This function calculates the ergodic and outage capacity of a MIMO Rayleigh
channel considering no CSIT (equal power allocation) and perfect CSIT
(waterfilling power allocation). In both cases perfect CSIR is assumed. The
channel is assumed to be spatially correlated according to a Kronecker
model but temporally uncorrelated.
I. C. Wong, Z. Shen, J. G. Andrews, and B. L. Evans, ``A Low Complexity Algorithm for Proportional Resource allocation in OFDMA Systems , Proc. IEEE Int. Work. Signal Processing Systems, 針對(duì)這篇文章給出的源代碼
Recently millimeter-wave bands have been postu-
lated as a means to accommodate the foreseen extreme bandwidth
demands in vehicular communications, which result from the
dissemination of sensory data to nearby vehicles for enhanced
environmental awareness and improved safety level. However, the
literature is particularly scarce in regards to principled resource
allocation schemes that deal with the challenging radio conditions
posed by the high mobility of vehicular scenarios
To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation
(5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy
efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation
(4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing
of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been
considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale
antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed
antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can
be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO
and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI)
acquisition, wireless transmission technology, and resource allocation.
We are currently witnessing an increase in telecommunications norms and
standards given the recent advances in this domain. The increasing number of
normalized standards paves the way for an increase in the range of offers and
services available for each consumer. Moreover, the majority of available radio
frequencies have already been allocated.
Mobile and wireless communication systems are a prominent communications
technology of the twenty-first century with profound economic and social impacts
in practically all parts of the world. The current state of wireless communication
systems allows for a much wider scope of applications than what it used to be
originally, that is, to be a mobile extension of the public switched telephone
network.
The radio spectrum is one of the most precious resources which must be managed
to ensure efficient access for the wireless communication services which use it. The
allocation and management of spectrum are administered by the regulatory
authorities. Traditionally, spectrum allocation is carried out exclusively of its use in
large geographic areas and assigning frequency bands to specific users or service
providers is proved to be inefficient. Recently, substantial knowledge about
dynamic spectrum access scheme has been accumulated to enable efficient spectrum
sharing.
Resource allocation is an important issue in wireless communication networks. In
recent decades, cognitive radio technology and cognitive radio-based networks have
obtained more and more attention and have been well studied to improve spectrum
utilization and to overcomethe problem of spectrum scarcity in future wireless com-
munication systems. Many new challenges on resource allocation appear in cogni-
tive radio-based networks. In this book, we focus on effective solutions to resource
allocation in several important cognitive radio-based networks, including a cogni-
tive radio-basedopportunisticspectrum access network, a cognitiveradio-basedcen-
tralized network, a cognitive radio-based cellular network, a cognitive radio-based
high-speed vehicle network, and a cognitive radio-based smart grid.
