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Tiejun Lv

Publications and source records attributed to Tiejun Lv.

66 records · Page 4Linked to original sources

Joint Multiple Symbol Differential Detection and Channel Decoding for Noncoherent UWB Impulse Radio by Belief Propagation

This paper proposes a belief propagation (BP) message passing algorithm based joint multiple symbol differential detection (MSDD) and channel decoding scheme for noncoherent differential ultra-wideband impulse radio (UWB-IR) systems. MSDD is an effective means to improve the performance of noncoherent differential UWB-IR systems. To optimize the overall detection and decoding performance, in this paper, we propose a novel soft-in soft-out (SISO) MSDD scheme and its integration with SISO channel decoding for noncoherent differential UWB-IR. we first propose a new auto-correlation receiver (AcR) architecture to sample the received UWB-IR signal. The proposed AcR can exploit the dependencies (imposed by the differential modulation) among data symbols throughout the whole packet. The signal probabilistic model has a hidden Markov chain structure. We use a factor graph to represent this hidden Markov chain. Then, we apply BP message passing algorithm on the factor graph to develop a SISO MSDD scheme, which has better performance than the previous MSDD scheme and is easy to be integrated with SISO channel decoding to form a joint MSDD and channel decoding scheme. Simulation results indicate the performance advantages of our MSDD scheme and joint MSDD and channel decoding scheme.

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Low-Complexity Downlink User Selection for Massive MIMO Systems

In this paper we propose a pair of low-complexity user selection schemes with zero-forcing precoding for multiuser massive MIMO downlink systems, in which the base station is equipped with a large-scale antenna array. First, we derive approximations of the ergodic sum rates of the systems invoking the conventional random user selection (RUS) and the location-dependant user selection (LUS). Then, the optimal number of simultaneously served user equipments (UEs), $K^*$, is investigated to maximize the sum rate approximations. Upon exploiting $K^*$, we develop two user selection schemes, namely $K^*$-RUS and $K^*$-LUS, where $K^*$ UEs are selected either randomly or based on their locations. Both of the proposed schemes are independent of the instantaneous channel state information of small-scale fading, therefore enjoying the same extremely-low computational complexity as that of the conventional RUS scheme. Moreover, both of our proposed schemes achieve significant sum rate improvement over the conventional RUS. In addition, it is worth noting that like the conventional RUS, the $K^*$-RUS achieves good fairness among UEs.

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Secrecy Transmit Beamforming for Heterogeneous Networks

In this paper, we pioneer the study of physical-layer security in heterogeneous networks (HetNets). We investigate secure communications in a two-tier downlink HetNet, which comprises one macrocell and several femtocells. Each cell has multiple users and an eavesdropper attempts to wiretap the intended macrocell user. Firstly, we consider an orthogonal spectrum allocation strategy to eliminate co-channel interference, and propose the secrecy transmit beamforming only operating in the macrocell (STB-OM) as a partial solution for secure communication in HetNet. Next, we consider a secrecy-oriented non-orthogonal spectrum allocation strategy and propose two cooperative STBs which rely on the collaboration amongst the macrocell base station (MBS) and the adjacent femtocell base stations (FBSs). Our first cooperative STB is the STB sequentially operating in the macrocell and femtocells (STB-SMF), where the cooperative FBSs individually design their STB matrices and then feed their performance metrics to the MBS for guiding the STB in the macrocell. Aiming to improve the performance of STB-SMF, we further propose the STB jointly designed in the macrocell and femtocells (STB-JMF), where all cooperative FBSs feed channel state information to the MBS for designing the joint STB. Unlike conventional STBs conceived for broadcasting or interference channels, the three proposed STB schemes all entail relatively sophisticated optimizations due to QoS constraints of the legitimate users. In order to efficiently use these STB schemes, the original optimization problems are reformulated and convex optimization techniques, such as second-order cone programming and semidefinite programming, are invoked to obtain the optimal solutions. Numerical results demonstrate that the proposed STB schemes are highly effective in improving the secrecy rate performance of HetNet.

