Cellular Interference Alignment: Omni-Directional Antennas and Asymmetric Configurations PROJECT TITLE :Cellular Interference Alignment: Omni-Directional Antennas and Asymmetric ConfigurationsABSTRACT:Though interference alignment (IA) can theoretically achieve the optimal degrees of freedom (DoFs) in the $K$ -user Gaussian interference channel, its direct application comes at the prohibitive value of precoding over exponentially several signaling dimensions. On the other hand, it is known that practical one-shot IA precoding (i.e., linear schemes while not symbol enlargement) provides a vanishing DoFs gain in massive totally connected networks with generic channel coefficients. In our previous work, we introduced the concept of cellular IA for a network topology induced by hexagonal cells with sectors and nearest-neighbor interference. Assuming that neighboring sectors can exchange decoded messages (and not received signal samples) within the uplink, we have a tendency to showed that linear one-shot IA precoding over $M$ transmit/receive antennas can achieve the optimal $M/2$ DoFs per user. In this paper, we tend to extend this framework to networks with omni-directional (non-sectorized) cells and take into account a restricted sensible state of affairs where users have two antennas, and base-stations have 2, 3, or 4 antennas. We tend to provide linear one-shot IA schemes for the $2times a pair of$ , $2times three$ , and $2times 4$ cases, and show the achievability of 3/four, 1, and 7/six DoFs per user, respectively. DoFs converses for one-shot schemes require the answer of a discrete optimization drawback over a range of variables that grows with the network size. We tend to develop a replacement approach to rework such optimization downside into a tractable linear program with considerably fewer variables. This approach is us- d to show that three/four DoFs per user are indeed optimal for one-shot schemes over giant (extended) cellular network with $2times 2$ links. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model Fine Land Cover Classification Using Daily Synthetic Landsat-Like Images at 15-m Resolution
