Analysis of UAV Communications in Cell-Free Massive MIMO Systems

We consider a wirelessly powered Internet of Things (IoT) based on cell-free massive MIMO with energy harvesting. 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. We assume that single antenna sensors send uplink pilots in order to allow APs to detect active users and estimate their channel coefficients. We assume that each AP is equipped with N$\geq$1 antennas and uses the linear minimum mean square error (LMMSE) channel estimation. Through an asymptotic analysis, we derive closedform approximations for the variance of the LMMSE channel coefficient estimates, the amount of harvested energy, and the achievable rates for the uplink data transmission. We next use these expressions to jointly optimize the charging duration and uplink and downlink power control coefficients to minimize the total transmit energy consumptions of APs and sensors, which is crucially important for IoT sensors. Simulation results verify the accuracy of the obtained expressions, and shows that significant gains in energy efficiency can be achieved by the proposed optimization algorithms.

Recently, unmanned aerial vehicle (UAV) communications have drawn significant research interests. Meanwhile, cell-free (CF) massive multiple-input multiple-output (MIMO) is proposed as a promising technology to achieve the ultimate performance limits. In this paper, we investigate the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. Take hardware impairments of UAV into account, novel closed-form downlink HE and uplink spectral efficiency (SE) expressions are derived. UAV communications with small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. Our results reveal that CF massive MIMO achieves two and four times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. To this end, SE is a concave function of the time-splitting fraction, and the optimal time-splitting fraction for maximizing SE is determined by the altitude and hardware impairment factor of the UAV.

In this paper, we consider cell-free (CF) massive MIMO systems employing a massive number of access points (APs) geographically distributed over a wide area to jointly serve a smaller number of users over the same time-frequency resources. We consider semi-blind methods for channel estimation in the presence of Gaussian i.i.d. data to resolve the pilot contamination. This task is further aided by exploiting prior channel information in a Bayesian formulation. We propose a variable level expectation propagation (VL-EP) algorithm for message passing (MP) style semi-blind channel estimation which provides an approximate minimum mean square error (MMSE) channel estimator (which itself can not be found analytically). Numerical simulations verify the analytical derivations and the proposed algorithm.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Cell-free Massive MIMO (mMIMO) is envisaged to be a next-generation technology beyond 5G with its high spectral efficiency and superior spatial diversity as compared to that of conventional MIMO technology. The main principle is that many distributed access points (APs) cooperate to simultaneously serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture. We propose a novel sequential processing algorithm with normalized linear minimum mean square error (N-LMMSE) combining at every AP. This algorithm enables interference suppression in cell-free mMIMO while keeping the cost and front-haul requirements low. The spectral efficiency of the proposed algorithm is computed and analyzed. We conclude that it provides an attractive trade-off between low front-haul requirements and high spectral efficiency.

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.

As the mobile networks become even denser and the traffic demand is increasing drastically, it is becoming more heterogeneous. Recently, it is intensive to investigate the Cell-Free Massive multiple-input multiple-output Massive (MIMO) system, where a large number of access points (APs) simultaneously serve a much smaller number of users, and the APs and users are clustered to provide good service for all users. This paper designs a mobile base station (MBS)-assisted Cell-Free Massive MIMO system: Utilizing an MBS deployed on the unmanned aerial vehicle (UAV) to form an air-ground heterogeneous system with the Cell-Free Massive MIMO system and offload part of traffic to the MBS to further improve the system performance. To provide better service to all users, this paper design an MBS flight trajectory optimal method to improve the wireless coverage performance and user experience, and proposed a joint power allocation algorithm based on the consideration of the fairness of the service quality. The simulation results indicate that the performance of the system designed in this paper has a significant improvement compared with the normal Cell-Free Massive MIMO system.

The cooperation of multiple Unmanned Aerial Vehicles (UAVs) has become a promising scenario in Space-Air-Ground Integrated Networks (SA-GINs) recently due to their widespread applications, where wireless communication is a basic necessity and is normally categorized into control and non-payload communication (CNPC) as well as payload communication. In this paper, we attempt to tackle two challenges of UAV communication respectively on establishing reliable CNPC links against the high mobility of UAVs as well as changeable communication conditions, and on offering dynamic resource optimization for Quality-of-Service (QoS) guaranteed payload communication with variable link connectivity. Firstly, we propose the concept of air controlling center (ACC), a virtual application equipped on the infrastructure in SAGINs, which can collect global information for estimating UAV trajectory and communication channels. We then introduce the knapsack problem for modelling resource optimization of UAV communication in order to provide optimal access points for both CNPC and payload communication. Meanwhile, using the air controlling information, predictive decision algorithm and handover strategy are introduced for the reliable connection with multiple access points. Simulation results demonstrate that our proposal ensures an approximate always-on reliable accessing of communication links and outperforms the existing methods against high mobility, sparse distribution, and physical obstacles.

Cell-Free (CF) Massive MIMO (mMIMO) is a tech-nology which can potentially augment not only the deployment of 5G, but also the deployment of beyond 5G (B5G) wireless networks. However, the cost for rolling out such systems may be significant. Radio stripes form a promising solution which offers the potential of scalability at a reduced price. This paper investigates the uplink scenario of a CF mMIMO system, imple-mented with a limited-capacity radio stripe which integrates a novel arrangement of access points (APs), fully exploiting macro-diversity benefits. We also analyze a heuristic Compare-and-Forward (CnF) strategy, which, by comparing normalized linear minimum mean square error (N-LMMSE) soft estimates, enables optimal dynamic cooperation clustering, thus leading to a user-centric radio stripe network approach. Aiming at maximizing the per-user uplink spectral efficiency (SE), we ensure that, under finite capacity constraints, our solution can guarantee better performance than existing radio stripe architectures, especially when system size scales.

