MOTIVATION: Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. RESULTS: We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric-fitness diversity-which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

Existing optimization and deep neural network (DNN) approaches for beamforming and power control cannot achieve satisfactory performance and computation time trade-offs for interference networks with arbitrary device locations, due to optimization algorithms' expensive computation costs and DNNs' universal approximation and lack of generality. To address this issue, we propose new beamforming and power control approaches by elegantly leveraging optimization and deep learning techniques. We investigate the maximization of the worst-case achievable rate of multiple transceiver pairs under power constraints in orthogonal frequency-division multiplexing (OFDM)-based wideband device-to-device (D2D) networks with multiple subcarriers and arbitrary device locations. First, we obtain each transceiver's transmit and receive beamforming using the standard singular value decomposition (SVD) method. Then, we propose an iterative algorithm named PSCA to obtain joint power control for all transceivers using the parallel successive convex approximation (PSCA) method. PSCA allows parallel and closed-form per-iteration updates and has a short per-iteration computation time. Next, we propose two PSCA-driven deep unrolling neural networks, namely PSCANet and S-PSCANet to reduce the overall computation time. PSCANet and S-PSCANet marry PSCA's parallel computation mechanism with the parallelizable neural network architecture and effectively optimize PSCA's algorithm parameters based on vast samples of random channel fading coefficients and device locations. Moreover, S-PSCANet successfully resolves the training problem due to vanishing or exploding gradients when unrolling many PSCA iterations. Numerical results demonstrate the superior advantages of PSCANet and S-PSCANet over the existing approaches.

Integrated Sensing and Communications (ISAC) is emerged as one of the key technologies in next generation wireless systems. However, ISAC systems have been commonly explored neglecting mutual coupling. This paper investigates the mutual coupling exploitation to further improve the performance of ISAC systems. We aim to maximize the sensing beampatern gain by optimizing the tunable loads and the dual-functional beamforming while satisfying the minimum signal-to-interference-plus-noise (SINR) per user and the hardware constraint of the tunable loads. To solve the non-convex problem, we propose a penaltybased iterative algorithm to obtain a stationary point. Specifically, in each iteration we adopt the block coordinate descent (BCD) method where the dual-functional beamforming is obtained by using Lagrange duality, and the tunable loads are solved with closed forms. Numerical results demonstrate the notable gains and effectiveness of the proposed algorithms compared to the baseline schemes. To the best of our knowledge, this is the first study utilizing the MC effect to improve system performance in an ISAC system with tunable loads.

With the development of the space-air-ground integration technology, the satellites are armed with a certain on-board computing resource. To fully offload communication tasks to each satellite, we investigate a multiuser multi-satellite cooperative communication system without a central processing unit (CPU), where the satellites are equipped with tunable load antennas leveraging the mutual coupling effect to reconfigure the wireless channel. First, we formulate the sum spectral efficiency (SE) maximization problem with respect to the beamforming and the tunable loads under the power constraint and the constraints of the tunable loads. Afterwards, we propose a cooperative algorithm with closed-form updates to obtain a stationary point based on parallel successive convex approximation (SCA). Furthermore, we propose an efficient information exchange strategy for the satellites based on the ring all-reduce method, which significantly reduces the information exchange overhead of each satellite. Lastly, numerical results verify the proposed design's notable gain over the baselines. As far as we know, this is the first work to study the multi-satellite cooperative communication system with tunable load antennas.

The costs of channel estimation, reflection adjustment, and computation have severe impacts on intelligent reflection surface (IRS)-aided physical layer security (PLS) wireless communication systems in practice but are usually overlooked for simplicity in most existing works. This paper considers a multi-antenna base station serving a single-antenna legitimate user with the assistance of a multi-element IRS under the surveillance of a single-antenna eavesdropper. Firstly, we introduce a partial instantaneous CSI and statistical CSI (PICSI-SCSI)-adaptive beamforming and SCSI-adaptive reflection design. Secondly, we maximize the achievable ergodic secrecy rate (ESR) with respect to the PICSI-SCSI-adaptive beamforming and SCSI-adaptive reflection design, resulting in a two-timescale stochastic non-convex problem. Thirdly, we present two stochastic iterative algorithms to reach stationary and approximate stationary points. Moreover, we show that the two proposed designs achieve lower computational complexities and adjustment costs for reflection than the existing PICSI-SCSI-adaptive beamforming and reflection design. Lastly, we numerically demonstrate the two proposed designs’ notable gains over baselines. To our knowledge, this is the first work providing an optimization-based PICSI-SCSI-adaptive beamforming and SCSI-adaptive reflection design in an IRS-aided PLS wireless communication system, achieving promising secure performance at the minimum adjustment cost for reflection.

