Cell-free Massive MIMO and SWIPT: Access Point Operation Mode Selection and Power Control

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems incorporating simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. To optimize both the spectral efficiency (SE) of IUs and harvested energy (HE) of EUs, we propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. We investigate the problem of maximizing the total HE for EUs, considering constraints on SE for individual IUs and minimum HE for individual EUs. Our numerical results showcase that the proposed AP operation mode selection algorithm can provide up to $76\%$ and $130\%$ performance gains over random AP operation mode selection with and without power control, respectively.Comment: 6 pages, 2 figures, to be presented at GLOBECOM 2023, Kuala Lumpu

Intelligent Reflecting Surface Aided Wireless Power Transfer With a DC-Combining Based Energy Receiver and Practical Waveforms

This paper studies intelligent reflecting surface (IRS) aided wireless power transfer (WPT) to batteryless Internet of Everything (IoE) devices. A practical energy receiver (ER) with multiple antennas is investigated. Multiple RF energy flows gleaned by all the receive antennas are input multiple energy harvesters, which are further rectified to direct-current (DC) energy. The resultant multiple DC energy flows are then combined in the DC domain for energy storage. Three classic waveforms, namely deterministic waveform, M-QAM waveform, and Gaussian waveform, are considered for WPT. We maximize the output DC power by jointly designing the active transmit beamformer of the transmitter and the passive reflecting beamformer of the IRS with the above-mentioned waveforms, respectively, subject to the transmit power constraint at the transmitter and to the limited resolution constraints on the phase-shifters of the IRS. A low complexity alternating optimization (AO) algorithm is proposed, which converges to a Karush-Kuhn-Tucker (KKT) point and thus results in a locally optimal solution. The numerical results demonstrate that the Gaussian waveform has the best energy performance with a low input RF power to the energy harvesters. By contrast, the deterministic waveform becomes superior with a high input RF power to the energy harvesters

CURE: Enabling RF Energy Harvesting using Cell-Free Massive MIMO UAVs Assisted by RIS

The ever-evolving internet of things (IoT) has led to the growth of numerous wireless sensors, communicating through the internet infrastructure. When designing a network using these sensors, one critical aspect is the longevity and self-sustainability of these devices. For extending the lifetime of these sensors, radio frequency energy harvesting (RFEH) technology has proved to be promising. In this paper, we propose CURE, a novel framework for RFEH that effectively combines the benefits of cell-free massive MIMO (CFmMIMO), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs) to provide seamless energy harvesting to IoT devices. We consider UAV as an access point (AP) in the CFmMIMO framework. To enhance the signal strength of the RFEH and information transfer, we leverage RISs owing to their passive reflection capability. Based on an extensive simulation, we validate our framework’s performance by comparing the max-min fairness (MMF) algorithm for the amount of harvested energy.This is a manuscript of a proceeding published as Khalil, Alvi Ataur, Mohamed Y. Selim, and Mohammad Ashiqur Rahman. "CURE: Enabling RF Energy Harvesting using Cell-Free Massive MIMO UAVs Assisted by RIS." The 46th IEEE Conference on Local Computer Networks (LCN), Edmonton, Alberta, Canada. October 4-7, 2021. Posted with permission.</p

Hardware-Impaired Rician-Faded Cell-Free Massive MIMO Systems With Channel Aging

We study the impact of channel aging on the uplink of a cell-free (CF) massive multiple-input multiple-output (mMIMO) system by considering i) spatially-correlated Rician-faded channels; ii) hardware impairments at the access points and user equipments (UEs); and iii) two-layer large-scale fading decoding (LSFD). We first derive a closed-form spectral efficiency (SE) expression for this system, and later propose two novel optimization techniques to optimize the non-convex SE metric by exploiting the minorization-maximization (MM) method. The first one requires a numerical optimization solver, and has a high computation complexity. The second one with closed-form transmit power updates, has a trivial computation complexity. We numerically show that i) the two-layer LSFD scheme effectively mitigates the interference due to channel aging for both low- and high-velocity UEs; and ii) increasing the number of AP antennas does not mitigate the SE deterioration due to channel aging. We numerically characterize the optimal pilot length required to maximize the SE for various UE speeds. We also numerically show that the proposed closed-form MM optimization yields the same SE as that of the first technique, which requires numerical solver, and that too with a much reduced time-complexity.Comment: This work has been submitted to the IEEE Transactions on Communications for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible, 32 pages, 14 figure

