Channel characterization is an important step in the design of wireless communication systems. While channel sounding procedures are a useful method in determining channel behavior, they also require expensive and time consuming procedures and equipment. Ray tracing has been an important substitute for measurement in deterministic channel modeling and characterization of the wireless channel. Much has been done to improve the precision and efficiency of this method for lower frequency bands (generally below 5 GHz) over the years. Recently, with the worldwide announcement of a broad unlicensed band in the millimeter wave spectrum around 60 GHz, a great amount of attention has been paid to this frequency band, previously considered un-utilizable ([1, 2]). However some basic dissimilarity between the 60 GHz and UHF bands has brought about the need for modification of the methods previously used. In this paper the focus has been placed on two propagation mechanisms: diffraction and rough surface scattering, and the impact of each on over-all channel response predictions have been investigated.
Millimeter (mm) wave massive MIMO has the potential for delivering orders of magnitude increases in mobile data rates, with compact antenna arrays providing narrow steerable beams for unprecedented levels of spatial reuse. A fundamental technical bottleneck, however, is rapid spatial channel estimation and beam adaptation in the face of mobility and blockage. Recently proposed compressive techniques which exploit the sparsity of mm wave channels are a promising approach to this problem, with overhead scaling linearly with the number of dominant paths and logarithmically with the number of array elements. Further, they can be implemented with RF beamforming with low-precision phase control. However, these methods make implicit assumptions on long-term phase coherence that are not satisfied by existing hardware. In this paper, we propose and evaluate a noncoherent compressive channel estimation technique which can estimate a sparse spatial channel based on received signal strength (RSS) alone, and is compatible with off-the-shelf hardware. The approach is based on cascading phase retrieval (i.e., recovery of complex-valued measurements from RSS measurements, up to a scalar multiple) with coherent compressive estimation. While a conventional cascade scheme would multiply two measurement matrices to obtain an overall matrix whose entries are in a continuum, a key novelty in our scheme is that we constrain the overall measurement matrix to be implementable using coarsely quantized pseudorandom phases, employing a virtual decomposition of the matrix into a product of measurement matrices for phase retrieval and compressive estimation. Theoretical and simulation results show that our noncoherent method scales almost as well with array size as its coherent counterpart, thus inheriting the scalability and low overhead of the latter.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.