The impacts of channel estimation, beamforming adjustment, phase shift adjustment, and computation costs on an intelligent reflecting surface (IRS)-assisted secure wireless communication system are severe in practice but are usually ignored for simplicity. In this paper, we consider a multi-antenna BS serving a single-antenna legitimate user with the help of a multi-element IRS in the presence of an eavesdropper. To maximally reduce the implementation cost, we investigate the no-instantaneous channel state information (ICSI) case with the legitimate user’s and eavesdropper’s statistical CSI (SCSI). First, we present a SCSI-adaptive (quasi-static) beamforming and phase shift design, also referred to as a quasi-static design, which has low channel estimation, beamforming adjustment, and phase shift adjustment costs. Then, we formulate the maximization of the achievable ergodic secrecy rate with respect to the quasi-static design as a challenging stochastic non-convex problem. Next, we propose two parallel iterative algorithms to obtain a stationary point and an approximate stationary point and present their respective quasi-static designs. Furthermore, we show that the quasi-static designs derived from the stationary point and approximate stationary point achieve lower implementation costs than existing designs. Finally, we numerically verify the analytical results and demonstrate notable gains of the two proposed quasi-static designs over existing designs.

Nowadays, with the rapid expansion of social media as a means of quick communication, real-time disaster information is widely disseminated through these platforms. Determining which real-time and multi-modal disaster information can effectively support humanitarian aid has become a major challenge. In this paper, we propose a novel end-to-end model, named GCN-based Multi-modal Domain Adaptation (GMDA), which consists of three essential modules: the GCN-based feature extraction module, the attention-based fusion module and the MMD domain adaptation module. The GCN-based feature extraction module integrates text and image representations through GCNs, while the attention-based fusion module then merges these multi-modal representations using an attention mechanism. Finally, the MMD domain adaptation module is utilized to alleviate the dependence of GMDA on source domain events by computing the maximum mean discrepancy across domains. Our proposed model has been extensively evaluated and has shown superior performance compared to state-of-the-art multi-modal domain adaptation models in terms of F1 score and variance stability.

Interference mitigation approaches in the presence of multiple receive antennas in the uplink of a multi-cell wireless communications system are studied in this paper. A formulation based on interference pricing is proposed, where it is shown that a single price per base-station can be computed and exchanged, in order to set the mobile transmit powers per cell. The work is premised on a decentralized network architecture where schedulers make decisions on users connected to their cell, and there is a low-rate inter-cell communication link to enable distributed interference mitigation. The proposed utility maximization approach provides a general framework for multi-user multiple-input multiple-output (MIMO) systems on the uplink and accommodates both optimal (MMSE) and sub-optimal (MRC) multi-antenna receivers.

We consider an interference network with multi-carrier transmission over M parallel sub-channels. There are K transmitter-receiver pairs, each transmitter transmits a single data stream with a rank-one precoding matrix, and the receivers are assumed to be linear. We show that a necessary condition for zero interference (alignment across sub-channels) is K ≤ 2M-2. In contrast, for a Multi-Input Multi-Output (MIMO) interference network with M×M spatial channels (full channel matrices) the corresponding condition is known to be K ≤ 2M - 1. We also characterize the sum rate at high Signal-to-Noise Ratios (SNR) by bounding the SNR offset (x-intercept) of the asymptote of the sum rate vs SNR curve. For a randomly chosen aligned solution as M increases, this offset shifts to the right as logM. In contrast, the SNR offset for a MIMO interference network does not increase with M. An approximation for the performance of sampling the best out of L aligned solutions is also presented. Numerical results show the analytical asymptotes accurately predict the sum rate curves at moderate to high SNRs.

We study distributed algorithms for updating transmit preceding matrices for a two-user Multi-Input/Multi-Output (MIMO) interference channel. Our objective is to maximize the sum rate with linear Minimum Mean Squared Error (MMSE) receivers, treating the interference as additive Gaussian noise. An iterative approach is considered in which given a set of preceding matrices and powers, each receiver announces an interference price (marginal decrease in rate due to an increase in interference) for each received beam, corresponding to a column of the precoding matrix. Given the interference prices from the neighboring receiver, and also knowledge of the appropriate cross-channel matrices, the transmitter can then update the beams and powers to maximize the rate minus the interference cost. Variations on this approach are presented in which beams are added sequentially (and then fixed), and in which all beams and associated powers are adjusted at each iteration. Numerical results are presented, which compare these algorithms with iterative water-filling (which requires no information exchange), and a centralized optimization algorithm, which finds locally optimal solutions. Our results show that the distributed algorithms perform close to the centralized algorithm, and by adapting the rank of the precoder matrices, achieve the optimal high-SNR slope.

We study distributed algorithms for allocating powers and/or adjusting beamforming vectors in a peer-to-peer wireless network which may have multiple-input-single-output (MISO) links. The objective is to maximize the total utility summed over all users, where each user's utility is a function of the received signal-to-interference-plus-noise ratio (SINR). Each user (receiver) announces an interference price, representing the marginal cost of interference from other users. A particular user (transmitter) then updates its power and beamforming vector to maximize its utility minus the interference cost to other users, which is determined from their announced interference prices. We show that if each transmitter update is based on a current set of interference prices and the utility functions satisfy certain concavity conditions, then the total utility is non-decreasing with each update. The proof is based on the convexity of the utility functions with respect to received interference, and applies to rate utility functions, and an arbitrary number of interfering MISO links. The extension to multi-carrier links is discussed as well as algorithmic variations in which the prices are not immediately updated after power or beam updates.