Browsing by Author "Uykan, Zekeriya"

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  • Angle-Delay Features and Distances for Channel Charting
    (2024) Uykan, Zekeriya; Al-Tous, Hanan; Yiğitler, Hüseyin; Jäntti, Riku; Tirkkonen, Olav
     A4 Artikkeli konferenssijulkaisussa
    Channel charting (CC) is an unsupervised machine learning framework for learning a lower-dimensional representation of Channel State Information (CSI), while preserving spatial relations between CSI samples. In this paper, we consider super-resolution features in the angle-delay domain in massive Multiple-Input Multiple-Output (MIMO) systems. We i) treat the angle and delay separately, ii) present the so-called 'Normalized Polar Feature' utilizing the channel statistics of the CSI samples, iii) use the Euclidean distance to compute the dissimilarity matrix, and create the channel chart. Simulation results based on the DeepMIMO data-set show that the proposed super-resolution representation with the Euclidean distance leads to the state-of-the-art quality CC as compared to other CSI features and distances from the literature such as angle-delay-power features with earth mover distance.
  • Composite vector quantization for optimizing antenna locations
    (2018-05-30) Uykan, Zekeriya; Jäntti, Riku
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    In this paper, we study the location optimization problem of remote antenna units (Raus) in generalized distributed antenna systems (GDASs). We propose a composite vector quantization (CVQ) algorithm that consists of unsupervised and supervised terms for Rau location optimization. We show that the CVQ can be used i) to minimize an upper bound to the cell-averaged SNR error for a desired/demanded location-specific SNR function, and ii) to maximize the cell-averaged effective SNR. The CVQ-DAS includes the standard VQ, and thus the well-known squared distance criterion (SDC) as a special case. Computer simulations confirm the findings and suggest that the proposed CVQ-DAS outperforms the SDC in terms of cell-averaged “effective SNR”.
  • Hopfield Neural Network based Uplink/Downlink Transmission Order Optimization for Dynamic Indoor TDD Femtocells
    (2023) Alam, Mirza Nazrul; Jantti, Riku; Uykan, Zekeriya
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The Uplink/Downlink transmission mode or Transmission Order (TO) optimization has recently appeared as a new optimization domain in radio resource management. Such optimization is a combinatorics problem and requires good heuristic algorithm to be approximately solved within short time for the dynamic radio environment. This paper shows how the TO optimization problem in Time Division Duplex (TDD) indoor femtocells can be formulated and solved by the Hopfield Neural Network (HNN) based TO schedulers. Both centralized and distributed versions are analyzed in the context of indoor femtocells. We also examine proposed TO schedulers' system performance in TDD indoor femtocells environment by extensive simulation campaigns. Our simulation results for a large 3-story building including 120 femtocells show that (i) the indoor femtocell system performance is improved up to 13 to 20 percent by the proposed HNN schedulers depending on the number of femtocells, (ii) the proposed TO schedulers converge within the first few epochs. (iii) The performance of the proposed schedulers are justified by a time-consuming but a thorough Genetic Algorithm Scheduler.
  • A modified GADIA-based upper-bound to the capacity of Gaussian general N-relay networks
    (2021-08-04) Uykan, Zekeriya; Jäntti, Riku
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    In this paper, we present a general Gaussian N-relay network by allowing relays to communicate to each other and allowing a direct channel between source and destination as compared to the standard diamond network in Nazaroglu et al. (IEEE Trans Inf Theory 60:6329-6341, 2014) at the cost of extra channel uses. Our main focus is to examine the min-cut bound capacities of the relay network. Very recently, the results in Uykan (IEEE Trans Neural Netw Learn Syst 31:3294-3304, 2020) imply that the GADIA in Babadi and Tarokh (IEEE Trans Inf Theory 56:6228-6252, 2010), a pioneering algorithm in the interference avoidance literature, actually performs max-cut of a given power-domain (nonnegative) link gain matrix in the 2-channel case. Using the results of the diamond network in Nazaroglu et al. (2014) and the results in Uykan (2020), in this paper, we (i) turn the mutual information maximization problem in the Gaussian N-relay network into an upper bound minimization problem, (ii) propose a modified GADIA-based algorithm to find the min-cut capacity bound and (iii) present an upper and a lower bound to its min-cut capacity bound using the modified GADIA as applied to the defined "squared channel gain matrix/graph". Some advantages of the proposed modified GADIA-based simple algorithm are as follows: (1) The Gaussian N-relay network can determine the relay clusters in a distributed fashion and (2) the presented upper bound gives an insight into whether allowing the relays to communicate to each other pays off the extra channel uses or not as far as the min-cut capacity bound is concerned. The simulation results confirm the findings. Furthermore, the min-cut upper bound found by the proposed modified-GADIA is verified by the cut-set bounds found by the spectral clustering based solutions as well.
  • Some Clustering-Based Algorithms for Design of Radial Basis Function Networks
    (1998) Uykan, Zekeriya
     Helsinki University of Technology | Licentiate thesis
  • Stochastic Shadow-Cutting Machine
    (2024-01-01) Uykan, Zekeriya; Jantti, Riku
     A4 Artikkeli konferenssijulkaisussa
    Recently, a new concept called shadow-cuts has recently been proposed for a fully-connected graph whose edge matrix is Hermitian with arbitrary complex numbers. Each neuron is associated with a phase and the sum of shadow cuts is defined as the sum of inter-cluster phased edges. However, the shadow-cut machine is 100% deterministic and therefore its modeling capacity is relatively limited. In this brief, we (i) extend it to stochastic domain which yields the so-called 'Stochastic Shadow-Cutting Machine' (SSCM), and (ii) show that choosing the energy function of the SSCM as the sum of shadow-cuts yields similar phenomena as in those from the statistical mechanics like Ising model, xy-model, pott model, Stochastic Hopfield Networks, etc., Thus, the proposed SSCM provides a general framework to examine various phenomena like the phase changes of the SSCM as the temperature increases. Because the SSCM in low temperatures behaves as an Associative Memory system (i.e., 'ferro-magnet'), it is possible to examine the critical temperatures when the SSCM cannot 'recover/remember' the patterns any more (i.e. 'anti-ferromagnet'), which we define as 'phase change' of the SSCM.