Unmanned Aerial Vehicles (UAVs) can be used as aerial base stations for data collection in next-generation wireless networks due to their high adaptability and maneuverability. This paper investigates the scenario where multiple UAVs cooperatively fly over heterogeneous ground users (GUs) and collect data without a central controller. With the consideration of signal-to-interference-and-noise ratio (SINR) and fairness among users, we jointly optimize the trajectories of UAVs and the GUs associations to maximize the total throughput and energy efficiency. We formulate the long-term optimization problem as a decentralized partially observed Markov decision processes (DEC-POMDP) and derive an approach combining the coalition formation game (CFG) and multi-agent deep reinforcement learning (MADRL). We first formulate the discrete association scheduling problem as a non-cooperative theoretical game and use the CFG algorithm to achieve a decentralized scheme converging to Nash equilibrium (NE). Then, a MARL-based technique is developed to optimize the trajectories and energy consumption continuously in a centralized-training but decentralized-execution manner. Simulation results demonstrate that the proposed algorithm outperforms the commonly used schemes in the literature, regarding the fair throughput and energy consumption in a distributed manner.

The rate regions of multilevel diversity coding systems (MDCSs), a sub-class of the broader family of multi-source multi-sink networks with special structure, are investigated in a systematic way. We enumerate all non-isomorphic MDCS instances with at most three sources and four encoders. Then, the exact rate region of every one of these more than 7000 instances is proven via computations showing that the Shannon outer bound matches with a custom constructed linear code-based inner bound. Results gained from these computations are summarized in key statistics involving aspects, such as the sufficiency of scalar binary codes, the necessary size of vector binary codes, and so on. Also, it is shown how to construct the codes for an achievability proof. Based on this large repository of rate regions, a series of results about general MDCS cases of arbitrary size that they inspired is introduced and proved. In particular, a series of embedding operations that preserve the property of sufficiency of scalar or vector codes is presented. The utility of these operations is demonstrated by boiling the thousands of MDCS instances for which scalar binary (superposition) codes are insufficient down to 12 (26) forbidden the smallest embedded MDCS instances.instances.

Coded caching is an emerging technique to reduce the data transmission load during the peak-traffic times. In such a scheme, each file in the data center or library is divided into a number of packets to pursue a low broadcasting rate based on the designed placements at each user’s cache. However, the implementation complexity of this scheme increases with the number of packets. It is crucial to design a scheme with a small subpacketization level, while maintaining a relatively low transmission rate. Recently, a combinatorial structure called placement delivery array (PDA) was proposed as an effective tool to design coded caching schemes with a relatively low subpacketization level. This paper proposes a novel PDA construction by selecting proper orthogonal arrays (POAs), which generalizes the existing construction but with a more flexible memory size. Based on the proposed PDA construction, an effective transform is further proposed to enable a coded caching scheme to achieve a smaller subpacketization level. Moreover, two new coded caching schemes with the coded placement are derived. It is shown that the proposed schemes can yield a lower subpacketization level or transmission rate over the benchmark schemes.

Recent algorithmic developments have enabled computers to automatically determine and prove the capacity regions of small hypergraph networks under network coding. A structural theory relating network coding problems of different sizes is developed to make the best use of this newfound computational capability. A formal notion of network minimality is developed, which removes components of a network coding problem that are inessential to its core complexity. Equivalence between different network coding problems under relabeling is formalized via group actions, an algorithm which can directly list single representatives from each equivalence class of minimal networks up to a prescribed network size is presented. This algorithm, together with rate region software, is leveraged to create a database containing the rate regions for all minimal network coding problems with five or fewer sources and edges, a collection of 744119 equivalence classes representing more than 9 million networks. In order to best learn from this database, and to leverage it to infer rate regions and their characteristics of networks at scale, a hierarchy between different network coding problems is created with a new theory of combinations and embedding operators.