Leveraging UAVs for 6G Networks

Advancing towards 6G networks emphasizes integrating communication with sensing functionalities, promising unparalleled connectivity, efficiency, and intelligence in forthcoming networks. In this context, uncrewed aerial vehicles (UAVs) emerge as pivotal assets, offering versatile solutions for both communication and sensing tasks. Leveraging their mobility and flexibility, optimizing the UAV deployment or trajectory can enhance the network performance to meet the new demands.Anticipating next-generation 6G networks, traditional cellular architectures' constraints have led to the exploration of new network topologies, including cell-free architectures. These architectures abandon the concept of cell, allowing users to connect to multiple base stations and mitigating the effects of cellular boundaries for fairer scenarios. Combining cell-free architectures with UAVs offers substantial performance gains by leveraging UAV adaptability for dynamic coverage and capacity optimization. To fully leverage this potential, we propose a comprehensive framework for cell-free UAV networks. Initially, UAVs operate as flying base stations within a framework of perfect fronthaul connectivity. This paradigm is extended to accommodate wireless fronthaul scenarios, prompting UAVs to function as flying relays instead of flying base stations.Moreover, UAVs hold significant potential beyond their role in communication. Equipped with sensors and video cameras, UAVs can serve a dual purpose, enabling efficient data collection and sensing tasks. One critical application is wildfire tracking, addressing the pressing need for early detection and monitoring of wildfires. With the escalating frequency and intensity of wildfires globally, efficient wildfire tracking has become imperative for mitigating their devastating impact. Integrating the strengths of cell-free UAV networks with artificial intelligence, our aim is to optimize UAV trajectories to achieve two primary objectives: (i) cover the fire perimeter with cameras and (ii) ensure reliable transmission of captured images to the network. This design significantly enhances resilience, allowing UAVs to transmit images even if certain base stations are compromised by fire incidents. However, the complexity of the overall problem presents a challenge, leading to the utilization of reinforcement learning in this scenario.In addition to the aforementioned applications in cell-free networks and wildfire tracking, this dissertation also explores similar scenarios with cellular connectivity. This includes exploring the integration of communication, sensing, and data collection functionalities withintraditional cellular networks. These methodologies, which involve optimizing trajectories via traditional techniques or more sophisticated such as reinforcement learning, contribute to enhancing the efficiency and reliability of cellular networks as well

Power control with Machine Learning Techniques in Massive MIMO cellular and cell-free systems

