Recent advances in deep learning (DL) have significantly enhanced automatic modulation classification (AMC), reducing the dependence on intricate feature engineering and substantially improving classification accuracy. Nonetheless, the conventional DL-based AMC methods have mostly adopted frequentist approaches, often exhibit limited adaptability and struggle to offer reliable uncertainty estimates for their predictions, especially when trained on limited data. To address these challenges, we propose a novel Bayesian DL framework tailored for the uncertainty-aware incremental AMC. Our framework involves a two-stage process, beginning with the optimization of model parameters using a cumulative loss function in frequentist approach, followed by the application of Bayesian Neural Network (BNN) techniques, such as Laplace approximation and variational inference, for statistical inference. This framework ensures robust calibration and scalability, enabling the classifier to incrementally refine its predictions with the reception of additional signal samples and to terminate the refinement process once the confidence score meets the predetermined classification accuracy target. In addition, these enhanced capabilities of our classifier can extend to identifying a new, unseen modulation scheme by discerning distinct confidence score patterns associated with varying sequence lengths. Simulation results validate the efficacy of the proposed uncertainty-aware incremental AMC by showing enhanced classification accuracy and superior calibration relative to conventional DL-based AMC methods in practical scenarios with limited training data.

In this paper, we consider a radar network for sensing ground targets where radars mounted on unmanned aerial vehicles (UAVs) that fly at a certain altitude are randomly distributed according to a two-dimensional homogeneous Poisson point process (HPPP). For such a sensing network, we derive the distribution of the radar interference using a stochastic geometry based analysis. In particular, when Swerling I model is considered for radar cross-section area (RCS) for the target, we derive the Laplace transform of the radar interference. Here, to avoid a strong interference between neighboring radars, a guard zone is introduced within which the UAV radar transmission around the permitted active radar is inhibited. As the radar performance metric, we derive the successful ranging probability (SRP) of a given radar by exploiting the Laplace transform of radar interference. Using our analytical results, which are verified by computer simulations, we argue that the radar network design parameters such as the radius of the guard zone and the density of the active radars can be optimized.

Unmanned aerial vehicles (UAVs) are expected to be used extensively in the future for various applications, either as user equipment (UEs) connected to a cellular wireless network, or as an infrastructure extension of an existing wireless network to serve other UEs. Next generation wireless networks will consider the use of UAVs for joint communication and radar and/or as dedicated radars for various sensing applications. Increasing number of UAVs will naturally result in larger number of communication and/or radar links that may cause interference to nearby networks, exacerbated further by the higher likelihood of line-of-sight signal propagation from UAVs even to distant receivers. With all these, it is critical to study network coexistence of UAV-mounted base stations (BSs) and radar transceivers. In this paper, using stochastic geometry, we derive closed-form expressions to characterize the performance of coexisting UAV radar and communication networks for spectrum overlay multiple access (SOMA) and time-division multiple access (TDMA). We evaluate successful ranging probability (SRP) and the transmission capacity (TC) and compare the performance of TDMA and SOMA. Our results show that SOMA can outperform TDMA on both SRP and TC when the node density of active UAV-radars is larger than the node density of UAV-comms.

This paper studies precoder designs for simultaneous wireless information and power transfer (SWIPT) in multi-input multi-output (MIMO) channels, where a transmitter sends information to information decoding (ID) users while satisfying the minimum energy requirement of energy harvesting users. In contrast to the previous designs focused only on maximum information rate (MIR), we propose a more general and simpler solution using the weighted minimum mean squared error (WMMSE) criterion. To solve the SWIPT-WMMSE problem which is generally non-convex, we suggest two different design schemes, separate and joint designs. Interestingly, it is shown that the joint design achieves optimal performance with a single initial point and a few iterations, while the separate design needs a large number of iterations and initial points to approach the optimum. Based on the observation, we propose a simple closed-form solution, which is shown to achieve near optimal performance with reduced complexity. The derived solution can be adopted in various pragmatic applications of MIMO communications, such as the MMSE, quality-of-service, equal error designs, as well as the MIR by adjusting the weight matrix. We also confirm that our design strategies are a great use for managing co-channel interference in multiple ID-user scenarios. Finally, simulation results demonstrate the efficiency of the proposed MIMO-SWIPT framework.

To determine the transmission strategy for the joint wireless information and energy transfer (JWIET) in the MIMO interference channel (IFC), the information access point (IAP) and energy access point (EAP) require the channel state information (CSI) of their associated links to both the information-decoding (ID) mobile stations (MSs) and energy-harvesting (EH) MSs (so-called local CSI). In this paper, to reduce the feedback overhead of MSs for the JWIET in two-user MIMO IFC, we propose a Geodesic energy beamforming scheme that requires partial CSI at the EAP. Furthermore, in the two-user MIMO IFC, it is proved that the Geodesic energy beamforming is the optimal non-cooperative strategy under local CSIT assumption. By adding a rank-one constraint on the transmit signal covariance of IAP, we can further reduce the feedback overhead to IAP by exploiting Geodesic information beamforming. Under the rank-one constraint of IAP's transmit signal, we prove that Geodesic information/energy beamforming approach is the optimal non-cooperative strategy for JWIET in the two-user MIMO IFC. We also discuss the extension of the proposed rank-one Geodesic information/energy beamforming strategies to general K-user MIMO IFC. Finally, by analyzing the achievable rate-energy performance statistically under imperfect partial CSIT, we propose an adaptive bit allocation strategy for both EH MS and ID MS.

