem by presenting two novel schemes of pilot assignment and pilot power control design based on the matching technique for the uplink of cell-free massive MIMO systems to maximize spectral efficiency. We first formulate an assignment optimization problem in order to find the best possible pilot sequence to be used by utilizing genetic algorithm (GA) and then propose a Hungarian matching algorithm to solve this formulated problem. Regarding the power control design, we formulate a minimum-weighted assignment problem to assign pilot power control coefficients to the estimated channel’s minimum mean-squared error by considering the access point (AP) selection. Then, we also propose the Hungarian algorithm to solve this problem. Simulation results show that our proposed schemes outperform the state-of-the-art techniques concerning both the pilot assignment and the pilot power control design by achieving a 15% improvement in the spectral efficiency. Finally, the computational complexity analysis is provided for the proposed schemes compared with the state-of-the-art techniques.

Revenue-Maximizing Resource Allocation for Multitenant Cell-Free Massive MIMO Networks

Cell-free massive multiple-input--multiple-output (MIMO) (CF-mMIMO) is a distributed massive MIMO network architecture, which leads to a better ability to resist shadow fading than centralized massive MIMO. When network slicing is applied to CF-mMIMO networks in order to serve multiple tenants, the deployment flexibility of CF-mMIMO networks could be further improved. Nevertheless, how to effectively allocate resources in network slicing based CF-mMIMO networks is still a challenging problem to be tackled. In this article, we present a novel resource allocation scheme for network slicing based multi-tenant CF-mMIMO networks to maximize the infrastructure operator’s revenue while maintaining the quality of network services. Specifically, the resource allocation scheme is first formulated as an optimization problem, which is NP-hard and nonconvex. Then, the problem is decomposed into two subproblems. For the first subproblem, we propose a joint user clustering and power control coefficient allocation algorithm, which not only maintains fairness among users but also improves the access rate of CF-mMIMO networks. For the second subproblem, the interior-point method is adopted. In our article, extensive simulations are carried out to prove the effectiveness of the proposed resource allocation scheme. Our experimental results indicate that the proposed scheme leads to a lower outage probability and a higher infrastructure provider’s revenue than existing approaches.

The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels.

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

Impact of Max-Min Power Control, Channel Estimation and User Grouping Strategies on Uplink Massive MIMO-NOMA Systems

We analyze the uplink (UL) of a non-orthogonal multiple access (NOMA) based massive multiple input multiple output (MIMO) system and deduce new lower bounds on the achievable rate based on zero-forcing (ZF) decoding at the base station. Users are grouped to employ NOMA. In order to cancel the inter-group interference, the ZF decoder is designed as a function of channel estimates acquired based on two low overhead channel estimation schemes, namely, Scheme-I and Scheme-S. Under these estimation schemes, pilots are shared among users in a group while they are orthogonal across groups. User grouping and power allocation are two pivotal ways to regulate the performance of users in a NOMA system. Thus, to ensure uniform quality-of-service to all users, we obtain the max-min power control coefficients which maximize the minimum achievable rate. Furthermore, we investigate two different user grouping strategies, namely, non-scalable near-far grouping and scalable neighbor grouping, and study their impact on the UL max-min achievable rate. We benchmark against the performance obtained with the maximum ratio decoder. Extensive analysis and simulations show that in a substantial portion of the over-loaded regime and the entire under-loaded regime, ZF decoder designed using channel estimates acquired via Scheme-S with near-far grouping gives the highest max-min achievable rate among all the considered decoding and grouping strategies.

SWIPT-Enhanced Cell-Free Massive MIMO Networks

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology to provide near- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">perpetual</i> operation to low-powered wireless devices in Internet-of-Things (IoT)-based wireless networks. In this paper, a SWIPT-enhanced cell-free massive MIMO network is proposed. In such a network, a large set of spatially distributed access points (APs) interconnected via a central processing unit (CPU) can collaboratively serve a large number of both energy harvesting mobile stations (MSs) (requiring wireless energy transfer) and conventional MSs (not requiring wireless energy transfer) on the same time-frequency resources. We consider spatially correlated Rician fading channels and the use of different precoding schemes that are based on different channel estimators differing on the assumed knowledge of the line-of-sight component. Mathematically manageable expressions are derived for the harvested energy during the downlink (DL) energy harvesting phase and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">achievable</i> spectral and energy efficiencies during the uplink (UL) payload transmission phase. A coupled UL/DL optimization problem is formulated aiming at finding the power control coefficients that maximize the minimum of the weighted achievable UL signal-to-interference-plus-noise ratios (SINRs) of all MSs. Extensive numerical results are presented that serve to highlight the existing trade-offs among the achievable spectral and energy efficiencies, the harvested energy, the energy dedicated to UL pilot transmission or the system configuration.

