9 research outputs found
Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications
A fundamental challenge of the large-scale Internet-of-Things lies in how to
support massive machine-type communications (mMTC). This letter proposes a
media modulation based mMTC solution for increasing the throughput, where a
massive multi-input multi-output based base station (BS) is used for enhancing
the detection performance. For such a mMTC scenario, the reliable active device
detection and data decoding pose a serious challenge. By leveraging the
sparsity of the uplink access signals of mMTC received at the BS, a compressive
sensing based massive access solution is proposed for tackling this challenge.
Specifically, we propose a block sparsity adaptive matching pursuit algorithm
for detecting the active devices, whereby the block-sparsity of the uplink
access signals exhibited across the successive time slots and the structured
sparsity of media modulated symbols are exploited for enhancing the detection
performance. Moreover, a successive interference cancellation based structured
subspace pursuit algorithm is conceived for data demodulation of the active
devices, whereby the structured sparsity of media modulation based symbols
found in each time slot is exploited for improving the detection performance.
Finally, our simulation results verify the superiority of the proposed scheme
over state-of-the-art solutions.Comment: submitted to IEEE Transactions on Vehicular Technology [Major
Revision
Iterative signal detection for large scale GSM-MIMO systems
Generalized spatial modulations (GSM) represent a novel multiple input multiple output (MIMO) scheme which can be regarded as a compromise between spatial multiplexing MIMO and conventional spatial modulations (SM), achieving both spectral efficiency (SE) and energy efficiency (EE). Due to the high computational complexity of the maximum likelihood detector (MLD) in large antenna settings and symbol constellations, in this paper we propose a lower complexity iterative suboptimal detector. The derived algorithm comprises a sequence of simple processing steps, namely an unconstrained Euclidean distance minimization problem, an element wise projection over the signal constellation and a projection over the set of valid active antenna combinations. To deal with scenarios where the number of possible active antenna combinations is large, an alternative version of the algorithm which adopts a simpler cardinality projection is also presented. Simulation results show that, compared with other existing approaches, both versions of the proposed algorithm are effective in challenging underdetermined scenarios where the number of receiver antennas is lower than the number of transmitter antennas.info:eu-repo/semantics/acceptedVersio
Efficient recovery algorithm for discrete valued sparse signals using an ADMM approach
Motivated by applications in wireless communications, in this paper we propose a reconstruction algorithm for sparse signals whose values are taken from a discrete set, using a limited number of noisy observations. Unlike conventional compressed sensing algorithms, the proposed approach incorporates knowledge of the discrete valued nature of the signal in the detection process. This is accomplished through the alternating direction method of the multipliers which is applied as a heuristic to decompose the associated maximum likelihood detection problem in order to find candidate solutions with a low computational complexity order. Numerical results in different scenarios show that the proposed algorithm is capable of achieving very competitive recovery error rates when compared with other existing suboptimal approaches.info:eu-repo/semantics/publishedVersio
Spatial Coded Modulation
In this paper, we propose a spatial coded modulation (SCM) scheme, which
improves the accuracy of the active antenna detection by coding over the
transmit antennas. Specifically, the antenna activation pattern in the SCM
corresponds to a codeword in a properly designed codebook with a larger minimum
Hamming distance than its counterpart conventional spatial modulation. As the
minimum Hamming distance increases, the reliability of the active antenna
detection is directly enhanced, which in turn improves the demodulation of the
modulated symbols and yields a better system reliability. In addition to the
reliability, the proposed SCM scheme also achieves a higher capacity with the
identical antenna configuration compared to the conventional spatial modulation
technique. Moreover, the proposed SCM scheme strikes a balance between spectral
efficiency and reliability by trading off the minimum Hamming distance with the
number of available codewords. The optimal maximum likelihood detector is first
formulated. Then, a low-complexity suboptimal detector is proposed to reduce
the computational complexity, which has a two-step detection. Theoretical
derivations of the channel capacity and the bit error rate are presented in
various channel scenarios, i.e., Rayleigh, Rician, Nakagami-m, imperfect
channel state information, and spatial correlation. Further derivation on
performance bounding is also provided to reveal the insight of the benefit of
increasing the minimum Hamming distance. Numerical results validate the
analysis and demonstrate that the proposed SCM outperforms the conventional
spatial modulation techniques in both channel capacity and system reliability.Comment: 30 pages, 17 figure
Enhanced performance and efficiency schemes for generalised spatial modulation.
Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2017.Abstract available in PDF file
Efficient Compressive Sensing Detectors for Generalized Spatial Modulation Systems
International audienceGeneralized spatial modulation (GSM) is a novel multiple-input-multiple-output (MIMO) technique, which relies on a sparse use of radio-frequency (RF) front ends at the transmitter. In this paper, low-complexity and compressive-sensing (CS)-based detectors for GSM systems are proposed. First, an extension of the normalized CS detector (E-NCS) based on the orthogonal matching pursuit (OMP) algorithm is proposed, which is shown to be suitable for large-scale GSM implementations, due to its low complexity. Furthermore, to mitigate the error floor effect of the E-NCS detector, two efficient CS (ECS) detectors based on the OMP algorithm are designed with the aid of a preset threshold. Specifically, different searching algorithms are designed, whose objective is to balance computational complexity and system performance. An upper bound for the average bit error probability (ABEP) of the first ECS detector is derived and used to optimize the preset threshold. Simulation results show that the proposed ECS detectors are capable of achieving a considerable reduction in computational complexity, compared with other near-optimal algorithms, with a negligible performance loss