74 research outputs found
Utility-maximization Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks
Device-to-device(D2D) underlaying communication brings great benefits to the
cellular networks from the improvement of coverage and spectral efficiency at
the expense of complicated transceiver design. With frequency spectrum sharing
mode, the D2D user generates interference to the existing cellular networks
either in downlink or uplink. Thus the resource allocation for D2D pairs should
be designed properly in order to reduce possible interference, in particular
for uplink. In this paper, we introduce a novel bandwidth allocation scheme to
maximize the utilities of both D2D users and cellular users. Since the
allocation problem is strongly NP-hard, we apply a relaxation to the
association indicators. We propose a low-complexity distributed algorithm and
prove the convergence in a static environment. The numerical result shows that
the proposed scheme can significant improve the performance in terms of
utilities.The performance of D2D communications depends on D2D user locations,
the number of D2D users and QoS(Quality of Service) parameters
Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks
In cognitive radio networks, the channel gain between primary transceivers,
namely, primary channel gain, is crucial for a cognitive transmitter (CT) to
control the transmit power and achieve spectrum sharing. Conventionally, the
primary channel gain is estimated in the primary system and thus unavailable at
the CT. To deal with this issue, two estimators are proposed by enabling the CT
to sense primary signals. In particular, by adopting the maximum likelihood
(ML) criterion to analyze the received primary signals, a ML estimator is first
developed. After demonstrating the high computational complexity of the ML
estimator, a median based (MB) estimator with proved low complexity is then
proposed. Furthermore, the estimation accuracy of the MB estimation is
theoretically characterized. By comparing the ML estimator and the MB estimator
from the aspects of the computational complexity as well as the estimation
accuracy, both advantages and disadvantages of two estimators are revealed.
Numerical results show that the estimation errors of the ML estimator and the
MB estimator can be as small as dB and dB, respectively.Comment: Submitted to IEEE Transactions on Communication
Detect-and-forward relaying aided cooperative spatial modulation for wireless networks
A novel detect-and-forward (DeF) relaying aided cooperative SM scheme is proposed, which is capable of striking a flexible tradeoff in terms of the achievable bit error ratio (BER), complexity and unequal error protection (UEP). More specifically, SM is invoked at the source node (SN) and the information bit stream is divided into two different sets: the antenna index-bits (AI-bits) as well as the amplitude and phase modulation-bits (APM-bits). By exploiting the different importance of the AI-bits and the APM-bits in SM detection, we propose three low-complexity, yet powerful relay protocols, namely the partial, the hybrid and the hierarchical modulation (HM) based DeF relaying schemes. These schemes determine the most appropriate number of bits to be re-modulated by carefully considering their potential benefits and then assigning a specific modulation scheme for relaying the message. As a further benefit, the employment of multiple radio frequency (RF) chains and the requirement of tight inter-relay synchronization (IRS) can be avoided. Moreover, by exploiting the benefits of our low-complexity relaying protocols and our inter-element interference (IEI) model, a low-complexity maximum-likelihood (ML) detector is proposed for jointly detecting the signal received both via the source-destination (SD) and relay-destination (RD) links. Additionally, an upper bound of the BER is derived for our DeF-SM scheme. Our numerical results show that the bound is asymptotically tight in the high-SNR region and the proposed schemes provide beneficial system performance improvements compared to the conventional MIMO schemes in an identical cooperative scenario.<br/
Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry
Massive machine-type communications (mMTC) is a crucial scenario to support
booming Internet of Things (IoTs) applications. In mMTC, although a large
number of devices are registered to an access point (AP), very few of them are
active with uplink short packet transmission at the same time, which requires
novel design of protocols and receivers to enable efficient data transmission
and accurate multi-user detection (MUD). Aiming at this problem, grant-free
non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA,
active devices can directly transmit their preambles and data symbols
