3,847 research outputs found
Correlated shadowing and fading characterization of MIMO off-body channels by means of multiple autonomous on-body nodes
In off-body communication systems low-cost and compact transceivers are important for realistic applications. An autonomous off-body wireless node was designed and integrated onto a textile antenna. Channel measurements were performed for an indoor non line-off-sight 4x2 MIMO (Multiple-Input Multiple-Output) link using four off-body transmitting nodes and two similar fixed receiving nodes. The channel behavior is characterized as Rayleigh fading with lognormal shadowing and is fitted to a model determining fading and shadowing correlation matrices. The physics of the propagation is captured accurately by the model which is further used to simulate a link using diversity by means of Selection Combining, as implemented on the wireless nodes. The performance of measured and simulated links is compared in terms of outage probability level. The measurements and analysis confirm that the correlated shadowing and fading model is relevant for realistic off-body networks employing diversity by means of Selection Combining
Energy Efficient Transmission over Space Shift Keying Modulated MIMO Channels
Energy-efficient communication using a class of spatial modulation (SM) that
encodes the source information entirely in the antenna indices is considered in
this paper. The energy-efficient modulation design is formulated as a convex
optimization problem, where minimum achievable average symbol power consumption
is derived with rate, performance, and hardware constraints. The theoretical
result bounds any modulation scheme of this class, and encompasses the existing
space shift keying (SSK), generalized SSK (GSSK), and Hamming code-aided SSK
(HSSK) schemes as special cases. The theoretical optimum is achieved by the
proposed practical energy-efficient HSSK (EE-HSSK) scheme that incorporates a
novel use of the Hamming code and Huffman code techniques in the alphabet and
bit-mapping designs. Experimental studies demonstrate that EE-HSSK
significantly outperforms existing schemes in achieving near-optimal energy
efficiency. An analytical exposition of key properties of the existing GSSK
(including SSK) modulation that motivates a fundamental consideration for the
proposed energy-efficient modulation design is also provided
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
In downlink multi-antenna systems with many users, the multiplexing gain is
strictly limited by the number of transmit antennas and the use of these
antennas. Assuming that the total number of receive antennas at the
multi-antenna users is much larger than , the maximal multiplexing gain can
be achieved with many different transmission/reception strategies. For example,
the excess number of receive antennas can be utilized to schedule users with
effective channels that are near-orthogonal, for multi-stream multiplexing to
users with well-conditioned channels, and/or to enable interference-aware
receive combining. In this paper, we try to answer the question if the data
streams should be divided among few users (many streams per user) or many users
(few streams per user, enabling receive combining). Analytic results are
derived to show how user selection, spatial correlation, heterogeneous user
conditions, and imperfect channel acquisition (quantization or estimation
errors) affect the performance when sending the maximal number of streams or
one stream per scheduled user---the two extremes in data stream allocation.
While contradicting observations on this topic have been reported in prior
works, we show that selecting many users and allocating one stream per user
(i.e., exploiting receive combining) is the best candidate under realistic
conditions. This is explained by the provably stronger resilience towards
spatial correlation and the larger benefit from multi-user diversity. This
fundamental result has positive implications for the design of downlink systems
as it reduces the hardware requirements at the user devices and simplifies the
throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/one-or-multiple-stream
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