A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output(MIMO)*** inter user interference(IUI)which is an inevitable problem in MIMO systems becomes controllable wh...
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A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output(MIMO)*** inter user interference(IUI)which is an inevitable problem in MIMO systems becomes controllable when the precoding scheme is *** this paper,the horizontal Gauss-Seidel(HGS)method is proposed as precoding scheme in massive MIMO *** massive MIMO systems,the exact inversion of channel matrix is impractical due to the severe computational ***,the conventionalGauss-Seidel(GS)method is used to approximate the inversion of channel *** GS has good performance by using previous calculation results as ***,the required time for obtaining the precoding symbols is too long due to the sequential process of ***,the HGS with parallel calculation is proposed in this paper to reduce the required *** rows of channel matrix are eliminated for parallel calculation *** addition,HGSuses the ordered channelmatrix to prevent performance degradation which is occurred by parallel *** HGS with proper number of parallelly computed symbols has better performance and reduced required time compared to the traditional GS.
We establish area theorems for iterative detection and decoding (or simply, iterative detection) over coded linear systems, including multiple-input multiple-output channels, intersymbol interference channels, and ort...
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We establish area theorems for iterative detection and decoding (or simply, iterative detection) over coded linear systems, including multiple-input multiple-output channels, intersymbol interference channels, and orthogonal frequency-division multiplexing systems. We propose a linear precoding technique that asymptotically ensures the Gaussianness of the messages passed in iterative detection, as the transmission block length tends to infinity. Area theorems are established to characterize the behavior of the iterative receiver. We show that, for unconstrained signaling, the proposed single-code scheme with linear precoding and iterative linear minimum mean-square error (LMMSE) detection is potentially information lossless, under various assumptions on the availability of the channel state information at the transmitter. We further show that, for constrained signaling, our proposed single-code scheme considerably outperforms the conventional multicode parallel transmission scheme based on singular value decomposition and water-filling power allocation. Numerical results are provided to verify our analysis.
Rate-Splitting Multiple Access (RSMA) has emerged as a robust transmission strategy for multi-antenna wireless systems. This paper investigates the performance of RSMA in a downlink Decode-and-Forward (DF) relay netwo...
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Rate-Splitting Multiple Access (RSMA) has emerged as a robust transmission strategy for multi-antenna wireless systems. This paper investigates the performance of RSMA in a downlink Decode-and-Forward (DF) relay network under imperfect Channel State Information (CSI) at both the transmitter and the relay. The system operates in two phases: in the first phase, the Base Station (BS) transmits signals to both BS Users (BUs) and the relay;in the second phase, the relay decodes and forwards the signals to Relay Users (RUs) located outside the BS coverage area. RSMA is employed for facilitating transmission from both the BS and the relay. To optimize the network performance, we derive a tractable lower bound for the ergodic sum-rate, which enables the power allocation coefficients of common and private streams in the RSMA structures to maximize the overall sum-rate in both phases. The simulation results demonstrate that the proposed power allocation algorithm, coupled with a low-complexity precoding design, significantly improves the sum-rate performance of DF relay RSMA networks compared to scenarios where RSMA is not utilized. Notably, RSMA outperforms Spatial Division Multiple Access (SDMA)-based benchmarks, achieving sum-rate gains of up to 81%. Furthermore, a three-user use-case scenario is examined, revealing that RSMA consistently surpasses Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA)-based benchmarks, even in the presence of imperfect channel state information (CSI) at both the transmitter and the relay.
This paper considers a cooperative wireless system with multiple antennas at the source. A multi-antenna relay equipped with a power splitter is introduced between the source and destination. It utilizes the energy fr...
