Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - Scopus: 1On Stream Selection for Interference Alignment With Limited Feedback in Heterogeneous Networks(John Wiley and Sons Inc., 2016) Aycan, Esra; Özbek, Berna; Le Ruyet, DidierThis paper presents a stream selection based interference alignment approach with imperfect channel state information for heterogeneous networks. The proposed solution constructs stream sequences by selecting only the strongest stream of each user where the first stream of the constructed stream sequences is associated to a pico user. While selecting the streams, the channel matrices of the unselected streams are projected orthogonally to the virtual transmit and receive channels of the selected stream in order to align the interference in the null space of these virtual channels. In addition, the influence of imperfect channel state information on the proposed algorithm is analysed. A bit allocation scheme is given by deriving an upper bound on the rate loss because of quantisation. The simulation results are carried out by considering various scenarios with different locations of pico cells at the cell edge regions of the macro cell. The performance results show that the proposed algorithm with the imperfect channel state information achieves higher performance than the existing algorithms.Article Citation - WoS: 4Citation - Scopus: 6Semiblind Blue Channel Estimation With Applications To Digital Television(Institute of Electrical and Electronics Engineers Inc., 2006) Pladdy, Christopher; Özen, Serdar; Nerayanuru, Sreenivasa M.; Zoltowski, Michael; Fimoff, MarkA semiblind iterative algorithm to construct the best linear unbiased estimate (BLUE) of the channel impulse response (CIR) vector h for communication systems that utilize a periodically transmitted training sequence within a continuous stream of information symbols is devised. The BLUE CIR estimate for the general linear model y = Ah + w, where w is the correlated noise, is given by the Gauss-Markoff theorem. The covariance matrix of the correlated noise, which is denoted by C(h), is a function of the channel that is to be identified. Consequently, an iteration is used to give successive approximations h(k), k = 0, 1, 2,...to hBLUE, where h(0) is an initial approximation given by the correlation processing, which exists at the receiver for the purpose of frame synchronization. A function F(h) for which hBLUE is a fixed point is defined. Conditions under which hBLUE is the unique fixed point and for which the iteration proposed in the algorithm converges to the unique fixed point hBLUE are given. The proofs of these results follow broadly along the lines of Banach fixed-point theorems.