Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMajority Based TAS/MRC Scheme in Downlink NOMA Network with Channel Estimation Errors and Feedback Delay
118
0
0.0
(
0
)
Added by
Mahmoud Aldababsa
Publication date
2019
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The antenna selection (AS) in non-orthogonal multiple access (NOMA) networks is still a challenging problem since finding optimal AS solution may not be available for all channel realizations and has quite computational complexity when it exists. For this reason, in this paper, we develop a new suboptimal solution, majority based transmit antenna selection (TAS-maj), with significant reduction in computational complexity. The TAS-maj basically selects the transmit antenna with the majority. It is more efficient when compared to previously proposed suboptimal AS algorithms, namely max-max-max (A^3) and max-min-max (AIA) because these schemes are merely interested in optimizing the performance of the strongest and weakest users, respectively at the price of worse performance for the remaining users. On the other hand, the TAS-maj scheme yields better performance for more than half of mobile users in the NOMA networks. In this paper, we consider a multiple-input multiple-output communication system, where all the nodes are equipped with multi-antenna. Besides the TAS-maj is employed at the base station, a maximal ratio combining (MRC) is also employed at each mobile user in order to achieve superior performance. The impact of the channel estimation errors (CEEs) and feedback delay (FD) on the performance of the TAS-maj/MRC scheme is studied in the NOMA network over Nakagami-m fading channels.
rate research
Read More
This paper studies the performance of a downlink non-orthogonal multiple access (NOMA) based cooperative network with maximal ratio transmission/receive antenna selection (MRT/RAS) over Nakagami-m fading channels in the presence of channel estimation errors (CEEs). In the system, a base station communicates with multiple mobile users through a half duplex channel state information based amplify-and-forward relay. All nodes are equipped with multiple antennas and the hybrid diversity technique MRT/RAS is employed in both hops. The outage behavior of the system is investigated by driving closed-form expression for outage probability (OP). In addition, the corresponding lower and upper bounds of the derived OP are obtained. Moreover, the behavior of the system is studied in high signal-to-noise ratio region by obtaining an error floor value in the presence of CEE as well as achieving diversity and array gains in the absence of CEE. Finally, the analytical results in the presence and absence of the CEEs are verified by the Monte Carlo simulations. Results show that the MRT/RAS scheme enhances the OP significantly and is much more robust to the CEEs in comparison with the single antenna case.
This paper investigates the application of deep deterministic policy gradient (DDPG) to intelligent reflecting surface (IRS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks. The deployment of the UAV equipped with an IRS is important, as the UAV increases the flexibility of the IRS significantly, especially for the case of users who have no line of sight (LoS) path to the base station (BS). Therefore, the aim of this letter is to maximize the sum rate by jointly optimizing the power allocation of the BS, the phase shifting of the IRS and the horizontal position of the UAV. Because the formulated problem is not convex, the DDPG algorithm is utilized to solve it. The computer simulation results are provided to show the superior performance of the proposed DDPG based algorithm.
For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. We divide the whole detection scheme into two modules: slot-wise multi-user detection (SMD) and combined signal and channel estimation (CSCE). SMD is designed to decouple the transmissions of different users by leveraging the approximate message passing (AMP) algorithms, and CSCE is designed to deal with the nonlinear coupling of activity state, channel coefficient and transmit signal of each user separately. To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm. JUICESD-RIGM achieves a performance close to JUICESD with a much lower complexity. Capitalizing on the feature of RIGM, we further analyze the performance of JUICESD-RIGM with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and the derived state evolution method predicts the system performance accurately.
Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence, allocating an orthogonal pilot tone for each coherence bandwidth leads to excessive number of pilots. We leverage generative adversarial networks (GANs) to accurately estimate frequency selective channels with few pilots at low SNR. The proposed estimator first learns to produce channel samples from the true but unknown channel distribution via training the generative network, and then uses this trained network as a prior to estimate the current channel by optimizing the networks input vector in light of the current received signal. Our results show that at an SNR of -5 dB, even if a transceiver with one-bit phase shifters is employed, our design achieves the same channel estimation error as an LS estimator with SNR = 20 dB or the LMMSE estimator at 2.5 dB, both with fully digital architectures. Additionally, the GAN-based estimator reduces the required number of pilots by about 70% without significantly increasing the estimation error and required SNR. We also show that the generative network does not appear to require retraining even if the number of clusters and rays change considerably.
The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