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This paper considers a cooperative wireless system with multiple antennas at the source. A multi-antenna relay equipped with a power splitter is introduced between the source and destination. It utilizes the energy from the received signal for amplification and transmit it to the destination. The linear precoding techniques such as zero forcing (ZF) and minimum mean square error (MMSE) are used to reduce the interference. We compare two antenna selection techniques viz. norm-based antenna selection (NBAS) and received signal-to-noise ratio (SNR) based antenna selection (RSBAS). It is analyzed that the NBAS and RSBAS antenna selection techniques offer improvement of 20% and 37% over random antenna selection technique. However, the antenna selection technique NBAS offers improvement in system bit error rate (BER) performance by 14% as compared to RSBAS at SNR value of 0 dB. We presented a scenario where the energy harvesting at relay node offers saving the battery power at the user node. Hence, the presented analysis is applicable in communication with remote sensors. This paper also analyses that the BER is improved with the reduction in the distance between source and relay for the given value of energy harvesting efficiency.
This article addresses two key challenges in the multibeam multicast nonorthogonal multiple access (MB-MC-NOMA) scheme and respective beamforming design problems in satellite systems. Achieving the max-min fairness an...
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This article addresses two key challenges in the multibeam multicast nonorthogonal multiple access (MB-MC-NOMA) scheme and respective beamforming design problems in satellite systems. Achieving the max-min fairness and maximum sum-rate among multiple multicast groups of users are jointly considered in a theoretical information framework. It is assumed that each frame contains information of multiple users in multicast transmission. Therefore, contrary to the unicast linear precoding, we have developed the multicast linear precoding with mapping function considering tradeoffs to deal with the lack of the spatial degree of freedom. In our proposed scheme, each beamforming vector conveys information to more than one group of users in an NOMA framework, relying on the superposition techniques at the transmitters and successive interference cancellation (SIC) at the receivers. We have derived the capacity rates achievable in each beam, proposing the methods to maximize the minimum rate and weighted sum-rate. Considering the dependency of the broadcasting power and the respective achievable rates, the equivalent channel and water-filling algorithm for the MB-MC-NOMA is developed;as such, the optimal transmit power density for the groups of users within multiple beams are efficiently computed. The extensive simulation results confirm the proposed theoretical findings, providing a considerable boost in both minimum-rate and sum-rate with respect to state-of-the-art MB-MC satellite systems.
The recent introduction of vector coded caching has revealed that multi-rank transmissions in the presence of receiver-side cache content can dramatically ameliorate the file-size bottleneck of coded caching and subst...
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The recent introduction of vector coded caching has revealed that multi-rank transmissions in the presence of receiver-side cache content can dramatically ameliorate the file-size bottleneck of coded caching and substantially boost performance in error-free wire-like channels. In this work, we employ large-matrix analysis to explore the effect of vector coded caching in realistic wireless multi-antenna downlink systems. For a given downlink MISO system already optimized to exploit both multiplexing and beamforming gains, and for a fixed set of antenna and SNR resources, our analysis answers a simple question: What is the multiplicative throughput boost obtained from introducing reasonably-sized receiver-side caches that can pre-store information content? The derived closed-form expressions capture various linear precoders, and a variety of practical considerations such as power dissemination across signals, realistic SNR values, as well as feedback costs. The schemes are very simple (we simply collapse precoding vectors into a single vector), and the recorded gains are notable. For example, for 32 transmit antennas, a received SNR of 20 dB, a coherence bandwidth of 300 kHz, a coherence period of 40 ms, and under realistic file-size and cache-size constraints, vector coded caching is here shown to offer a multiplicative throughput boost of about 310% with ZF/RZF precoding and a 430% boost in the performance of already optimized MF-based (cacheless) systems. Interestingly, vector coded caching also accelerates channel hardening to the benefit of feedback acquisition, often surpassing 540% gains over traditional hardening-constrained cacheless downlink systems.
Nowadays, Multi-user Multiple-In Multiple-Out (MU-MIMO) systems are used in new generation wireless technologies. Due to ongoing improvement in wireless technology, the numbers of users and applications increase rapid...