The space limitation and channel acquisition prevent Massive MIMO from being easily deployed in a practical setup. Motivated by current deployments of LTE-Advanced, the use of multi-polarized antenna elements can be an efficient solution to address the space constraint. Furthermore, the dual-structured precoding, in which a preprocessing based on the spatial correlation and a subsequent linear precoding based on the short-term channel state information at the transmitter (CSIT) are concatenated, can reduce the feedback overhead efficiently. By grouping and preprocessing spatially correlated mobile stations (MSs), the dimension of the precoding signal space is reduced and the corresponding short-term CSIT dimension is reduced. In this paper, to reduce the feedback overhead further, we propose a dual-structured multi-user linear precoding, in which the subgrouping method based on co-polarization is additionally applied to the spatially grouped MSs in the preprocessing stage. Furthermore, under imperfect CSIT, the proposed scheme is asymptotically analyzed based on random matrix theory. By investigating the behavior of the asymptotic performance, we also propose a new dual-structured precoding in which the precoding mode is switched between two dual-structured precoding strategies with 1) the preprocessing based only on the spatial correlation and 2) the preprocessing based on both the spatial correlation and polarization. Finally, we extend it to 3D dual-structured precoding.

Recently, joint wireless information and energy transfer (JWIET) methods have been proposed to relieve the battery limitation of wireless devices. However, the JWIET in a general K-user MIMO interference channel (IFC) has been unexplored so far. In this paper, we investigate for the first time the JWIET in K-user MIMO IFC, in which receivers either decode the incoming information data (information decoding, ID) or harvest the RF energy (energy harvesting, EH). In the K-user IFC, we consider three different scenarios according to the receiver mode: i) multiple EH receivers and a single ID receiver, ii) multiple IDs and a single EH, and iii) multiple IDs and multiple EHs. For all scenarios, we have found a common necessary condition of the optimal transmission strategy and, accordingly, developed the transmission strategy that satisfies the common necessary condition, in which all the transmitters transferring energy exploit a rank-one energy beamforming. Furthermore, we have also proposed an iterative algorithm to optimize the covariance matrices of the transmitters that transfer information and the powers of the energy beamforming transmitters simultaneously, and identified the corresponding achievable rate-energy tradeoff region. Finally, we have shown that by selecting EH receivers according to their signal-to-leakage-and-harvested energy-ratio (SLER), we can improve the achievable rate-energy region further.

Frequency domain blind channel estimation methods for orthogonal frequency division multiplexing (OFDM) systems with multiple antennas are proposed, in which the cross relation between the channel gains and a single snapshot of the received signal on each subcarrier is utilised. Necessary and sufficient conditions for the identifiability of a single‐input multiple‐output OFDM channel estimator are derived and, furthermore, using a first‐order perturbation analysis, the approximation of mean‐squared‐error (MSE) is analytically obtained, which shows that the proposed estimator is approximately unbiased at high signal‐to‐noise ratio. The proposed estimator exhibits lower MSEs than those of both the existing subspace‐based and deterministic blind channel estimators and is less sensitive to the estimation error of the number of multipaths. Finally, the extension of the proposed channel estimation to multiple‐input–multiple‐output OFDM is presented.

This paper investigates joint wireless information and energy transfer in a two-user MIMO interference channel, in which each receiver either decodes the incoming information data (information decoding, ID) or harvests the RF energy (energy harvesting, EH) to operate with a potentially perpetual energy supply. In the two-user interference channel, we have four different scenarios according to the receiver mode - (ID1, ID2), (EH1, EH2), (EH1, ID2), and (ID1, EH2). While the maximum information bit rate is unknown and finding the optimal transmission strategy is still open for (ID1, ID2), we have derived the optimal transmission strategy achieving the maximum harvested energy for (EH1, EH2). For (EH1, ID2), and (ID1, EH2), we find a necessary condition of the optimal transmission strategy and, accordingly, identify the achievable rate-energy (R-E) tradeoff region for two transmission strategies that satisfy the necessary condition - maximum energy beamforming (MEB) and minimum leakage beamforming (MLB). Furthermore, a new transmission strategy satisfying the necessary condition - signal-to-leakage-and-energy ratio (SLER) maximization beamforming - is proposed and shown to exhibit a better R-E region than the MEB and the MLB strategies. Finally, we propose a mode scheduling method to switch between (EH1, ID2) and (ID1, EH2) based on the SLER.

