Biblio

Traditional downlink (DL)-uplink (UL) coupled cell association scheme is suboptimal solution for user association as most of the users are associated to a high powered macro base station (MBS) compared to low powered small base station (SBS) in heterogeneous network. This brings challenges like multiple interference issues, imbalanced user traffic load which leads to a degraded throughput in HetNet. In this paper, we investigate DL-UL decoupled cell association scheme to address these challenges and formulate a sum-rate maximization problem in terms of admission control, cell association and power allocation for MBS only, coupled and decoupled HetNet. The formulated optimization problem falls into a class of mixed integer non linear programming (MINLP) problem which is NP-hard and requires an exhaustive search to find the optimal solution. However, computational complexity of the exhaustive search increases exponentially with the increase in number of users. Therefore, an outer approximation algorithm (OAA), with less complexity, is proposed as a solution to find near optimal solution. Extensive simulations work have been done to evaluate proposed algorithm. Results show effectiveness of proposed novel decoupled cell association scheme over traditional coupled cell association scheme in terms of users associated/attached, mitigating interference, traffic offloading to address traffic imbalances and sum-rate maximization.

Physical unclonable functions (PUFs) utilize manufacturing ariations of circuit elements to produce unpredictable response to any challenge vector. The attack on PUF aims to predict the PUF response to all challenge vectors while only a small number of challenge-response pairs (CRPs) are known. The target PUFs in this paper include the Arbiter PUF (ArbPUF) and the Memristor Crossbar PUF (MXbarPUF). The manufacturing variations of the circuit elements in the targeted PUF can be characterized by a weight vector. An optimization-theoretic attack on the target PUFs is proposed. The feasible space for a PUF's weight vector is described by a convex polytope confined by the known CRPs. The centroid of the polytope is chosen as the estimate of the actual weight vector, while new CRPs are adaptively added into the original set of known CRPs. The linear behavior of both ArbPUF and MXbarPUF is proven which ensures that the feasible space for their weight vectors is convex. Simulation shows that our approach needs 71.4% fewer known CRPs and 86.5% less time than the state-of-the-art machine learning based approach.