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An ESPRIT-Based Approach for 2-D Localization of Incoherently Distributed Sources in Massive MIMO Systems

In this paper, an approach of estimating signal parameters via rotational invariance technique (ESPRIT) is proposed for two-dimensional (2-D) localization of incoherently distributed (ID) sources in large-scale/massive multiple-input multiple-output (MIMO) systems. The traditional ESPRIT-based methods are valid only for one-dimensional (1-D) localization of the ID sources. By contrast, in the proposed approach the signal subspace is constructed for estimating the nominal azimuth and elevation direction-of-arrivals and the angular spreads. The proposed estimator enjoys closed-form expressions and hence it bypasses the searching over the entire feasible field. Therefore, it imposes significantly lower computational complexity than the conventional 2-D estimation approaches. Our analysis shows that the estimation performance of the proposed approach improves when the large-scale/massive MIMO systems are employed. The approximate Cramér-Rao bound of the proposed estimator for the 2-D localization is also derived. Numerical results demonstrate that albeit the proposed estimation method is comparable with the traditional 2-D estimators in terms of performance, it benefits from a remarkably lower computational complexity.

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A Novel Scheme for Downlink Opportunistic Interference Alignment

In this paper we propose a downlink codebook-based opportunistic interference alignment (OIA) in a three-cell MIMO system. A codebook composed of multiple transmit vector sets is utilized to improve the multiuser selection diversity. The sum rate increases as the size of the codebook grows. In addition, during the user selection, effective channel gain and alignment metric are combined to generate a novel criterion, which improves the system performance, especially at low SNR. Furthermore, a threshold-based feedback approach is introduced to reduce the feedback load in the proposed scheme. Both the analytical results and simulations show that the proposed scheme provides significant improvement in terms of sum rates with no feedback load growth and slight increase of complexity.

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Low-Complexity Transmission Mode Selection in MU-MIMO Systems

We propose a low-complexity transmission strategy in multi-user multiple-input multiple-output downlink systems. The adaptive strategy adjusts the precoding methods, denoted as the transmission mode, to improve the system sum rates while maintaining the number of simultaneously served users. Three linear precoding transmission modes are discussed, i.e., the block diagonalization zero-forcing, the cooperative zero-forcing (CZF), and the cooperative matched-filter (CMF). Considering both the number of data streams and the multiple-antenna configuration of users, we modify the common CZF and CMF modes by allocating data streams. Then, the transmission mode is selected between the modified ones according to the asymptotic sum rate analyses. As instantaneous channel state information is not needed for the mode selection, the computational complexity is significantly reduced. Numerical simulations confirm our analyses and demonstrate that the proposed scheme achieves substantial performance gains with very low computational complexity.

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Limited Feedback-Based Interference Alignment for Interfering Multi-Access Channels

A limited feedback-based interference alignment (IA) scheme is proposed for the interfering multi-access channel (IMAC). By employing a novel performance-oriented quantization strategy, the proposed scheme is able to achieve the minimum overall residual inter-cell interference (ICI) with the optimized transceivers under limited feedback. Consequently, the scheme outperforms the existing counterparts in terms of system throughput. In addition, the proposed scheme can be implemented with flexible antenna configurations.

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Approximate Bayesian Probabilistic-Data-Association-Aided Iterative Detection for MIMO Systems Using Arbitrary M-ary Modulation

In this paper, the issue of designing an iterative-detection-and-decoding (IDD)-aided receiver, relying on the low-complexity probabilistic data association (PDA) method, is addressed for turbo-coded multiple-input-multiple-output (MIMO) systems using general M-ary modulations. We demonstrate that the classic candidate-search-aided bit-based extrinsic log-likelihood ratio (LLR) calculation method is not applicable to the family of PDA-based detectors. Additionally, we reveal that, in contrast to the interpretation in the existing literature, the output symbol probabilities of existing PDA algorithms are not the true a posteriori probabilities (APPs) but, rather, the normalized symbol likelihoods. Therefore, the classic relationship, where the extrinsic LLRs are given by subtracting the a priori LLRs from the a posteriori LLRs, does not hold for the existing PDA-based detectors. Motivated by these revelations, we conceive a new approximate Bayesian-theorem-based logarithmic-domain PDA (AB-Log-PDA) method and unveil the technique of calculating bit-based extrinsic LLRs for the AB-Log-PDA, which facilitates the employment of the AB-Log-PDA in a simplified IDD receiver structure. Additionally, we demonstrate that we may dispense with inner iterations within the AB-Log-PDA in the context of IDD receivers. Our complexity analysis and numerical results recorded for Nakagami-m fading channels demonstrate that the proposed AB-Log-PDA-based IDD scheme is capable of achieving a performance comparable with that of the optimal maximum a posteriori (MAP)-detector-based IDD receiver, while imposing significantly lower computational complexity in the scenarios considered.

