Torrellas, M.; Agustin, A.; Vidal, J. IEEE International Conference on Acoustics, Speech, and Signal Processing p. 2445-2449 DOI: 10.1109/ICASSP.2014.6854039 Data de presentació: 2014-05-04 Presentació treball a congrés
The degrees of freedom (DoF) of the 3-user multiple input multiple output interference channel (3-user MIMO IC) are investigated where there is delayed channel state information at the transmitters (dCSIT). We generalize the ideas of Maleki et al. about Retrospective Interference Alignment (RIA) to be applied to the MIMO IC, where transmitters and receivers are equipped with (M, N) antennas, respectively. We propose a two-phase transmission scheme where the number of slots per phase and number of transmitted symbols are optimized by solving a maximization problem. Finally, we review the existing achievable DoF results in the literature as a function of the ratio between transmitting and receiving antennas ¿ = M/N. The proposed scheme improves all other strategies when ¿ (1/2, 31/32].
Torrellas, M.; Agustin, A.; Vidal, J. IEEE International Conference on Acoustics, Speech, and Signal Processing p. 1155-1159 DOI: 10.1109/ICASSP.2014.6853778 Data de presentació: 2014-05-04 Presentació treball a congrés
This work investigates the degrees of freedom (DoF) of the K-user multiple-input single-output (MISO) interference channel (IC) with imperfect delayed channel state information at the transmitters (dCSIT). For this setting, new DoF inner bounds are provided, and benchmarked with cooperation-based outer bounds. The achievability result is based on a precoding scheme that aligns the interfering received signals through time, exploiting the concept of Retrospective Interference Alignment (RIA). The proposed approach outperforms all previous known schemes. Furthermore, we study the proposed scheme under channel estimation errors (CEE) on the reported dCSIT, and derive a closed-form expression for the achievable DoF with imperfect dCSIT.
Physical Layer (PHY) abstraction aims to model link performance and accelerate system-level analyses of communication systems by reducing simulation complexity and their associated computational costs. We examine a Physical Layer (PHY) abstraction approach based on Stochastic Dynamic Systems (SDS) for easing the requirement of expensive Monte Carlo system-level evaluations. We present the framework, recently
applied in simplified models for Successive Interference Cancellation
(SIC) receivers in the finite and large user limits, and refine
the analysis for a related scheme named Successive Interference
Blocking (SIB), with the objective of evaluating performance
metrics such as the Packet Error Rate using a probability particle
algorithm for approximating the equivalent SDS.
In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genie-aided detector which assumes perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise variance. This is used as a benchmark for comparing with more practical detectors when some or all of these parameters are unknown to the Secondary User (SU). Two scenarios are considered: 1)
the antenna correlation coefficients and PU signal power are unknown to the SU; 2) the antenna correlation coefficients, PU signal power and noise variance are unknown to the SU. To derive sub-optimum detectors for these two scenarios, we apply the Rao test, an asymptotically equivalent test to the Generalized Likelihood Ratio Test (GLRT) that does not require the Maximum Likelihood (ML) estimates of unknown parameters. Additionally, we calculate analytical approximations to the detection and false-alarm probabilities of the proposed detectors and verify them with Monte-Carlo simulations. The simulation results show that these new detectors outperform several recently proposed detectors for CR using multiple antennas.
Spectrum sensing is a key component for enabling the cognitive radio paradigm. In this paper, we propose a novel totally-blind spectrum sensing technique for cognitive radio device equipped with multiple antennas, namely the Space Frequency Cross Product Sensing (SFCPS) algorithm. Existing correlation-based spectrum sensing techniques rely on the assumption that the received signals are correlated and their performance becomes poor when the signal correlation is low. By appropriately combining the received signals from multiple antennas, the proposed method creates new signals that are fully correlated and on which a sensing method is developed. SFCPS performs better than existing correlation-based techniques and with a lower computational complexity for small number of observed samples.