Trajectory Optimization and Power Allocation Algorithm in MBS-Assisted Cell-Free Massive MIMO Systems

We study support for unmanned aerial vehicle (UAV) communications through a cell-freemassive MIMO architecture, wherein a large number of access points (APs) is deployed in place of largeco-located massive MIMO arrays. We consider also a variation of the pure cell-free architecture by applyinga user-centric association approach, where each user is served only from a subset of APs in the network.Under the general assumption that the propagation channel between the mobile stations, either UAVs orground users (GUEs), and the APs follows a Ricean distribution, we derive closed form spectral efficiencylower bounds for uplink and downlink with linear minimum mean square error channel estimation. Weconsider several power allocation and user scheduling strategies for such a system, and, among these,also minimum-rate maximizing power allocation strategies to improve the system fairness. Our numericalresults reveal that cell-free massive MIMO architecture and its low-complexity user-centric alternativemay provide better performance than a traditional multi-cell massive MIMO network deployment.

This paper investigates the secure communication in reconfigurable intelligent surface aided cell-free massive MIMO system in the presence of active eavesdropping, where the access points (APs) are equipped with low resolution analogto-digital/digital-to-analog converters (ADCs/DACs). Specifically, the additive quantization noise model (AQNM) is explored to capture the impacts of coarse ADCs/DACs. To reduce the channel estimation overhead, all APs employ a minimum mean-squared-error channel estimator which estimates the aggregated channel including both the direct and indirect channels. With the available imperfect channel state information (CSI), conjugate beamforming and random beamforming are applied at the APs and the RISs for downlink data transmission. Moreover, the closed-form expression of the achievable ergodic secrecy rate is derived which can be used to evaluate the impact of the number of the APs, the number of the RISs and the ADCs/DACs resolution on the systems performance. Finally, numerical and simulation results are presented to verify the analytical results.

The sixth generation (6G) of wireless communication networks should deliver higher data rates and spectral efficiency for novel demands and utilizations. Cell free massive MIMO is one promising technique to achieve these goals. Distribution of base stations (BSs) in this system creates more efficiency and better coverage of users than the conventional cellular system. On the other hand, deep learning methods have been proven to be competitive frameworks in comparison of traditional optimization approaches in wireless communication applications. In this paper, power allocation in cell free massive MIMO system in uplink transmission is investigated. Firstly, we achieve lower bound of spectral efficiency, which is valid for all decoders. Besides, we evaluate MAX- MIN power allocation problem from the perspective of deep learning and common optimization methods. The simulation results confirm the superior performance of the deep learning compared to traditional optimization methods. Furthermore, computations complexity of power allocation in the deep learning case are extremely low.

This paper presents the first performance analysis of physical layer downlink secure transmissions in a scalable cell-free massive MIMO (SCF-mMIMO) system. A stochastic geometry approach is used to model the locations of the access points (APs), user equipments (UEs) and eavesdroppers (Eves) as independent homogeneous Poisson point processes (HPPPs). In addition to applying maximum ratio transmission (MRT) to send the confidential messages, null-space artificial noise is also injected for secrecy enhancement. We analytically characterize the secrecy performance in terms of both the outage-based secrecy transmission rate (STR) and the ergodic secrecy rate (ESR), appropriate for slow quasi-static fading channels and fast block-fading channels, respectively. By utilizing moment matching and Gil-Pelaez inversion theorem, we are able to obtain mathematically tractable approximations for the performance metrics. These approximations are shown to have high accuracy as compared to simulation results. Our numerical results reveal useful design insights that cannot be inferred from existing studies. These insights answer important questions such as whether it is best to deploy as many APs each with fewer antennas and to what extent the artificial noise insertion is beneficial.

We consider a cell-free massive MIMO system with multi-antennas at both access points (APs) and users. Then, the uplink spectral efficiency (SE) of this system with data power control at users and zero-forcing (ZF) combining at the APs is exploited. The numerical results show that, the SE of cell-free massive MIMO with ZF combining outperforms the system with maximum ratio (MR) combining. Moreover, additional antennas at users improves the system's SE when there are some active users in the system.

Attack from malicious access points in cell-free massive MIMO systems: Performance analysis and countermeasure

Since the energy efficiency is affected by multiple system parameters in Cell-Free massive MIMO system, it is difficult to get the global optimal solution. In this paper, we propose a heuristic algorithm to iteratively improve the system sum-rate with a grouping sorting pilot allocation algorithm based on channel similarity and geographical location and reduce the total power consumption by an AP selection and power allocation algorithm, so as to improve energy efficiency. We also propose an algorithm to optimize the uplink pilot power based on the Lagrange dual gradient descent method with the goal of maximizing the sum of all users' channel estimation variances to further improve energy efficiency. Simulation results show that compared with the optimal algorithm that only optimizes AP selection and power allocation, the proposed scheme can improve energy efficiency by 43.66%.

Training sequence is used in multiple antenna systems to estimate channel state information and mitigate channel distortion between transmitter and receiver. However, the training sequence or pilot must be limited to a certain size in order to reduce the impact of overhead loss due to limited channel coherence length in mobile users. In this paper, we proposed to use training size optimization in cell-free massive MIMO system. In addition, we proposed and compared the performance of different training size optimization algorithms, namely exhaustive search optimization, bisection optimization and min–max optimization, with each method has different level of calculation complexities. The results showed that in general, all of the 3 training length optimization methods improved the downlink rate compared to the conventional pilot length method. We also showed that the training optimization methods are more effective when the coherence length is small or the number of users is very large. In the case of large number of users or small coherence length, the exhaustive search has the best median downlink rate, followed closely by min–max optimum and finally the bisection method. Even though the exhaustive search optimization has the best downlink rate, we showed that the proposed reduce optimization complexity methods has significantly less calculation complexity. In addition, the median downlink rate performance of min–max optimization method is only slightly less than that of the exhaustive search method for various number of users and coherence length.

