In this paper, we propose practical yet effective statistically-aided codebook-based hybrid precoding schemes for massive multiple-input multiple-output systems in millimeter wave bands. Particularly, we develop novel...
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In this paper, we propose practical yet effective statistically-aided codebook-based hybrid precoding schemes for massive multiple-input multiple-output systems in millimeter wave bands. Particularly, we develop novel low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency analog precoders from statistically skewed DFT-basedcodebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to the interference noise ratio for the single-user and multi-user cases, respectively. We investigate the performance of the proposed algorithms by considering the mutual information of the analog beamforming procedure (the common stage among the proposed algorithms) as a performance evaluation metric. We derive lower and upper bounds on the mutual information of the channel given the proposed algorithms. Moreover, we show that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the millimeter wave channel or equivalently the transmit channel covariance matrix when only statistical channel knowledge is available at the transmitter. Then, we show that the proposed algorithms are asymptotically optimal as the number of transmit antennas M goes to infinity and the millimeter wave channel has a limited number of paths P , i.e., P < M. Further, we verify the performance of the proposed algorithms numerically where results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results show that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art.
As the demand for high-capacity and energy-efficient wireless communications grows, the exploration of cost-effective and power-optimized hybrid digital-analog beamforming techniques for millimeter-wave massive MIMO s...
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As the demand for high-capacity and energy-efficient wireless communications grows, the exploration of cost-effective and power-optimized hybrid digital-analog beamforming techniques for millimeter-wave massive MIMO systems has gained substantial traction. Adopting low-resolution phase shifters offers a more practical solution in terms of reducing power consumption and implementation costs. We investigate hybrid beamforming strategies for mmWave massive MIMO systems that employ low-resolution phase shifters, with a specific focus on designing and optimizing the analog beamforming component. To exploit the full potential of analog phase shifter network (APSN) in mmWave massive MIMO system while maintaining low-power consumption, we propose a two-layer APSN with low-resolution PSs to replace the conventional single-layer APSN with high-resolution PSs. Under the constraints imposed by the two-layer APSN hardware architecture, we propose a two-level codebook structure and an adaptive-weighted cross-entropy optimization algorithm that offers superior performance while substantially reducing the computational burden compared to the conventional cross-entropy optimization algorithm. We further develop two alternative schemes through the maximal gain harvest method to reduce complexity. We also investigate the case that only one steering vector is shared in the inner beamformer and present two low-complexity beamforming algorithms. Through simulations, the proposed two-layer APSN structure and the proposed algorithms have been shown to outperform existing methods, with notable effectiveness in scenarios where low-resolution phase shifters are utilized.
Enhancing the throughput of multi-user (MU) massive multiple-input multiple-output (MIMO) networks is one of the biggest promises that the fifth generation (5G) networks are expected to deliver. In the Third Generatio...
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ISBN:
(纸本)9781728137834
Enhancing the throughput of multi-user (MU) massive multiple-input multiple-output (MIMO) networks is one of the biggest promises that the fifth generation (5G) networks are expected to deliver. In the Third Generation Partnership Project (3GPP) New Radio (NR) standardization efforts, downlink precoding designs that balance performance and uplink feedback overhead are being investigated. Most recently, a high-resolution precoder (Type-II codebook) was specified for downlink NR Release (Rel.) 15 wherein the channel state information (CSI) feedback is compressed in the spatial domain via exploiting a Discrete Fourier Transform (DFT)-basedcodebook structure. An extension of the Type-II codebook for NR Rei. 16 which also exploits frequency correlation to reduce CSI feedback overhead is currently under study. In this paper, an overview of some of the recent developments for Rel. 16 Type-II codebook is provided. In addition, a practical approach is proposed that uses multi-stage quantization of codebook parameters with variable quantization resolution, where the resolution is proportional to the coefficients' amplitude values. This approach helps provide better utilization of the CSI feedback, compared with the case with the same quantization resolution for all coefficients. System-level simulation results are provided which show that the proposed approach significantly reduces the CSI feedback overhead without notable impact on performance.
Recently, a variety of reduced complexity soft-in soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-in soft-ou...
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Recently, a variety of reduced complexity soft-in soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-in soft-output detectors for MIMO systems due to heavy burden in computational complexity. In this paper, we propose a soft detection algorithm for MIMO systems which performs close to the full dimensional joint detection, yet offers significant complexity reduction over the existing detectors. The proposed algorithm, referred to as soft-input soft-output successive group (SSG) detector, detects a subset of symbols (called a symbol group) successively using a deliberately designed preprocessing to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of the interfering symbol groups successively using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results on realistic MIMO systems demonstrate that the proposed SSG detector achieves considerable complexity reduction over the conventional approaches with negligible performance loss.
In this paper, we investigate the impact of various remote unit collaboration schemes on the transmission performance to passengers, traveling in high speed trains. We assume that the user equipments directly communic...
