This paper addresses the performance improvement that structured pilot assignment policies can bring about, relative to a random pilot assignment, in cell-free massive MIMO wireless networks. It is shown that structured policies can deliver a multiple-fold reduction in the pilot overhead required to keep pilot contamination at some acceptably low level. While the implementation of the structured policies considered in the paper might require some degree of centralized control, their performance also captures what distributed nonrandom pilot assignment schemes (e.g., greedy or collision-detecting algorithms) can hope to approach.

We present a modification of conjugate beamforming for the forward link of cell-free massive MIMO networks. This modification eliminates the self-interference and yields a performance that, without forward pilots, closely approaches what would be achieved with such pilots in place. The simplicity of conjugate beamforming is preserved, with no need for matrix inversions, at the expense of fading-rate coordination among the access points.

This paper formulates linear MMSE receivers that are both network-and user-centric for the uplink of cell-free wireless networks with centralized processing. Precisely, every user's reception involves a distinct subset of access points (APs) while every AP participates in the reception of a distinct subset of users, hence the moniker subset MMSE receivers. These subsets, defined on the basis of the large-scale channel gains between users and APs, capture the most relevant signal and interference contributions while disregarding those whose processing is costineffective and whose associated channel estimations would incur unnecessary overheads. With that, subset reception approaches the performance of network-wide MMSE reception, offering a multiple-fold improvement over cellular and matched-filtering counterparts, while being scalable in terms of cost and channel estimation. Moreover, because the subsets overlap considerably, they can sometimes be advantageously combined and the computation of the corresponding receivers can share a hefty amount of processing.

In this study, a group of wireless sensors are tasked to trace indoor obstacles without the need to sense them, directly. The authors introduce a novel framework based on compressed sensing theory that allows sensors to map twodimensional spatial details, non-invasively. By exploiting an alternative projection method which reduces the randomness nature of previous works, the resulting measurement matrix can provide linear samples from an unknown environment more efficiently. It is shown that how sparse representation of the spatial parameters in some domains can be utilised in order to realise obstacle mapping with minimum number of wireless transmissions and receptions. In addition, theoretical analyses along with simulation results illustrate premier performance of their framework. Furthermore, they test their method in different circumstances and show how drawbacks such as walls, bulkheads, and environmental constraints can affect the reconstruction performance. Therefore, they proposed two algorithms (i.e. reducing wall effect and reducing bulkhead effect) in order to decrease the impression of walls and bulkheads which is supported theoretically. Finally, a well-applicable scenario based on their framework is defined and proposing the next best transmitter algorithm remarkable results are achieved.

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