This PhD thesis presents a comprehensive investigation into power control (PC) optimization in cellular (CL) and cell-free (CF) massive multiple-input multiple-output (mMIMO) systems using machine learning (ML) techniques. The primary focus is on enhancing the sum spectral efficiency (SE) of these systems by leveraging various ML methods. To begin with, it is combined and extended two existing datasets, resulting in a unique dataset tailored for this research. The weighted minimum mean square error (WMMSE) method, a popular heuristic approach, is utilized as the baseline method for addressing the sum SE maximization problem. It is compared the performance of the WMMSE method with the deep Q-network (DQN) method through training on the complete dataset in both CL and CF-mMIMO systems. Furthermore, the PC problem in CL/CF-mMIMO systems is effectively tackled through the application of ML-based algorithms. These algorithms present highly efficient solutions with significantly reduced computational complexity [3]. Several ML methods are proposed for CL/CF-mMIMO systems, tailored explicitly to address the PC problem in CL/CF-mMIMO systems. Among them are the innovative proposed Fuzzy/DQN method, proposed DNN/GA method, proposed support vector machine (SVM) method, proposed SVM/RBF method, proposed decision tree (DT) method, proposed K-nearest neighbour (KNN) method, proposed linear regression (LR) method, and the novel proposed fusion scheme. The fusion schemes expertly combine multiple ML methods, such as system model 1 (DNN, DNN/GA, DQN, fuzzy/DQN, and SVM algorithms) and system model 2 (DNN, SVM-RBF, DQL, LR, KNN, and DT algorithms), which are thoroughly evaluated to maximize the sum spectral efficiency (SE), offering a viable alternative to computationally intensive heuristic algorithms. Subsequently, the DNN method is singled out for its exceptional performance and is further subjected to in-depth analysis. Each of the ML methods is trained on a merged dataset to extract a novel feature vector, and their respective performances are meticulously compared against the WMMSE method in the context of CL/CF-mMIMO systems. This research promises to pave the way for more robust and efficient PC solutions, ensuring enhanced SE and ultimately advancing the field of CL/CF-mMIMO systems. The results reveal that the DNN method outperforms the other ML methods in terms of sum SE, while exhibiting significantly lower computational complexity compared to the WMMSE algorithm. Therefore, the DNN method is chosen for examining its transferability across two datasets (dataset A and B) based on their shared common features. Three scenarios are devised for the transfer learning method, involving the training of the DNN method on dataset B (S1), the utilization of model A and dataset B (S2), and the retraining of model A on dataset B (S3). These scenarios are evaluated to assess the effectiveness of the transfer learning approach. Furthermore, three different setups for the DNN architecture (DNN1, DNN2, and DNN3) are employed and compared to the WMMSE method based on performance metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the research evaluates the impact of the number of base stations (BSs), access points (APs), and users on PC in CL/CF-mMIMO systems using ML methodology. Datasets capturing diverse scenarios and configurations of mMIMO systems were carefully assembled. Extensive simulations were conducted to analyze how the increasing number of BSs/APs affects the dimensionality of the input vector in the DNN algorithm. The observed improvements in system performance are quantified by the enhanced discriminative power of the model, illustrated through the cumulative distribution function (CDF). This metric encapsulates the model's ability to effectively capture and distinguish patterns across diverse scenarios and configurations within mMIMO systems. The parameter of the CDF being indicated is the probability. Specifically, the improved area under the CDF refers to an enhanced probability of a random variable falling below a certain threshold. This enhancement denotes improved model performance, showcasing a greater precision in predicting outcomes. Interestingly, the number of users was found to have a limited effect on system performance. The comparison between the DNN-based PC method and the conventional WMMSE method revealed the superior performance and efficiency of the DNN algorithm. Lastly, a comprehensive assessment of the DNN method against the WMMSE method was conducted for addressing the PC optimization problem in both CL and CF system architectures. In addition to, this thesis focuses on enhancing spectral efficiency (SE) in wireless communication systems, particularly within cell-free (CF) mmWave massive MIMO environments. It explores the challenges of optimizing SE through traditional methods, including the weighted minimum mean squared error (WMMSE), fractional programming (FP), water-filling, and max-min fairness approaches. The prevalence of access points (APs) over user equipment (UE) highlights the importance of zero-forcing precoding (ZFP) in CF-mMIMO. However, ZFP faces issues related to channel aging and resource utilization. To address these challenges, a novel scheme called delay-tolerant zero-forcing precoding (DT-ZFP) is introduced, leveraging deep learning-aided channel prediction to mitigate channel aging effects. Additionally, a cutting-edge power control (PC) method, HARP-PC, is proposed, combining heterogeneous graph neural network (HGNN), adaptive neuro-fuzzy inference system (ANFIS), and reinforcement learning (RL) to optimize SE in dynamic CF mmWave-mMIMO systems. This research advances the field by addressing these challenges and introducing innovative approaches to enhance PC and SE in contemporary wireless communication networks. Overall, this research contributes to the advancement of PC optimization in CL/CF-mMIMO systems through the application of ML techniques, demonstrating the potential of the DNN method, and providing insights into system performance under various scenarios and network configurations