Dynamic Power Control for Cell-Free Industrial Internet of Things With Random Data Arrivals

In this article, we propose an uplink cell-free Industrial Internet of Things (IIoT) framework to support a large number of devices with random data arrivals. By adopting nonorthogonal random pilots and the large-scale fading decoding technique, we derive the closed-form expression of the transmission capacity for each terminal. Considering different statistics of random data arrivals, we formulate a long-term stochastic optimization problem to maximize the minimum time average transmission success ratio (TATSR) through jointly determining the power control coefficients and the combining coefficients for each time period. We reformulate the long-term max–min problem into a sequence of subproblems to minimize the Lyapunov drift plus penalty in each time period. We approximate each mixed integer subproblem as a sigmoid optimization problem, and propose an iterative algorithm by the aid of quadratic transform-based fractional programming and the sequential convex programming to solve it. Simulation results show that our proposed scheme can boost the TATSR.

User‐Centric UAV Group Power Allocation Algorithm

User‐centric unmanned aerial vehicle group (UUAVG) organizes a dynamic user‐centric access point group for each user, which can improve user service quality and throughput. However, in UUAVG, unmanned aerial vehicle user equipment (UUE) and device‐to‐device user equipment (DUE) using the same frequency will cause co‐frequency interference and reduce system performance. In response to this problem, a power allocation algorithm of the two‐level Stackelbreg game (PAOTLSG) is proposed. The algorithm builds a two‐level Stackelberg game model based on UUE and DUE and uses power control coefficients and other parameters to control UAV adjust with the transmit power of DUE to achieve dynamic balance. The optimal transmit power of UAV and DUE is theoretically deduced to achieve the game Nash equilibrium and theoretically prove the existence and uniqueness of the Nash equilibrium solution. The simulation results show that compared with the pricing‐based Stackelbreg game (PSG) algorithm, the PAOTLSG algorithm can effectively increase the throughput by 4.65%.

Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

We consider an uplink cell-free massive multiple-input multiple-output (MIMO) system, in which the access points are connected to the central processing unit (CPU) through a fronthaul network. This system has the advantages of wide coverage and flexible deployment. However, the performance of this system depends on a capacity-limited fronthaul, and when the fronthaul is saturated, the quality of service will be reduced. To address this issue, we propose a joint user clustering and AP selection scheme, which can reduce the pressure on the fronthaul link while taking into account the system performance and computational complexity. We first derive a closed-form expression for the uplink spectral efficiency over Rician fading channels. Based on the derived expression, we formulate the problem of maximizing the minimum uplink spectral efficiency across all users by jointly optimizing the large-scale fading decoding (LSFD) coefficient and power control coefficient. Then, combined with the optimization results and channel estimation error, a suboptimal access point selection scheme is proposed. In addition, we propose a user clustering scheme to further reduce the complexity of the AP selection scheme. The simulation results show that the joint user clustering and access point selection scheme can reduce the system fronthaul link pressure, while the performance degrades only slightly.

Joint Power Control and LSFD for Wireless-Powered Cell-Free Massive MIMO

This paper considers wireless uplink information and downlink power transfer in cell-free massive multiple-input multiple-output systems. The single-antenna user equipments (UEs) utilize the energy harvested in the downlink to transmit uplink pilot and information signals to the multiple-antenna access points (APs). We consider Rician fading and maximum ratio processing based on either linear minimum mean-squared error (LMMSE) or least-squares (LS) channel estimation. We derive the average harvested energy by using a practical non-linear energy harvesting circuit model for both coherent and non-coherent transmission schemes. Furthermore, the uplink spectral efficiency (SE) is derived for all the considered methods and the max-min fairness problem is cast where the optimization variables are the AP and UE power control coefficients together with the large-scale fading decoding vectors. The objective is to maximize the minimum SE of the UEs’ under APs’ and UEs’ transmission power constraints. A novel alternating optimization algorithm with guaranteed convergence and improvement at each step is proposed to solve the highly-coupled non-convex problem.

Pilot Assignment and Power Allocation for Multipair Massive MIMO DF Relaying Networks

This article considers a multi-pair massive multiple-input multiple-output relaying channel where K single-antenna sources communicate with K single-antenna destinations via the assistance of a decode-and-forward relay equipped with massive antenna arrays. The relay station uses maximum-ratio processing to decode the signals transmitted from the sources and precode the signals before broadcasting to the destinations. To perform maximum-ratio processing, the relay needs to know the channels to the sources and the destination. This is done during the training phase where all sources and destinations send pilot sequences to the relay. We consider a very general case in which the pilot sequences are arbitrary. An exact closed-form expression for the end-to-end spectral efficiency is derived taking into account the imperfect channel estimates with arbitrary power control coefficients. From the spectral efficiency formula, we formulate and propose to solve a max-min fairness power allocation. With this power control scheme, the spectral efficiency improves significantly, compared to the uniform power control. In particular, a simple greedy pilot assignment scheme is proposed to reduce the pilot contamination effect, and hence, improve the system performance under the limitations of training duration.

