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Register a new userEnergy Efficiency Scheme with Cellular Partition Zooming for Massive MIMO Systems
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Added by
Di Zhang Mr.
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Massive multiple-input multiple-output (Massive MIMO) has been realized as a promising technology for next generation wireless mobile communications, in which Spectral efficiency (SE) and energy efficiency (EE) are two critical issues. Prior estimates have indicated that 57% energy of the cellular system need to be supplied by the operator, especially to feed the base station (BS). While varies scheduling studies concerned on the user equipment (UE) to reduce the total energy consumption instead of BS. Fewer literatures address EE issues from a BS perspective. In this paper, an EE scheme is proposed by reducing the energy consumption of BS. The transmission model and parameters related to EE is formulated first. Afterwards, an cellular partition zooming (CPZ) scheme is proposed where the BS can zoom in to maintain the coverage area. Specifically, if no user exists in the rare area of the coverage, BS will zoom out to sleep mode to save energy. Comprehensive simulation results demonstrate that CPZ has better EE performance with negligible impact on transmission rate.
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In this paper, we investigate the coexistence of two technologies that have been put forward for the fifth generation (5G) of cellular networks, namely, network-assisted device-to-device (D2D) communications and massive MIMO (multiple-input multiple-output). Potential benefits of both technologies are known individually, but the tradeoffs resulting from their coexistence have not been adequately addressed. To this end, we assume that D2D users reuse the downlink resources of cellular networks in an underlay fashion. In addition, multiple antennas at the BS are used in order to obtain precoding gains and simultaneously support multiple cellular users using multiuser or massive MIMO technique. Two metrics are considered, namely the average sum rate (ASR) and energy efficiency (EE). We derive tractable and directly computable expressions and study the tradeoffs between the ASR and EE as functions of the number of BS antennas, the number of cellular users and the density of D2D users within a given coverage area. Our results show that both the ASR and EE behave differently in scenarios with low and high density of D2D users, and that coexistence of underlay D2D communications and massive MIMO is mainly beneficial in low densities of D2D users.
This letter shows that optimizing the transmit powers along with optimally designed nonorthogonal pilots can significantly reduce pilot contamination and improve the overall throughput of the uplink multi-cell massive multiple-input multiple-output (MIMO) system as compared to the conventional schemes that use orthogonal pilots. Given the optimized nonorthogonal pilots, power control as a function of the large-scale path-loss can be thought of as a stochastic optimization problem due to the presence of fast fading. This paper advocates a deterministic approach to solve this problem, then further proposes a stochastic optimization method that utilizes successive convex approximation as a benchmark to quantify the performance of the proposed approach. Simulation results reveal significant advantage of using optimized nonorthogonal pilots together with power control to combat pilot contamination.
Hybrid analog and digital BeamForming (HBF) is one of the enabling transceiver technologies for millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) systems. This technology offers highly directional communication, which is able to confront the intrinsic characteristics of mmWave signal propagation. However, the small coherence time in mmWave systems, especially under mobility conditions, renders efficient Beam Management (BM) in standalone mmWave communication a very difficult task. In this paper, we consider HBF transceivers with planar antenna panels and design a multi-level beam codebook for the analog beamformer comprising flat top beams with variable widths. These beams exhibit an almost constant array gain for the whole desired angle width, thereby facilitating efficient hierarchical BM. Focusing on the uplink communication, we present a novel beam training algorithm with dynamic beam ordering, which is suitable for the stringent latency requirements of the latest mmWave standard discussions. Our simulation results showcase the latency performance improvement and received signal-to-noise ratio with different variations of the proposed scheme over the optimum beam training scheme based on exhaustive narrow beam search.
In this paper, we consider the downlink of a massive multiple-input-multiple-output (MIMO) single user transmission system operating in the millimeter wave outdoor narrowband channel environment. We propose a novel receive spatial modulation architecture aimed to reduce the power consumption at the user terminal, while attaining a significant throughput. The energy consumption reduction is obtained through the use of analog devices (amplitude detector), which reduces the number of radio frequency chains and analog-to-digital-converters (ADCs). The base station transmits spatial and modulation symbols per channel use. We show that the optimal spatial symbol detector is a threshold detector that can be implemented by using one bit ADC. We derive closed form expressions for the detection threshold at different signal-to-noise-ratio (SNR) regions showing that a simple threshold can be obtained at high SNR and its performance approaches the exact threshold. We derive expressions for the average bit error probability in the presence and absence of the threshold estimation error showing that a small number of pilot symbols is needed. A performance comparison is done between the proposed system and fully digital MIMO showing that a suitable constellation selection can reduce the performance gap.
In this paper, we investigate the performance of cell-free massive MIMO systems with massive connectivity. With the generalized approximate message passing (GAMP) algorithm, we obtain the minimum mean-squared error (MMSE) estimate of the effective channel coefficients from all users to all access points (APs) in order to perform joint user activity detection and channel estimation. Subsequently, using the decoupling properties of MMSE estimation for large linear systems and state evolution equations of the GAMP algorithm, we obtain the variances of both the estimated channel coefficients and the corresponding channel estimation error. Finally, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO system. With numerical results, we analyze the impact of the number of pilots used for joint activity detection and channel estimation, the number of APs, and signal-to-noise ratio (SNR) on the achievable rates.
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