The introduction of massive MIMO (Multiple Input Multiple Output) communication systems enables base stations (BS) to perform beamforming for enhancing communication reliability. A typical key assumption, however, is the availability of accurate downlink channel state information (CSI). In practice, CSI estimation and reporting delays coupled with the process of channel aging result in the BS receiving outdated CSI information, which in turn impacts the system's spectral efficiency. To combat this latency, this paper develops efficient methods of CSI prediction that preemptively predict future downlink CSI based on historical data. We leverage the spatial and temporal correlation properties of the channel and use explicit feature extraction frameworks for both dimensions to accurately predict future CSI. We analyze combinations of spatial and temporal feature extractors in terms of a tradeoff between performance and latency. We evaluate the performance of the proposed prediction model in terms of proximity to the ground truth, prediction latency, and model footprint. Our experiments show that our method outperforms classical statistical methods as well as existing CSI prediction baselines.

In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generative models have been proposed to synthesize channel matrices, but outputs produced by such methods may not correspond to geometrically viable channels and do not provide any insight into the scenario being generated. In this work, we aim to address both these issues by integrating established parametric, physics-based geometric channel (PPGC) modeling frameworks with generative methods to produce realistic channel matrices with interpretable representations in the parameter domain. We show that the generative model converges to prohibitively suboptimal stationary points when learning the underlying prior directly over the parameters due to the non-convex PPGC model. To address this limitation, we propose a linearized reformulation of the problem to ensure smooth gradient flow during generative model training, while also providing insights into the underlying physical environment. We evaluate our model against prior baselines by comparing the generated, scenario-specific samples in terms of the 2-Wasserstein distance and through its utility when used for downstream compression tasks.

One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D) between distributed devices has been shown to be an effective tool for dealing with this problem and robust to stragglers. In an unsupervised case, however, it is not obvious how data exchanges should take place due to the absence of labels. In this paper, we propose an approach to create an optimal graph for data transfer using Reinforcement Learning. The goal is to form links that will provide the most benefit considering the environment's constraints and improve convergence speed in an unsupervised FL environment. Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method to different available FL schemes and benchmark datasets.

Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve conver-gence speed and reduce model bias through rapid local information exchange. However, data privacy concerns, device trust issues, and unreliable wireless channels each pose challenges to determining an effective yet resource efficient D2D structure. In this paper, we develop a decentralized reinforcement learning (RL) methodology for D2D graph discovery that promotes communication of non-sensitive yet impactful data-points over trusted yet reliable links. Each device functions as an RL agent, training a policy to predict the impact of incoming links. Local (device-level) and global rewards are coupled through message passing within and between device clusters. Numerical experiments confirm the advantages offered by our method in terms of convergence speed and straggler resilience across several datasets and FL schemes.

Federated learning (FL) has been recognized as one of the most promising solutions for distributed machine learning (ML). In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data. Nevertheless, in many applications, it is impractical to assume existence of labeled data across devices. To this end, we develop a novel methodology, Cooperative Federated unsupervised Contrastive Learning (CF-CL), for FL across edge devices with unlabeled datasets. CF-CL employs local device cooperation where data are exchanged among devices through device-to-device (D2D) communications to avoid local model bias resulting from non-independent and identically distributed (non-i.i.d.) local datasets. CF-CL introduces a push-pull smart data sharing mechanism tailored to unsupervised FL settings, in which, each device pushes a subset of its local datapoints to its neighbors as reserved datapoints, and pulls a set of datapoints from its neighbors, sampled through a probabilistic importance sampling technique. We demonstrate that CF-CL leads to (i) alignment of unsupervised learned latent spaces across devices, (ii) faster global convergence, allowing for less frequent global model aggregations; and (iii) is effective in extreme non-i.i.d. datasettings across the devices.

Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this goal are (i) heterogeneity in devices’ compute resources and (ii) topology constraints on which devices communicate with each other. We address these challenges by developing a novel network-aware distributed learning methodology where devices optimally share local data processing and send their learnt parameters to a server for periodic aggregation. Unlike traditional federated learning, our method enables devices to offload their data processing tasks to each other, with these decisions optimized to trade off costs associated with data processing, offloading, and discarding. We analytically characterize the optimal data transfer solution under different assumptions on the fog network scenario, showing for example that the value of offloading is approximately linear in the range of computing costs in the network when the cost of discarding is modeled as decreasing linearly in the amount of data processed at each node. Our experiments on real-world data traces from our testbed confirm that our algorithms improve network resource utilization substantially without sacrificing the accuracy of the learned model, for varying distributions of data across devices. We also investigate the effect of network dynamics on model learning and resource costs.

Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this are (i) heterogeneity in devices' compute resources and (ii) topology constraints on which devices can communicate. We are the first to address these challenges by developing a network-aware distributed learning optimization methodology where devices process data for a task locally and send their learnt parameters to a server for aggregation at certain time intervals. Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points. We analytically characterize the optimal data transfer solution for different fog network topologies, showing for example that the value of a device offloading is approximately linear in the range of computing costs in the network. Our subsequent experiments on both synthetic and real-world datasets we collect confirm that our algorithms are able to improve network resource utilization substantially without sacrificing the accuracy of the learned model.