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ISBN:
(纸本)9781479942336
Nowadays, Multi-user Multiple-In Multiple-Out (MU-MIMO) systems are used in new generation wireless technologies. Due to ongoing improvement in wireless technology, the numbers of users and applications increase rapidly. At the same time, wireless communication need the high data rate and link reliability. Therefore, MU-MIMO improvements have to consider 1) providing the high data rate and link reliability, 2) support all users in the same and frequency resource, and 3) using low power consumption. In practice, inter-user interference has a strong impact when more users access the wireless link. Complicated transmission techniques such as interference cancellation are used to maintain a given desired quality of service. Due to these problems, MU-MIMO systems with very large antenna arrays (known as massive MIMO) are proposed. With massive MU-MIMO systems, we mean a hundred or more serving tens of users. The channel vectors are nearly orthogonal, and the inter-user interference is reduced significantly. Therefore, the users can be served with high data rate simultaneously. In this paper, we investigate the performance of the massive MU-MIMO downlink system in a single cell where the base station utilizes linear precoding schemes to serve many users over the Rayleigh fading channel.
We investigate the weighted sum-rate (WSR) maximization linear precoder design under total power constraint (TPC) for massive MIMO downlink with matrix manifold optimization. Particularly, we prove that the precoders ...
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ISBN:
(纸本)9798350303582;9798350303599
We investigate the weighted sum-rate (WSR) maximization linear precoder design under total power constraint (TPC) for massive MIMO downlink with matrix manifold optimization. Particularly, we prove that the precoders under TPC are on a Riemannian submanifold, and transform the constrained problem in Euclidean space to the unconstrained one on manifold. In accordance with this, Riemannian design methods using Riemannian steepest descent and Riemannian conjugate gradient are provided to design the WSR-maximization precoders under TPC. Riemannian methods are free of the inverse of large dimensional matrix, posing significant computational savings and potentially allowing to avoid ill numerical behavior in algorithms. Complexity analysis and performance simulations demonstrate the advantages of the proposed precoder design.
Massive MIMO (multiple-input multiple-output) has been recognized as an efficient solution to improve the spectral efficiency of future communication systems. However, increasing the number of antennas and users goes ...
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ISBN:
(纸本)9780992862619
Massive MIMO (multiple-input multiple-output) has been recognized as an efficient solution to improve the spectral efficiency of future communication systems. However, increasing the number of antennas and users goes hand-in-hand with increasing computational complexity. In particular, the precoding design becomes involved since near-optimal precoding, such as regularized-zero forcing (RZF), requires the inversion of a large matrix. In our previous work [1] we proposed to solve this issue in the single-cell case by approximating the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are selected for optimal system performance. In this paper, we generalize this technique to multi-cell scenarios. While the optimization of the RZF precoding has, thus far, not been feasible in multi-cell systems, we show that the proposed TPE precoding can be optimized to maximize the weighted max-min fairness. Using simulations, we compare the proposed TPE precoding with RZF and show that our scheme can achieve higher throughput using a TPE order of only 3.
We study downlink channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in time-division duplex. The users must know their effective channel gains to decode their received d...
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We study downlink channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in time-division duplex. The users must know their effective channel gains to decode their received downlink data. Previous works have used the mean value as the estimate, motivated by channel hardening. However, this is associated with a performance loss in non-isotropic scattering environments. We propose two novel estimation methods that can be applied without downlink pilots. The first method is model-based and asymptotic arguments are utilized to identify a connection between the effective channel gain and the average received power during a coherence interval. The second method is data-driven and trains a neural network to identify a mapping between the available information and the effective channel gain. Both methods can be utilized for any channel distribution and precoding. For the model-aided method, we derive all expressions in closed form for the case when maximum ratio or zero-forcing precoding is used. We compare the proposed methods with the state-of-the-art using the normalized mean-squared error and spectral efficiency (SE). The results suggest that the two proposed methods provide better SE than the state-of-the-art when there is a low level of channel hardening, while the performance difference is relatively small with the uncorrelated channel model.
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