Energy efficiency enhancement of cell‐free massive multiple‐input multiple‐output network employing threshold‐based beamforming

AbstractThe downlink of cell‐free massive multiple‐input multiple‐output network is considered. It is assumed that both access points (APs) and users are equipped with multiple antennas. A heuristic threshold‐based‐beamforming (TBF) protocol is proposed, which performs an AP selection algorithm to significantly improve the total energy efficiency (EE) of the network. The beamforming method that fits the model is the maximum eigenmode beamforming (MEB). The threshold level is determined in such a way that the strongest channel eigenmodes contribute in the downlink transmissions. Indeed, power control coefficients are chosen in such a way that the number of users served by each AP is limited according to the TBF‐based AP selection scheme. The spectral efficiency of the proposed method is investigated considering both perfect and imperfect knowledge of the channel state information at transmitters. Simulation results show that the proposed TBF protocol greatly enhances the total EE of the network compared to the MEB, conjugate beamforming, and zero‐force precoding techniques. Furthermore, a modification algorithm is proposed to obtain the minimum per‐user quality of service requirement, which prevents the dramatic reduction in the EE caused by high inter‐user interference levels.

Spectral and Energy Efficiency in Cell-Free Massive MIMO Systems Over Correlated Rician Fading

In this article, we investigate the downlink (DL) of a cell-free massive multiple-input multiple-output (MIMO) system over spatially correlated Rician fading, in which many distributed access points (APs) equipped with multiple antennas serve single-antenna users. The APs apply minimum mean-square error channel estimation to obtain the uplink channel state information (CSI). Furthermore, in order to obtain DL CSI at users, this article considers the use of maximum-ratio transmission to beamform DL pilots in the DL beamforming training (BT) phase. For such a system, we derive the closed-form expressions of the sum spectral efficiency (SE) and total energy efficiency (EE). Based on the obtained closed-form expressions, we develop two successive approximation algorithms to improve the sum SE and total EE by optimizing the power control coefficients of DL data and pilot. Numerical results are provided to demonstrate the superiority of the proposed algorithms in improving the sum SE and total EE. In addition, the numerical results also show that the sum SE of a cell-free massive MIMO system with exploiting the BT scheme can be significantly improved over the system without employing the BT scheme.

Wirelessly Powered Cell-Free IoT: Analysis and Optimization

In this article, we propose a wirelessly powered Internet-of-Things (IoT) system based on the cell-free massive MIMO technology. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. Collocated massive MIMO and small-cell IoT can be treated as special cases of cell-free IoT. We derive the tight closed-form lower bound on the amount of harvested energy, and the closed-form expression of SINR as the metrics of power transfer and data transmission, respectively. To improve energy efficiency, we jointly optimize the uplink and downlink power control coefficients to minimize the total transmit energy consumption while meeting the target SINRs. Extended simulation results show that cell-free IoT outperforms collocated massive MIMO and small-cell IoT in terms of both downlink and uplink 95% likely performances. Moreover, significant gains can be achieved by the proposed joint power control in terms of both per user throughput and energy consumption.

Long-Term Scheduling and Power Control for Wirelessly Powered Cell-Free IoT

We investigate the long-term scheduling and power control scheme for a wirelessly powered cell-free Internet-of-Things (IoT) network which consists of distributed access points (APs) and a large number of sensors. In each time slot, a subset of sensors is scheduled for uplink data transmission or downlink power transfer. Through asymptotic analysis, we obtain closed-form expressions for the harvested energy and the achievable rates that are independent of random pilots. Then, using these expressions, we formulate a long-term scheduling and power control problem to maximize the minimum time-average achievable rate among all sensors while maintaining the battery state of each sensor higher than a predefined minimum level. Using Lyapunov optimization, the transmission mode, the active sensor set, and the power control coefficients for each time slot are jointly determined. Finally, simulation results validate the accuracy of our derived closed-form expressions and reveal that the minimum time-average achievable rate is boosted significantly by the proposed scheme compared with the simple greedy transmission scheme.

BS-UE Association and Power Allocation in Heterogeneous Massive MIMO Systems

We consider a downlink Heterogeneous Massive Multiple-Input Multiple-Output (HM-MIMO) system with the macro-base station (BS) having hundreds of antennas and each of the micro-BSs having tens of antennas. Two lower bounds and one approximation of the achievable per-user equipment (UE) rate in closed forms under this system are derived and compared in terms of network utility. Using the derived approximation, three BS-UE association approaches are proposed. Firstly, a simple, sub-optimal, and heuristic multiple BS-UE association approach is designed, which achieves around 25% utility performance improvement compared with the conventional maximum received signal strength (Max-RSS) approach. Secondly, based on the results given by this heuristic approach, a learning approach for BS-UE association using convolutional neural network (CNN) is introduced. After being fully trained, the CNN can take any new BS-UE configuration as input and provide a sub-optimal BS-UE association for that configuration directly. It has only a small performance degradation compared with the proposed heuristic approach. Thirdly, realizing that the BS-UE connection probability in the proposed CNN architecture can be considered as a power allocation ratio, a combined power allocation and association approach is proposed. Its performance achieves as high as 60% utility improvement compared with the Max-RSS association and is also comparable to that achieved by the max-min power allocation approach which requires more than $10000\times $ running time. It is remarkable that by using the gradients of the derived achievable per-UE rate approximation with respect to the power control coefficients, accurate target data is in fact not required for training in this approach.