In this paper, we consider the problem of user scheduling and pilot assignment in TDD multicell multiuser Massive MIMO systems. While in TDD systems the channel is acquired using uplink pilots, we propose a scheme that utilizes additional downlink probing in order to improve the spectral efficiency. The idea is to dynamically assign mobile users to different clusters based on the directions of their channels through the use of downlink reference beams. This will result in forcing the interference to be centered in semiorthogonal subspaces without the need for important feedback and therefore enabling reduction of the pilot contamination effect. The scheduled users in each cluster employs orthogonal training sequences with a pilot reuse factor of 1 among the clusters. We then propose a graphical framework for pilot assignment. We show that this problem can be modeled as a max cut problem and we provide an approximation algorithm that optimizes the pilot allocation.

Massive MIMO is considered as one of the key enablers of the next generation 5G networks. With a high number of antennas at the BS, both spectral and energy efficiencies can be improved. Unfortunately, the downlink channel estimation overhead scales linearly with the number of antenna. This does not create complications in Time Division Duplex (TDD) systems since the channel estimate of the uplink direction can be directly utilized for link adaptation in the downlink direction. However, this channel reciprocity is unfeasible for the Frequency Division Duplex (FDD) systems where different physical transmission channels are existent for the uplink and downlink. In the aim of reducing the amount of Channel State Information (CSI) feedback for FDD systems, the promising method of two stage beamforming transmission was introduced. The performance of this transmission scheme is however highly influenced by the users grouping and selection mechanisms. In this paper, we first introduce a new similarity measure coupled with a novel clustering technique to achieve the appropriate users partitioning. We also use graph theory to develop a low complexity groups scheduling scheme that outperforms currently existing methods in both sum-rate and throughput fairness. This performance gain is demonstrated through computer simulations.

Using a network of cache enabled small cells, traffic during peak hours can be reduced considerably through proactively fetching the content that is most probable to be requested. In this paper, we aim at exploring the impact of proactive caching on an important metric for future generation networks, namely, energy efficiency (EE). We argue that, exploiting the correlation in user content popularity profiles in addition to the spatial repartitions of users with comparable request patterns, can result in considerably improving the achievable energy efficiency of the network. In this paper, the problem of optimizing EE is decoupled into two related subproblems. The first one addresses the issue of content popularity modeling. While most existing works assume similar popularity profiles for all users in the network, we consider an alternative caching framework in which, users are clustered according to their content popularity profiles. In order to showcase the utility of the proposed clustering scheme, we use a statistical model selection criterion, namely Akaike information criterion (AIC). Using stochastic geometry, we derive a closed-form expression of the achievable EE and we find the optimal active small cell density vector that maximizes it. The second subproblem investigates the impact of exploiting the spatial repartitions of users with comparable request patterns. After considering a snapshot of the network, we formulate a combinatorial optimization problem that enables to optimize content placement such that the used transmission power is minimized. Numerical results show that the clustering scheme enable to considerably improve the cache hit probability and consequently the EE compared with an unclustered approach. Simulations also show that the small base station allocation algorithm results in improving the energy efficiency and hit probability. arXiv:1703.07646v3 [cs.IT] 8 Nov 20172

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.