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ISBN:
(纸本)9781479980918
In this paper, we investigate the impact of various remote unit collaboration schemes on the transmission performance to passengers, traveling in high speed trains. We assume that the user equipments directly communicate with the remote units, i.e., the trains are not equipped with relay nodes. We introduce a theoretical model based on Maximum Ratio Transmission and Gamma distributed fading, and analyze a representative segment of a railroad track. Results from both, theory and simulations by the Vienna LTE-A System Level Simulator are provided in terms of train average spectral efficiency and allow us to discuss the suitability of the moving cell concept for trains without relay support. Our theoretical results exhibit an offset from the simulations. Investigating its origin reveals the limitations of codebook-based precoding and allows us to adapt our theoretical model for accurate performance predictions.
This paper considers codebook-based downlink beamforming proposing a linear adaptive channel predistortion method to close the performance gap between codebook and non-codebookbased beamforming defined in wireless st...
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ISBN:
(纸本)9781467355773
This paper considers codebook-based downlink beamforming proposing a linear adaptive channel predistortion method to close the performance gap between codebook and non-codebookbased beamforming defined in wireless standards, e. g., LTE-A. The proposed channel predistortion method does not involve any additional signalling overhead. In our novel beamforming concept, we optimize the codebook-based precoding vector assignment, the power allocation, and the channel predistortion matrix jointly to minimize the transmitted power of the base station (BS) while guaranteeing the quality-of-service (QoS) of the mobile stations (MSs) and satisfying the smoothness conditions on the channel predistortion matrix. The joint channel predistortion and codebook-based beamforming (PCB) problem represents a non-convex mixed integer program (MIP). An alternating optimization algorithm (ATOA) and an alternating feasibility search algorithm (AFSA) are developed to approximately solve the PCB problem. Simulation results show that the proposed codebook-based beamforming achieves significant reductions of the transmitted power of the BS and remarkable increases of the percentage of feasible cases of all Monte Carlo runs, as compared to the standard codebook-based beamforming. Numerical results also show that the proposed design performs very close to the non-codebookbased beamforming in the given settings and that the proposed ATOA and AFSA are close-to-optimal.
This paper considers robust codebook-based downlink beatnfortning (i.e., single-layer precoding), where the beamformer of each user is chosen from a fixed beamformer codebook defined, e.g., in LTE and LTE-A. Admission...
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ISBN:
(纸本)9781479903566
This paper considers robust codebook-based downlink beatnfortning (i.e., single-layer precoding), where the beamformer of each user is chosen from a fixed beamformer codebook defined, e.g., in LTE and LTE-A. Admission control and power allocation are embedded in the precoding vector selection procedure. The objective is to maximize the system utility, defined as the revenue gained from admitting users minus the cost for the transmitted power of the base station. We adopt the quality-of-service constrained approach and the robustness against channel covariance estimation errors is realized with worst-case design. The robust codebook-based beamforming problem, which is a hi-level mixed integer program, is converted into a more tractable mixed integer second-order cone program. Techniques are proposed to customize the convex continuous relaxation based branch-and-cut algorithm to compute the optimal solutions. A low-complexity inflation procedure is also developed to compute the near-optimal solutions for practical applications. Numerical examples show that the gap between the average number of admitted users achieved by the fast inflation procedure and that of the optimal solutions is less than 11.6% for all considered simulation settings. Further, the inflation procedure yields optimal solutions in 88% of the Monte Carlo runs under specific parameter settings.
This paper considers robust codebook-based downlink beamforming (i.e., single-layer precoding), where the beamformer of each user is chosen from a fixed beamformer codebook defined, e.g., in LTE and LTE-A. Admission c...
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ISBN:
(纸本)9781479903573
This paper considers robust codebook-based downlink beamforming (i.e., single-layer precoding), where the beamformer of each user is chosen from a fixed beamformer codebook defined, e.g., in LTE and LTE-A. Admission control and power allocation are embedded in the precoding vector selection procedure. The objective is to maximize the system utility, defined as the revenue gained from admitting users minus the cost for the transmitted power of the base station. We adopt the quality-of-service constrained approach and the robustness against channel covariance estimation errors is realized with worst-case design. The robust codebook-based beamforming problem, which is a bi-level mixed integer program, is converted into a more tractable mixed integer second-order cone program. Techniques are proposed to customize the convex continuous relaxation based branch-and-cut algorithm to compute the optimal solutions. A low-complexity inflation procedure is also developed to compute the near-optimal solutions for practical applications. Numerical examples show that the gap between the average number of admitted users achieved by the fast inflation procedure and that of the optimal solutions is less than 11.6% for all considered simulation settings. Further, the inflation procedure yields optimal solutions in 88% of the Monte Carlo runs under specific parameter settings.
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