By Howard Huang, Constantinos B. Papadias, Sivarama Venkatesan (auth.)
As the theoretical foundations of multiple-antenna thoughts evolve and as those multiple-input multiple-output (MIMO) thoughts turn into crucial for delivering excessive facts charges in instant structures, there's a turning out to be have to comprehend the functionality limits of MIMO in sensible networks. to handle this desire, MIMO verbal exchange for mobile Networks presents a scientific description of MIMO know-how periods and a framework for MIMO method layout that takes under consideration the fundamental physical-layer positive aspects of useful mobile networks.
In distinction to works that target the theoretical functionality of summary MIMO channels, MIMO communique for mobile Networks emphasizes the sensible functionality of life like MIMO platforms. A unified set of process simulation effects highlights relative functionality profits of alternative MIMO thoughts and offers insights into how top to exploit a number of antennas in mobile networks below a variety of conditions.
MIMO communique for mobile Networks describes single-user, multiuser, community MIMO applied sciences and system-level features of mobile networks, together with channel modeling, source scheduling, interference mitigation, and simulation methodologies. the most important techniques are provided with enough generality to be utilized to a variety of instant platforms, together with these in keeping with mobile criteria reminiscent of LTE, LTE-Advanced, WiMAX, and WiMAX2. The publication is meant to be used by means of graduate scholars, researchers, and working towards engineers drawn to the physical-layer layout of cutting-edge instant systems.
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Extra info for MIMO Communication for Cellular Networks
This capacity can be achieved using the MMSE-SIC to disentangle the users’ signals. 17). 1 Overview of MIMO fundamentals 15 the SU-MIMO link with an MMSE-SIC and without CSI and transmitter precoding. Multiple- access channel User 1 data bits User 1 Coding, modulation User data bits User s : Coding, modulation M M Joint demod, decoding: MMSESIC User 1 estimated data bits User 1 Demod, decoding User 1 estimated data bits User estimated data bits Broadcast channel User 1 data bits User data bits Joint coding, modulation: DPC User 1 Precoding User 1 : Precoding M M Σ M M User : Demod, decoding User estimated data bits Fig.
5 The Ricean MIMO channel model Similarly to the case of scalar channels, when a line of sight (LOS) exists between the transmitter and receiver, the channel is modeled as the sum of a random part representing the non-LOS component and a deterministic part that represents the LOS component. 11) H= 1+K where K ≥ 0 is the Rice factor (also called the “K factor”), HD denotes the LOS deterministic channel matrix and HR denotes the random channel matrix that can be modeled according to any of the MIMO channel models presented above.
R. 23) (If r < N there are also N −r equations of the type xi = ni , i = r +1, . . 23) describes an ensemble of r parallel, non-interfering SISO channels, with gains λ1 , λ2 , . . , λr and noise variance σ 2 . 2. 23), the SNR on the ith SISO channel is ρi = λ2i Pi /σ 2 , and the rate achievable over it is Ri = log2 (1 + ρi ). 2 Single-user MIMO capacity 47 M M M M M M M Fig. 2 Decomposition of the MIMO channel into r constituent SISO channels, where r is the rank of H. , the sum of the rates over the individual SISO channels.
MIMO Communication for Cellular Networks by Howard Huang, Constantinos B. Papadias, Sivarama Venkatesan (auth.)