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In this paper, we address the problem of channel allocation for femtocells that share the use of regular macrocell spectrum. The femto basestation (FBS) scheduling problem is formulated in the form of restless multiarmed bandit (RMAB) framework. Our goal is to choose the arms/channels that maximize the total expected discounted reward over infinite horizon while minimizing the induced interference due to channel sharing with macrocell. Without direct observation of true channel state, we use the available macrocell user feedback known as channel quality indicator (CQI). In general, the RMAB problem is P-SPACE hard. We propose a heuristic low complexity indexing policy referred as approximated Whittle index to rank available channels for FBS. Although finding a closed form channel ranking solution typically involve dynamic programming, we show that based on the partial channel information within CQI, there exists a closed form for the channel index. Moreover, we demonstrate the performance advantage of the proposed indexing policy over a myopic policy. 

This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph- based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm. 

In many packet-switched wireless systems including cellular networks, RObust Header Compression (ROHC) plays an important role in improving payload efficiency by reducing the number of header bits in a link session. However, there are only very few research works addressing the optimized control of ROHC. Our recent studies have demonstrated the advantage of a trans-layer ROHC design that exploits lower layer link status. We have presented a unidirectional ROHC design based on a partially observable Markov decision process formulation that enables the transmitter to decide the header compression level without receiver feedback. The present work considers the physical channel dynamics in an LTE environment and how they affect header decompressor status. Our new model takes into consideration the transport block (TBs) size defined in LTE transmission according to the modulation and coding scheme (MCS). Our novel and practical model can significantly improve the efficiency of the transmission when compared to a traditional timer-based ROHC control. 

Auction-based service provisioning and resource allocation have demonstrated strong potential in Cloud-RAN wireless network architecture and heterogeneous networks for effective resource sharing. One major technical challenge is the integration of interference constraints in auction-based solutions. In this work we transform the interference constraint requirement into a set of linear constraints on each cluster. We tackle the generally NP-hard clustering problem by developing a novel practical suboptimal solution that can meet our design requirement. Our novel algorithm utilizes the properties of chordal graphs and applies Lexicographic Breadth First Search (Lex-BFS) algorithm for cluster splitting. This polynomial time approximate algorithm searches for maximal cliques in a graph by generating strong performance in terms of subgraph density and probability of optimal clustering without suffering from the high complexity of the optimal solution. 

Learning network topology from partial knowledge of its connectivity is an important objective in practical scenarios of communication networks and social-media networks. Representing such networks as connected graphs, exploring and recovering connectivity information between network nodes can help visualize the network topology and improve network utility. This work considers the use of simple hop distance measurement obtained from a fraction of anchor/source nodes to reconstruct the node connectivity relationship for large scale networks of unknown connection topology. Our proposed approach consists of two steps. We first develop a tree-based search strategy to determine constraints on unknown network edges based on the hop count measurements. We then derive the logical distance between nodes based on principal component analysis (PCA) of the measurement matrix and propose a binary hypothesis test for each unknown edge. The proposed algorithm can effectively improve both the accuracy of connectivity detection and the successful delivery rate in data routing applications.