A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.

In this paper we consider cell-free (CF) massive MIMO (MaMIMO) systems, which comprise a very large number of geographically distributed access points (APs) serving a much smaller number of users. We exploit channel sparsity to tackle pilot contamination, which originates from the reuse of pilot sequences. Specifically, we consider semi-blind methods for joint channel estimation and data detection. Under the challenging assumption of deterministic parameters, we determine sufficient conditions and necessary conditions for semi-blind identifiability, which guarantee the non-singularity of the Fisher Information Matrix (FIM) and the existence of the Cramer-Rao bound (CRB). We propose a message passing (MP) algorithm which determines the exact channel coefficients in the case of semiblind identifiability. We show that the system is identifiable if the Karp-Sipser algorithm yields an empty core. Additionally, we propose a Bayesian semi-blind approach which results in an effective algorithm for joint channel estimation and multi-user detection. This algorithm alternates between channel estimation and linear multi-user detection. Numerical simulations verify the analytical derivations.

In recent years cell-free massive multiple-input multiple-output system has attracted considerable attention for promising large spectral and power efficiency. However, pilot contamination, a well-known challenge in cell-free massive MIMO, limits these aspired gains. Alongside, and since 2000, Location Based Services (LBS) have rapidly emerged and enabled users to know their current location information for various purposes. In order to reduce pilot contamination and improve system performance in cell-free massive MIMO, this paper proposes an efficient location-based greedy pilot assignment (LBGPA) algorithm. Specifically, by following the principle that two users in close vicinity of each other are not assigned the same pilot sequence and utilizing the advantage of greedy pilot assignment (GPA), the pilot contamination can be reduced significantly. Numerical experiments show that the proposed algorithm outperforms other conventional pilot assignment schemes in typical cell-free massive MIMO systems.

Cell-free massive MIMO (mMIMO) is expected to be a novel network architecture to support massive connections, ultra-high transmission rate, and ultra-reliability low-latency transmission. Therefore, applications in ultra-reliable low-latency communications (URLLC) can be served by the cell-free mMIMO network in the future. This paper considers a multiuser downlink cell-free massive MIMO communication system and defines two events to evaluate the system’s failure in latency and reliability performance of the transmission. The first kind of event is defined as Transmission Error, where the value of the downlink rate becomes too high to be supported by the channel between the access points (APs) and user equipments (UEs), while the second event is defined as Time Overflow, where the time of packet delivery violates the hard deadline. The probabilities of these events can help evaluate the URLLC performance of the system because the probability of Transmission Error reflects the reliability of the transmission while the probability of Time Overflow reflects the latency of the transmission. We derive the expressions of the probabilities of these two events and the final probability system outage, with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding, based on the properties of Gamma distribution and the approximation for non-isotropic vectors. We finally provide the numerical results by system-level simulations to show that the expressions are accurate and discuss the influence of the noise variance, the number of UEs, and the precoding scheme on the URLLC performance.

We consider the problem of joint beamforming and access point (AP)-user association for downlink millimeter Wave (mmWave) cell-free (CF) massive MIMO systems. Such a problem is formulated into an integer-constrained optimization problem which aims at maximizing the minimum average received signal power among users. Directly solving this problem is NP-hard due to its combinatorial nature. To address this difficulty, we first transform the problem into a feasibility checking problem within a bisection framework. Then an alternating average reflection algorithm (AARA)-based method is developed to solve the feasibility checking problem. Simulation results show that the proposed algorithm achieves a considerable performance improvement over existing methods.

Channel estimation (CE) is a crucial phase in wireless communication systems, especially in cell-free (CF) massive multiple input multiple output (M-MIMO) since it is a dynamic wireless network. Therefore, this work is introduced to study CE for the CF M-MIMO system in the uplink phase, wherein the performance of different estimators are evaluated, discussed, and compared in various situations. We assume the scenario in which each access point has prior knowledge of the channel statistics. The phase-aware-minimum mean square error (PA-MMSE) estimator, the non-phase-aware-MMSE (NPA-MMSE) estimator, and the least-squares estimator are the three estimators which are exploited in this work. Besides, we consider the Rician fading channel in which the line-of-sight path is realized with a phase-shift that models the users’ mobility where the considered phase-shift follows a uniform distribution. On the other hand, the mean-squared error metric is employed in order to evaluate the performance of each estimator, where an analytical and simulated result is provided for the PA-MMSE estimator and the NPA-MMSE estimator in order to assert our numerical results.

A scalable cell-free massive MIMO (SCF-mMIMO) system where all user equipments (UEs) and access points (APs) employ finite resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) over correlated Rician fading is presented and analysed in this paper. A closed-form expression for the uplink (UL) spectral efficiency (SE) using maximal-ratio combining (MRC) detection for centralized scheme is first derived. Moreover, a novel low complexity partial MMSE (P-MMSE) detector is proposed, which achieves very similar SE performance and maintains much less computational complexity in comparison with the original partial MMSE (P-MMSE) detector. In addition, a joint algorithm consisting of AP cluster formation, pilot assignment, and power control policy is proposed, which yields much higher SE performance than random pilot assignment and user-group based pilot assignment policies do, and meanwhile improves the quality of service (QoS) fairness for all accessing UEs as compared to the equal power transmit policy.