Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Proj...
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ISBN:
(纸本)9781538631805
Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10(-5) block error rate within 1 ms transmission period. Since the current wireless standard designed to maximize the coding gain by transmitting capacity achieving long code-block is not relevant for this purpose, entirely new transmission strategy is required. In this paper, we propose a new approach to transmit short packet information, called sparse vector coding (SVC). Key idea behind the proposed method is to transmit the control channel information after the sparse vector transformation. By mapping the transmit information into the position of nonzero elements and then transmitting it after the random spreading, we obtain underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations on realistic channel setting and decoder performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced physical downlink control channel (PDCCH) scheme.
In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop novel performance guarantees via an information-theoretic fram...
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ISBN:
(纸本)9781538647813
In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop novel performance guarantees via an information-theoretic framework for general noise models. For the noiseless and symmetric noise models, we find that the asymptotic number of tests required for vanishing error probability is within a factor log 2 approximate to 0.7 of the information-theoretic optimum at low sparsity levels, and that when a small fraction of incorrectly-decoded items is allowed, this guarantee extends to all sublinear sparsity levels. In many scaling regimes, these are the best known theoretical guarantees for any noisy group testing algorithm.
Massive multiple input multiple output (MIMO) systems are a promising technology for next generation wireless communications due to their ability to increase capacity and enhance both spectrum and energy efficiency. T...
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ISBN:
(纸本)9781538631805
Massive multiple input multiple output (MIMO) systems are a promising technology for next generation wireless communications due to their ability to increase capacity and enhance both spectrum and energy efficiency. To utilize the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential. Conventional approaches to obtain CSIT for frequency-division duplex (FDD) multi-user massive MIMO systems require downlink training and uplink CSI feedback. However, such training results in large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this paper, we investigate the channel estimation problem in FDD multi-user massive MIMO systems with spatially correlated channels and develop an efficient channel estimation algorithm that exploits the sparsity structure of the downlink channel matrix. The proposed algorithm selects the best features from the measurement matrix to obtain efficient CSI acquisition that can reduce the downlink training overhead compared with the conventional LS/MMSE channel estimators. We compare the performance of our proposed channel estimation method with traditional ones in terms of normalized mean square error (MSE). Simulation results verify that the proposed algorithm can significantly reduce the pilot overhead and has better performance compared with the traditional channel estimation methods.
The performances of the traditional anti-jamming DOA methods for the colocated MIMO radar system will be seriously deteriorated when the number of the snapshots is small. In this paper, a compressive sensing (CS)-base...
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ISBN:
(纸本)9781538647523
The performances of the traditional anti-jamming DOA methods for the colocated MIMO radar system will be seriously deteriorated when the number of the snapshots is small. In this paper, a compressive sensing (CS)-based anti-jamming DOA estimation with only a few snapshots is proposed. In the new DOA estimation, the information on the direction of strong jamming is used to construct a blocking matrix that can block the jamming component effectively. Hence, only the desired signal component is left after preprocessing with the blocking matrix. Then, the DOA estimation problem with a blocking matrix is formulated as a sparse recovery problem, and it is solved by the orthogonal matchingpursuit (OMP) algorithm. We compare the proposed method with the traditional CS-based method and the traditional anti-jamming DOA methods. Numerical results demonstrate that the proposed method outperforms the traditional methods.
Grant-free non-orthogonal multiple access (NOMA) has recently gained significant attention for reducing signaling overhead in machine-type communications (MTC). In this context, compressed sensing (CS) has been identi...
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ISBN:
(纸本)9781538631805
Grant-free non-orthogonal multiple access (NOMA) has recently gained significant attention for reducing signaling overhead in machine-type communications (MTC). In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame based multi-user uplink scenarios where users are (in)active for the duration of a frame, namely frame-wise joint sparsity model. Firstly, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and consider that the user sparsity level should be unknown for multiuser detection, two enhanced BCS-based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross validation aided block sparsity adaptive subspace pursuit (CVA-BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance, by reasonably setting the threshold based on the AWGN noise floor. And the proposed CVA-BSASP algorithm is a highly practical algorithm design that does not require any prior knowledge, by adopting the statistical and machine learning mechanism cross validation (CV) to determine the stopping condition of the algorithm. Superior performance of the proposed algorithms is demonstrated by numerical experiments.
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