We present a generalized singular value decomposition (GSVD) based multi-user (MU) multi-stream multiple-input multiple-output (MIMO) precoding scheme with limited feedback. Our precoding scheme maximizes the signal to leakage and noise ratio (SLNR), so that it does not require any iterations to obtain precoding matrices. However, in MU-MIMO system, the precoding matrix should be computed at transmit side, because the precoding matrix of a user affects those of other users. Accordingly, full channel state information (CSI) for all users is required at the transmitter, which prevents MU-MIMO precoding implementable. To overcome this problem, an efficient limited feedback algorithm optimized to the GSVD based MU-MIMO precoding is proposed. Here, by extracting the parameters to be utilized in GSVD based precoding from the full CSI, the amount of feedback information is reduced. Furthermore, by analyzing their statistical properties, the parameters are efficiently quantized according to their probability densities. Finally, we develop a modified GSVD based precoding considering the channel quantization errors due to the limited feedback.

In this paper, low-complexity generalized singular value decomposition (GSVD) based beamforming schemes are proposed for cognitive radio (CR) network, in which multiple secondary users (SUs) with multiple antennas are allowed to access the spectrum of the primary network only if they do not interfere with the communication of primary users (PUs). The optimal beamforming scheme that satisfies the interference constraints at multiple PUs and maximizes signal-to-interference-plus-noise ratios (SINRs) of multiple SUs simultaneously requires a complicated iterative optimization process. To overcome the computational complexity, we develop a signal-to-leakage-plus-noise ratio (SLNR) maximizing beamforming scheme in which the weight can be obtained by GSVD without any iterations. The proposed beamforming also avoids any matrix squaring operations, and therefore, the computational complexity is drastically reduced. By modifying the proposed algorithm and introducing additional iterations but less than the number of PUs, the system performance is further improved without serious increase of the computational complexity.

In this paper, we develop an adaptive transmission scheme in a wireless sensor network (WSN). Distributed sensors can cooperate with the neighborhood and transmit their data to the data collector by choosing a proper transmission mode adaptively based on the channel conditions and spatial correlation among the sensors. By investigating the statistical properties of a correlated virtual multiple-input multiple-output (MIMO) channel between the sensors and data collector, we assess the analytic performances of two different MIMO transmission modes -- spatial multiplexing and transmit diversity achieving schemes. We also evaluate energy efficiencies of two MIMO transmission modes. Based on these result, we derive a new energy efficient mode switching criterion between spatial multiplexing and transmit diversity suitable to a WSN.

We present a lattice reduction (LR) aided successive interference cancellation (SIC) scheme combined with linear pre-equalization in the correlated multi-user MIMO system. The LR-aided SIC schemes have been extensively studied to detect multiple users' symbols due to the nearly optimal performance. However, the LR algorithm and the sequential detecting method inherently incur considerable computational complexity overhead although it is of a polynomial order. To reduce their computational complexities, we decouple the entire system into multiple lower dimensional subsystems using linear pre-equalization based on the correlation among the users. We then utilize the LR-aided SIC parallel in each subsystem. Furthermore, to combine the LR-aided SIC with pre-equalization optimally, we modify the LR-aided SIC scheme, handling the residual interferences after pre-equalization.

We present a multi-user multiple-input multiple-output (MU-MIMO) preceding scheme utilizing generalized singular value decomposition (GSVD). Our work is motivated by the precoding scheme developed by Sadek, Tarighat, and Sayed that maximizes the signal to leakage and noise ratio (SLNR), in which the precoding weight is obtained by the generalized eigenvalue decomposition (GEVD). However, the covariance matrix utilized in GEVD becomes close to be singular as the signal to noise ratio goes high. To improve the numerical accuracy, a GSVD based algorithm is exploited in this paper and a novel method is derived to compute the precoding weights for multiple users by removing redundant computational loads rather than repeating the GSVD algorithm for each user. In addition, we propose a technique to speed up the GSVD based precoding algorithm by preprocessing using the QR decomposition. Finally, to improve the system performance, the antenna selection scheme using GSVD is also proposed which does not require the exhaustive search to choose the active antennas.

We present a lattice reduction (LR) aided preceding scheme combined with spatial division multiplexing (SDM) for clusters of correlated multiple users. Previously, the LR aided precoding schemes are deeply studied to transmit different symbols, simultaneously, to multiple users since it shows superior performance to linear precoding schemes like zero-forcing (ZF) algorithm. However, it has high complexity compared with linear precoding schemes. To resolve it, we exploit the spatial separability between clusters. That is, we apply the LR aided scheme for highly correlated users in a same cluster and to suppress the interferences from other clusters, we apply the ZF algorithm. Furthermore, we also propose a new SDM scheme to maximize the transmit signal to interference and noise ratio, which is formulated by generalized eigenvalue problem. We verify the performance of the proposed schemes by Monte-Carlo simulations and compare the computation complexities of them with the conventional LR aided precoding scheme.