5,749 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Dual-Branch MRC Receivers under Spatial Interference Correlation and Nakagami Fading
Despite being ubiquitous in practice, the performance of maximal-ratio
combining (MRC) in the presence of interference is not well understood. Because
the interference received at each antenna originates from the same set of
interferers, but partially de-correlates over the fading channel, it possesses
a complex correlation structure. This work develops a realistic analytic model
that accurately accounts for the interference correlation using stochastic
geometry. Modeling interference by a Poisson shot noise process with
independent Nakagami fading, we derive the link success probability for
dual-branch interference-aware MRC. Using this result, we show that the common
assumption that all receive antennas experience equal interference power
underestimates the true performance, although this gap rapidly decays with
increasing the Nakagami parameter of the interfering links. In
contrast, ignoring interference correlation leads to a highly optimistic
performance estimate for MRC, especially for large . In the low
outage probability regime, our success probability expression can be
considerably simplified. Observations following from the analysis include: (i)
for small path loss exponents, MRC and minimum mean square error combining
exhibit similar performance, and (ii) the gains of MRC over selection combining
are smaller in the interference-limited case than in the well-studied
noise-limited case.Comment: to appear in IEEE Transactions on Communication
Multiuser MIMO-OFDM for Next-Generation Wireless Systems
This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems
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