Cell-free massive MIMO (CFmMIMO) is a promising paradigm to provide uniform coverage in future wireless networks. However, a fully connected CFmMIMO system where all the access points (APs) serve every user equipment (UE) makes it challenging to deploy and scale in real-time due to high computational complexity and increased signaling overhead. In this work, we study the problem of downlink power allocation in partially connected CFmMIMO (p-CFmMIMO) systems using maximal ratio transmission (MRT). We utilize the underlying geometry of the problem to propose a graph representation of the CFmMIMO system and develop a graph neural network (GNN) based power allocation strategy to maximize the minimum SINR in the system. We demonstrate that the proposed GNN model has excellent generalizability to deployment size, radio propagation morphologies, and per-AP serving density1. Our GNN can address the power allocation problem in the fully connected case, the partially connected case, and even the cellular case with magnitudes lower computational complexity compared to the conventional numerical solvers. Notably, we show that over a wide range of service scenarios, the model achieves a median spectral efficiency that is within 10% of the optimal second-order cone programming (SOCP) solution while requiring 100 times fewer FLOPS.

We propose a framework for maximizing the number of machine-type devices connected in the uplink of a Narrow-band Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The system is based on the fast-uplink grant (FUG), where the base station (BS) schedules the access for active devices requesting connection. This problem is a mixed-integer non-convex problem and real-time solutions using general solvers are computationally prohibitive. The proposed scheduling solution comprises efficient device clustering and optimum power allocation using a bipartite graph matching approach, termed connection throughput maximizing full matching with pruning (CTMBM). Different from the other solutions of state-of-the-art, our proposed scheme considers scheduling over multiple transmission time intervals while considering the transmission deadlines and quality of service (QoS) for the devices. Additionally, we provide a method for priority scheduling of a subset of devices. We compare our solution to the state-of-the-art schemes and analyze the achieved gains through Monte-Carlo computer simulations.

Beyond 5G wireless technology Cell-Free Massive MIMO (CFmMIMO) downlink relies on carefully designed pre-coders and power control to attain uniformly high rate coverage. Many such power control problems can be calculated via second order cone programming (SOCP). In practice, several order of magnitude faster numerical procedure is required because power control has to be rapidly updated to adapt to changing channel conditions. We propose a Graph Neural Network (GNN) based solution to replace SOCP. Specifically, we develop a GNN to obtain downlink max-min power control for a CFmMIMO with maximum ratio transmission (MRT) beamforming. We construct a graph representation of the problem that properly captures the dominant dependence relationship between access points (APs) and user equipments (UEs). We exploit a symmetry property, called permutation equivariance, to attain training simplicity and efficiency. Simulation results show the superiority of our approach in terms of computational complexity, scalability and generaliz-ability for different system sizes and deployment scenarios.

We develop a framework for maximizing the number of transmitted packets for devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA) in the downlink. The base station (BS) chooses one of the multiple available physical resource blocks (PRBs) that are well separated in frequency for a device, giving them the advantage of exploiting frequency diversity. The scheduling strategy focuses on the two-fold problem involving efficient device clustering and optimum power allocation. This problem is a mixed-integer non-convex problem. We propose a bipartite graph matching approach, termed minimum weight full matching with pruning (MWFMP), to address the problem over multiple PRBs and solve it under the quality-of-service (QoS), allowable PRB, power budget, and interference constraints. Additionally, we provide a comparison with a greedy heuristic, the multi-PRB stratified device allocation (MPSDA), where we extend our previous work for a single PRB connectivity problem. Furthermore, we compare our algorithms to orthogonal multiple access (OMA) scheduling, which is prevalent in legacy LTE networks. We show that our algorithms steadily outperform the connectivity performance offered by OMA.

We address the issue of maximizing the number of connected devices in a Narrowband Internet-of-Things (NB-IoT) network using nonorthogonal multiple access (NOMA) in the downlink. We first propose an optimal joint subcarrier and power allocation strategy assuming perfect channel state information (CSI) called stratified device allocation (SDA), which maximizes the connectivity under data rate, power, and bandwidth constraints. Then, we generalize the connectivity maximization problem to the case of partial CSI, where only the distance-dependent path-loss component of the channel gain is available at the base station (BS). We introduce a novel framework called the stochastic connectivity optimization (SCO) framework. In this framework, we propose a heuristic improvement to SDA, namely, SDA with excess power (SDA-EP) algorithm for operation under partial CSI. Furthermore, we derive a concave approximation (SCO-CA) algorithm of near-optimal performance to SCO given the same amount of CSI. Through computer simulations, we show that SDA-EP and SCO-CA outperform conventional NOMA and OMA schemes in the presence of partial CSI over a wide range of service scenarios.

We address the issue of maximizing the number of connected devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The scheduling assignment is done on a per-transmit time interval (TTI) basis and focuses on efficient device clustering. We formulate the problem as a combinatorial optimization problem and solve it under interference, rate and sub-carrier availability constraints. We first present the bottom-up power filling algorithm (BU), which solves the problem given that each device can only be allocated contiguous sub-carriers. Then, we propose the item clustering heuristic (IC) which tackles the more general problem of non-contiguous allocation. The novelty of our optimization framework is two-fold. First, it allows any number of devices to be multiplexed per sub-carrier, which is based on the successive interference cancellation (SIC) capabilities of the network. Secondly, whereas most existing works only consider contiguous sub-carrier allocation, we also study the performance of allocating non-contiguous sub-carriers to each device. We show through extensive simulations that non-contiguous allocation through IC scheme can outperform BU and other existing contiguous allocation methods.

Understanding customer buying patterns is of great interest to the retail industry. Association rule mining is a common technique for extracting correlations such as people in the South of France buy rosé wine or customers who buy paté also buy salted butter and sour bread. Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular products in the top rules. As a result, a number of "interestingness" measures (over 30) have been proposed to rank rules. However, there is no agreement on which measures are more appropriate for retail data. Moreover, since pattern mining algorithms output thousands of association rules for each product, the ability for an analyst to rely on ranking measures to identify the most interesting ones is crucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), a framework that provides analysts with the ability to compare different rule rankings. We report on how we used C A PA to compare 34 interestingness measures applied to patterns extracted from customer receipts of more than 1,800 stores for a period of one year.

Social media has become a major source for analyzing all aspects of daily life. Thanks to dedicated latent topic analysis methods such as the Ailment Topic Aspect Model (ATAM), public health can now be observed on Twitter. In this work, we are interested in monitoring people's health over time. Recently, Temporal-LDA (TM?LDA) was proposed for efficiently modeling general-purpose topic transitions over time. In this paper, we propose Temporal Ailment Topic Aspect (TM?ATAM), a new latent model dedicated to capturing transitions that involve health-related topics. TM?ATAM learns topic transition parameters by minimizing the prediction error on topic distributions between consecutive posts at different time and geographic granularities. Our experiments on an 8-month corpus of tweets show that it largely outperforms its predecessors.

Consumer behaviour holds special importance in the retail industry. Consumer location impacts consumer behaviour by dictating purchase trends. This paper investigates the problem of examining product sales across a chain of stores to extract the geographic regions that characterize a product. Characterizing region for a product is a coherent geographic region where the consumers actively consume the said product. We introduce DICE, a diffusion-based technique to uncover all such regions for a given product, when they exist. In contrast to current state of the art, DICE involves minimal usage of parameters and shows remarkable tolerance to noise. We present experiments conducted on real datasets from a general commercial supermarket in France. Empirical evaluation and user-studies establish that the presented method significantly outperforms its natural baseline and previous state of the art approaches.