altogether within one time frame, without grant from the AP. Compressive
sensing (CS)-based receivers are adopted for non-orthogonal preambles
(NOP)-based MUD, and successive interference cancellation is exploited to
decode the superimposed data signals. In this paper, we model, analyze, and
optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an
aspect of network deployment. Based on the SG network model, we first analyze
the success probability as well as the channel estimation error of the CS-based
MUD in the preamble phase and then analyze the average aggregate data rate in
the data phase. As IoT applications highly demands low energy consumption, low
infrastructure cost, and flexible deployment, we optimize the energy efficiency
and AP coverage efficiency of GF-NOMA via numerical methods. The validity of
our analysis is verified via Monte Carlo simulations. Simulation results also
show that CS-based GF-NOMA with NOP yields better MUD and data rate
performances than contention-based GF-NOMA with orthogonal preambles and
CS-based grant-free orthogonal multiple access.Comment: This paper is submitted to IEEE Internet Of Things Journa
Phase rotation-based precoding for spatial modulation systems
In this study, the authors investigate the benefits of phase-rotation-assisted precoding (PRP) technique in spatial modulation (SM) systems, which are based on maximum free distance dmin. First, a closed-form solution of the maximumdmin PRP matrix is derived for the scenario of having two transmit antennas (Nt = 2). Moreover, two numerical methods are proposed for dealing with the case of Nt > 2. The complexity of the proposed algorithms is presented. The authors simulation results show that the proposed PRP algorithms provide beneficial bit error ratio performance improvements compared with both the conventional SM and with the existing adaptive S
Crystal-field Engineering of Ultrabroadband Mid-infrared Emission in Co2+-doped Nano-chalcogenide Glass Composites
unable and ultrabroadband mid-infrared (MIR) emissions in the range of 2.5–4.5 μm are firstly reported from Co2+-doped nano-chalcogenide (ChG) glass composites. The composites embedded with a variety of binary (ZnS, CdS, ZnSe) and ternary (ZnCdS, ZnSSe) ChG nanocrystals (NCs) can be readily obtained by a simple one-step thermal annealing method. They are highly transparent in the near- and mid-infrared wavelength region. Low-cost and commercially available Er3+-doped fiber lasers can be used as the excitation source. By crystal-field engineering of the embedded NCs through cation- or anion-substitution, the emission properties of Co2+ including its emission peak wavelength and bandwidth can be tailored in a broad spectral range. The phenomena can be accounted for by crystal-field theory. Such nano-ChG composites, perfectly filling the 3–4 μm spectral gap between the oscillations of Cr2+ and Fe2+ doped IIVI ChG crystals, may find important MIR photonic applications (e.g., gas sensing), or can be used directly as an efficient pump source for Fe2+: IIVI crystals which are suffering from lack of pump sources
A Novel Extreme-Learning-Machine Aided Receiver Design for THz-SM With Hardware Imperfections
Terahertz (THz) communication is promising as it can enable ultra-wide-band and ultra-high-rate for various emerging communication services. In this letter, we propose to exploit the extreme learning machine (ELM) network based regressor for simple and low-complexity joint channel estimation (CE) and signal detection (SD) for THz-band spatial modulation (THz-SM) communications impaired by hardware imperfections. Computer simulations show the performance superiority of the proposed joint CE/SD scheme when compared with the state-of-the-art schemes, and other machine learning-based ones, including the support vector machine (SVM), deep neural network (DNN) and some variants of ELM. Specifically, we show that its bit error rate (BER) performance approaches to that of the recently derived maximal likelihood (ML) SD. In addition, the robustness of the proposed scheme is validated by considering two types of background impulsive noises
Perpendicular magnetic anisotropy of full-Heusler films in Pt/Co2FeAl/MgO trilayers
We report on perpendicular magnetic anisotropy (PMA) in a Pt/Co2FeAl/MgO
sandwiched structure with a thick Co2FeAl layer of 2-2.5 nm. The PMA is
thermally stable that the anisotropy energy density Ku is 1.3{\times}106
erg/cm3 for the structure with 2 nm Co2FeAl after annealing at 350 oC. The
thicknesses of Co2FeAl and MgO layers greatly affect the PMA. Our results
provide an effective way to realize relative thick perpendicularly magnetized
Heusler alloy films.Comment: 15 pages,6 figure
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