Abstract-Novel transmit antenna selection techniques are conceived for Spatial Modulation (SM) systems and their symbol error rate (SER) performance is investigated. Specifically, low-complexity Euclidean Distance optimized Antenna Selection (EDAS) and Capacity Optimized Antenna Selection (COAS) are studied. It is observed that the COAS scheme gives a better SER performance than the EDAS scheme. We show that the proposed antenna selection based SM systems are capable of attaining a significant gain in signal-to-noise ratio (SNR) compared to conventional SM systems, and also outperform the conventional MIMO systems employing antenna selection at both low and medium SNRs.Index Terms-Spatial modulation, antenna selection, diversity, limited feedback, capacity. I. PRELIMINARIESW HEN considering a Spatial Modulation (SM) system [1]- [4] having N r receive and N t transmit antennas, which relies on a single RF chain at the transmitter, we have the system model of y = √ ρh i s + n, where y ∈ C Nr is the received signal vector, ρ is the average Signal-to-Noise Ratio (SNR) at each receive antenna, s is a random symbol selected from a unit-energy M -QAM or -PSK signal set represented by S, h i is the channel vector corresponding to the i th transmit antenna, and n ∈ C Nr is the noise vector. The entries of both n and of the channel matrix H obey the circularly symmetric complex-valued Gaussian distribution CN (0, 1). In SM, the input bitstream is divided into blocks of log 2 (N t M ) bits and in each such block, log 2 M bits select a symbol s from an M -QAM or M -PSK signal set, while log 2 N t bits select an antenna i out of N t transmit antennas for the transmission of the selected symbol s. Therefore, an SM symbol is comprised of the transmit antenna index and of the transmitted symbol from a conventional signal set. Let L = {i} Nt i=1 represent the set of transmit antenna indices. Assuming perfect Channel State Information at the Receiver (CSIR), the Maximum Likelihood (ML) detector conceived for this SM scheme is given by (î,ŝ) ML = arg min i∈L, s∈S y − √ ρh i s 2 2 . Let A represent the event of an antenna index error and S represent the event of a transmitted symbol error under ML detection. Then, the probability of a SM symbol error is given by P e (SM ) = Pr(A) + Pr(S, A c ), where A c represents the complement of A. Bounds on Pr(A) and Pr(S, A c ) can be easily derived, and are given byandIt is clear from (2) and (4) 1 that the diversity order of both Pr(A) and Pr(S, A c ) is only N r and hence the diversity order of P e (SM ) is N r . Fig. 1 plots P e (SM ), Pr(A) and Pr(S, A c ) explicitly, considering an SM system having N t = 4 and N r = 2 for various throughputs. Two important observations may be inferred from these plots:1) The diversity order (slope of the SER curve), associated with Pr(A) and Pr(S, A c ) are the same as formulated in (2) and (4). 2) As the number of bits/symbol increases (size of the QAM constellation), Pe(SM) is dominated by the probability Pr(S, A c ).Observe that Pr(S, A c ) given in ...
Spatial Modulation (SM) is a recently developed low-complexity Multiple-Input Multiple-Output scheme that jointly uses antenna indices and a conventional signal set to convey information. It has been shown that the Maximum-Likelihood (ML) detector of an SM system involves joint detection of the transmit antenna index and of the transmitted symbol, hence, the ML search complexity grows linearly with the number of transmit antennas and the size of the signal set. To circumvent the problem, we show that the ML search complexity of an SM system may be rendered independent of the constellation size, provided that the signal set employed is a square-or a rectangular-QAM. Furthermore, we derive bounds for the capacity of the SM system and derive the optimal power allocation between the data and the training sequences by maximizing the worst-case capacity bound of the SM system operating with imperfect channel state information. We show, with the aid of our simulation results, that the proposed detector is ML-optimal, despite its lowest complexity amongst the existing detectors. Furthermore, we show that employing the proposed optimal power allocation provides a substantial gain in terms of the SM system's capacity as well as signal-to-noise ratio compared to its equal-power-allocation counterpart. Finally, we compare the performance of the SM system to that of the conventional Multiple-Input Multiple-Output (MIMO) system and show that the SM system is capable of outperforming the conventional MIMO system by a significant margin, when both the systems are employing optimal power splitting.
Y. Guan and Z. Liu are with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (e-mail: EYLGuan@ntu.edu.sg; zilongliu@ntu.edu.sg).S. Sugiura is with the Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Koganei,
Abstract-We present an open-source, realtime, embedded implementation of a foot-mounted, zero-velocity-update-aided inertial navigation system. The implementation includes both hardware design and software, uses off-the-shelf components and assembly methods, and features a standard USB interface. The software is written in C and can easily be modified to run user implemented algorithms. The hardware design and the software are released under permissive open-source licenses and production files, source code, documentation, and further resources are available at www.openshoe.org. The reproduction cost for a single unit is below $800, with the inertial measurement unit making up the bulk ($700). The form factor of the implementation is small enough for it to be integrated in the sole of a shoe. A performance evaluation of the system shows a position errors for short trajectories (<100 [m]) of ± 0.2-1 % of the traveled distance, depending on the shape of trajectory.
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