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A low complexity approach of combining cooperative diversity and multiuser diversity in multiuser cooperative networks

In this paper, we investigate the scheduling scheme to combine cooperative diversity (CD) and multiuser diversity (MUD) in multiuser cooperative networks under the time resource allocation (TRA) framework in which the whole transmission is divided into two phases: the broadcast phase and the relay phase. The broadcast phase is for direct transmission whereas the relay phase is for relay transmission. Based on this TRA framework, a user selection based low complexity relay protocol (US-LCRP) is proposed to combine CD and MUD. In each time slot (TS) of the broadcast phase, a "best" user is selected for transmission in order to obtain MUD. In the relay phase, the relays forward the messages of some specific users in a fixed order and then invoke the limited feedback information to achieve CD. We demonstrate that the diversity-multiplexing tradeoff (DMT) of the US-LCRP is superior to that of the existing schemes, where more TSs are allocated for direct transmission in order to jointly exploit CD and MUD. Our analytical and numerical results show that the US-LCRP constitutes a more efficient resource utilization approach than the existing schemes. Additionally, the US-LCRP can be implemented with low complexity because only the direct links' channel state information (CSI) is estimated during the whole transmission.

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Large-scale MU-MIMO: It Is Necessary to Deploy Extra Antennas at Base Station

In this paper, the large-scale MU-MIMO system is considered where a base station (BS) with extremely large number of antennas (N) serves relatively less number of users (K). In order to achieve largest sum rate, it is proven that the amount of users must be limited such that the number of antennas at the BS is preponderant over that of the antennas at all the users. In other words, the antennas at the BS should be excess. The extra antennas at the BS are no longer just an optional approach to enhance the system performance but the prerequisite to the largest sum rate. Based on this factor, for a fixed N, the optimal K that maximizes the sum rate is further obtained. Additionally, it is also pointed out that the sum rate can be substantially improved by only adding a few antennas at the BS when the system is N=KM with M denoting the antennas at each user. The derivations are under the assumption of N and M going to infinity, and being implemented on different precoders. Numerical simulations verify the tightness and accuracy of our asymptotic results even for small N and M.

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BER Analysis of Decision-Feedback Multiple Symbol Detection in Noncoherent MIMO Ultra-Wideband Systems

In this paper, we investigate noncoherent multiple-input multiple-output (MIMO) ultra-wideband (UWB) systems where the signal is encoded by differential space-time block code (DSTBC). DSTBC enables noncoherent MIMO UWB systems to achieve diversity gain. However, the traditional noncoherent symbol-by-symbol differential detection (DD) for DSTBC-UWB suffers from performance degradation compared with the coherent detection. We introduce a noncoherent multiple symbol detection (MSD) scheme to enhance the performance of DSTBC-UWB system. Although the MSD scheme can boost the performance more as the observation window size gets to larger, the complexity of the exhaustive search for MSD also exponentially increases in terms of the window size. To decrease the computational complexity, the concept of decision-feedback (DF) is introduced to MSD for DSTBC-UWB in this paper. The resultant DF-MSD yields reasonable complexity and also solid performance improvement. We provide the bit error rate (BER) analysis for the proposed DF-MSD. Both theoretical analysis and simulation results validate the proposed scheme.

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Relaxed Belief Propagation for MIMO Detection

In this paper, relaxed belief propagation (RBP) based detectors are proposed for multiple-input multiple-out (MIMO) system. The factor graph is leveraged to represent the MIMO channels, and based on which our algorithms are developed. Unlike the existing complicated standard belief propagation (SBP) detector that considers all the edges of the factor graph when updating messages, the proposed RBP focuses on partial edges, which largely reduces computational complexity. In particular, relax degree is introduced in to determine how many edges to be selected, whereby RBP is a generalized edge selection based BP method and SBP is a special case of RBP having the smallest relax degree. Moreover, we propose a novel Gaussian approximation with feedback information mechanism to enable the proposed RBP detector. In order to further improve the detection performance, we also propose to cascade a minimum mean square error (MMSE) detector before the RBP detector, from which pseudo priori information is judiciously exploited. Convergence and complexity analyses, along with the numerical simulation results, verify that the proposed RBP outperform other BP methods having the similar complexity, and the MMSE cascaded RBP even outperform SBP at the largest relax degree in large MIMO systems.

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