Background: Cell Free massive MIMO, containing a very large number of distributed access points (APs), which is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell-free M-MIMO is maximum when selecting optimal access points (AP) from the large number of APs. The linear precoding methods of zeroforcing (ZF) and minimum mean square error (MMSE) are utilized in this study because they are devoid of self-interference and so improve the system capacity. Objective: The objective of this study is to maximize the system data rate in a cell-free M-MIMO network. Method: To maximize the system data rate, the maximum channel gain-based Access Point Selection (MCGAPS), Distance based Access Point Selection (DAPS), and Random-Access Point Selection (RAPS) algorithms are used to pick access points (APs) in a cell-free M-MIMO network. Because the MCGAPS algorithm selects those APs with the highest channel gain, the system’s rate is improved. Result & Discussion: The DAPS algorithm is used to choose the closest APs to the user. The APs were randomly chosen using RAPS. Random user selection (RUS) algorithm schedules the same number of users. Conclusion: It is observed that the DAPS and RUS algorithms jointly improve the system rate significantly in cell-free massive MIMO system compared to the other proposed algorithms.

SummaryThis paper introduces the concept of spatial and media‐based modulated (SMBM) orthogonal frequency division multiplexing (OFDM) as a potential candidate for highly mobile next generation beyond 5G (B5G) wireless communications. The proposed SMBM‐OFDM technique utilizes not only the transmit antenna and channel state indices but also OFDM subcarriers to improve the system performance under high mobility. In addition, this study sheds light on challenging fast time‐varying channel estimation problem of MBM‐based systems by using the linear minimum mean square error (LMMSE) approach due to its optimality to investigate the achievable system performance with the aid of basis expansion modeling. The minimum lower bound on the channel estimation error (Bayesian Cramer–Rao bound) is derived theoretically and shown to be attainable by the considered LMMSE estimator. Moreover, symbol detection performance is provided for different modulation types and higher mobile velocities. Simulation results demonstrate that SMBM‐OFDM system under high mobility is able to provide around 12‐dB performance gains in terms of both channel estimation and symbol detection error compared to conventional spatial modulation (SM)‐OFDM systems without MBM. The presented framework is important due to addressing the high mobility support of SMBM‐OFDM systems for B5G wireless communications in terms of achievable channel estimation and data detection performance.

Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV’s eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.

With the increasing rate of wireless devices and high bandwidth operations, wireless networking and communications are becoming over crowded. To cope with such crowded and messy situation, massive MIMO is designed to work with hundreds of low-cost serving antennas at a time as well as improve the spectral efficiency at the same time. Time Division Duplexing (TDD) has been used for gaining beamforming which is a major part of massive MIMO, to gain its best improvement to transmit and receive pilot sequences. All the benefits are only possible if the channel state information or channel estimation is gained properly. Least Square (LS), Minimum Mean Square Error (MMSE) and Linear Minimum Mean Square Error (LMMSE) are the estimators commonly used for estimating channel matrices. We have optimized these methods using genetic algorithm to minimize the mean squared error and finding the best channel matrix from existing algorithms with less computational complexity. Our simulation result has shown that the use of Genetic Algorithm (GA) worked well on existing algorithms in a Rayleigh slow fading channel and existence of Additive White Gaussian Noise. We found that the GA optimized LS is better than existing algorithms. GA provides optimal result in some few iterations in terms of Mean Square Error (MSE) with respect to SNRand computational complexity.

Pair-wise error probability (PEP) is analyzed in the presence of channel estimation error (CEE) for orthogonal frequency division multiplexing (OFDM) in a quasi-static Rayleigh fading channel. Subcarriers in OFDM are grouped in an equi-spaced manner with the number of subcarriers in a group equal to the number of channel taps. One group is dedicated for training and the rest are for data transmission. A linear minimum mean square error (LMMSE) based channel estimation for coherent detection is considered in this paper. This enables the signal dependent CEE to be uncorrelated to the data. Since ML decoding employed with perfect CSI becomes suboptimal in the presence of CEE, an optimal ML decoding is derived. It is observed from our training-based PEP expression that the CEE does not reduce the diversity order but contributes to a loss of coding gain. Moreover, a code design criterion is established. To reduce the loss of coding gain, an optimal training scheme is developed, based on the PEP expression. Loss of performance due to an imperfect channel estimate is quantified in terms of bounds on bit error probability (BEP) for high SNR. Our analytical findings are corroborated by simulation examples.

Cell-free massive MIMO (CF-mMIMO) systems promise enhanced spectral efficiency and uniform service by deploying numerous distributed access points (APs) that jointly serve users. However, centralized precoding, which offers optimal performance by leveraging global channel state information (CSI), imposes significant fronthaul overhead due to the extensive CSI exchange between APs and the central processing unit (CPU). In contrast, distributed precoding reduces fronthaul demands, but undergoes performance degradation due to the lack of coordination between APs. This paper introduces a novel framework that strikes a balance between these two scenarios by employing channel charting (CC) as a lightweight CSI compression mechanism at each AP. CC maps high-dimensional CSI into a low-dimensional pseudo-location space, capturing essential channel characteristics. These pseudo-locations are transmitted to the CPU with minimal overhead, where advanced machine learning models are employed to infer effective downlink precoding vectors, thereby approximating centralized performance while reducing fronthaul signaling. Extensive simulations on realistic synthetic datasets demonstrate that our approach achieves very promising results. This work highlights the potential of integrating CC and machine learning to enhance scalability and efficiency in CF-mMIMO networks.