Relaying techniques, in which a source node communicates to a destination node
with the help of a relay, have been proposed as a cost-effective solution to address
the increasing demand for high data rates and reliable services over the air. As
such, it is crucial to design relay systems that are able to not only provide high
spectral efficiency, but also fully exploit the diversity of the relay channel.
The growing interest for high data rate wireless communications over the last few decades
gave rise to the emergence of a number of wideband wireless systems. The resulting scarcity
of frequency spectrum has been forcing wireless system designers to develop methods that
will push the spectral efficiency to its limit.
Cognitive radio has emerged as a promising technology for maximizing the utiliza-
tion of the limited radio bandwidth while accommodating the increasing amount of
services and applications in wireless networks. A cognitive radio (CR) transceiver
is able to adapt to the dynamic radio environment and the network parameters to
maximize the utilization of the limited radio resources while providing flexibility in
wireless access. The key features of a CR transceiver are awareness of the radio envi-
ronment (in terms of spectrum usage, power spectral density of transmitted/received
signals, wireless protocol signaling) and intelligence.
The recent developments in full duplex (FD) commu-
nication promise doubling the capacity of cellular networks using
self interference cancellation (SIC) techniques. FD small cells
with device-to-device (D2D) communication links could achieve
the expected capacity of the future cellular networks (5G). In
this work, we consider joint scheduling and dynamic power
algorithm (DPA) for a single cell FD small cell network with
D2D links (D2DLs). We formulate the optimal user selection and
power control as a non-linear programming (NLP) optimization
problem to get the optimal user scheduling and transmission
power in a given TTI. Our numerical results show that using
DPA gives better overall throughput performance than full power
transmission algorithm (FPA). Also, simultaneous transmissions
(combination of uplink (UL), downlink (DL), and D2D occur
80% of the time thereby increasing the spectral efficiency and
network capacity
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.
In order to improve the spectral efficiency in wireless communications, multiple
antennas are employed at both transmitter and receiver sides, where the resulting
system is referred to as the multiple-input multiple-output (MIMO) system. In
MIMO systems, it is usually requiredto detect signals jointly as multiple signals are
transmitted through multiple signal paths between the transmitter and the receiver.
This joint detection becomes the MIMO detection.
The investigation of the propagation channel is becoming more and more important in mod-
ern wireless communication. The demand for spectral efficiency motivates exploitation of
all channels that can possibly be used for communications. Nowadays, a common trend for
designing physical layer algorithms is to adapt the transceiving strategy, either by maximizing
the diversity gains or by utilizing the coherence of the channels to improve the signal-to-noise
power ratio.
This paper presents a Hidden Markov Model (HMM)-based speech
enhancement method, aiming at reducing non-stationary noise from speech
signals. The system is based on the assumption that the speech and the noise
are additive and uncorrelated. Cepstral features are used to extract statistical
information from both the speech and the noise. A-priori statistical
information is collected from long training sequences into ergodic hidden
Markov models. Given the ergodic models for the speech and the noise, a
compensated speech-noise model is created by means of parallel model
combination, using a log-normal approximation. During the compensation, the
mean of every mixture in the speech and noise model is stored. The stored
means are then used in the enhancement process to create the most likely
speech and noise power spectral distributions using the forward algorithm
combined with mixture probability. The distributions are used to generate a
Wiener filter for every observation. The paper includes a performance
evaluation of the speech enhancer for stationary as well as non-stationary
noise environment.
The explosion in demand for wireless services experienced over the past 20 years
has put significant pressure on system designers to increase the capacity of the
systems being deployed. While the spectral resource is very scarce and practically
exhausted, the biggest possibilities are predicted to be in the areas of spectral reuse
by unlicensed users or in exploiting the spatial dimension of the wireless channels.
The former approach is now under intense development and is known as the cogni-
tive radio approach (Haykin 2005).
