A subcorrelation detection algorithm in CDMA location systems is proposed in this paper.CDMA location systems are introduced first,then the coherent correlation and noncoherently combined correlation detection algorithms are analyzed.At last,the subcorrelation detection algorithm and experimental result are presented.It is shown that the method proposed in this paper can effectively decrease the hardware resources and computation complexity,improve the efficiency and real-time performance of correlation detection algorithm.
Cloud radio access network (CRAN) is a promising candidate for the next-generation wireless communication systems. In CRAN, remote radio heads (RRHs) are deployed to serve users in a target area, which are connected to a central processor (CP) via limited-capacity links termed the fronthaul. In practice, the large amount of information transferred between each RRH and the CP for centralized processing can easily exceed its fronthaul capacity. This motivates our study in this paper on a new hybrid decoding scheme here the RRHs can either locally decode-and-forward the user messages to save the fronthaul capacity, or quantize and forward their signals to the CP for joint decoding as in the conventional CRAN (i.e., forward-and-decode). We consider the uplink transmission in an orthogonal frequency division multiplexing (OFDM)-based CRAN with multiple RRHs to serve a single user, where the proposed hybrid decoding is performed on each OFDM sub-channel (SC). We consider a joint optimization of the processing mode selections (decode-and-forward or forward-and-decode) along with the user's power allocation over all SCs to maximize the sum-rate for the user subject to the RRHs' individual fronthaul capacity constraints and the user's total power constraint. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We also compare the performance of the proposed hybrid decoding with existing schemes that perform either decode-and-forward or forward-and-decode processing at all SCs, which shows promising throughput gains.
The rate maximization for the K-user interference channels (ICs) has been investigated extensively in the literature. However, the practical problem of minimizing the error probability with given signal modulations and/or data rates of the users is less studied. In this paper, by utilizing additional degrees of freedom from the improper signaling (versus the conventional proper signaling) , we seek to optimize the precoding matrices for the K-user single-input single-output (SISO) ICs to minimize pair-wise error probability (PEP) and symbol error rate (SER) with two proposed algorithms, respectively. Compared with conventional proper signaling and other state-of-the-art improper signaling designs, our proposed improper signaling schemes achieve notable error rate improvement in SISO-ICs under both the additive white Gaussian noise (AWGN) and cellular system setups with or without channel coding. Our study provides another viewpoint for optimizing transmissions in ICs and further justifies the practical benefit of improper signaling in interference-limited communication systems.
Improving energy efficiency is key to network providers maintaining profit levels and an acceptable carbon footprint in the face of rapidly increasing data traffic in cellular networks in the coming years. The energy-saving concept studied in this paper is the adaptation of a base station's (BS's) transmit power levels and coverage area according to channel conditions and traffic load. Cell coverage is usually pre-designed based on the estimated static (e.g. peak) traffic load. However, traffic load in cellular networks exhibits significant fluctuations in both space and time, which can be exploited, through cell range adaptation, for energy saving. In this paper, we design short- and long-term BS power control (STPC and LTPC respectively) policies for the OFDMA-based downlink of a single-cell system, where bandwidth is dynamically and equally shared among a random number of mobile users (MUs). STPC is a function of all MUs' channel gains that maintains the required user-level quality of service (QoS), while LTPC (including BS on-off control) is a function of traffic density that minimizes the long-term energy consumption at the BS under a minimum throughput constraint. We first develop a power scaling law that relates the (short-term) average transmit power at BS with the given cell range and MU density. Based on this result, we derive the optimal (long-term) transmit adaptation policy by considering a joint range adaptation and LTPC problem. By identifying the fact that energy saving at BS essentially comes from two major energy saving mechanisms (ESMs), i.e. range adaptation and BS on-off power control, we propose low-complexity suboptimal schemes with various combinations of the two ESMs to investigate their impacts on system energy consumption. It is shown that when the network throughput is low, BS on-off power control is the most effective ESM, while when the network throughput is higher, range adaptation becomes more effective.
Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they are constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of transmit waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain in practical WET systems. In this paper, we propose a new channel training scheme to achieve optimal EB gain in a distributed WET system, where multiple separated ETs adjust their transmit phases to collaboratively send power to a single ER in an iterative manner, based on one-bit feedback from the ER per training interval which indicates the increase/decrease of the received power level from one particular ET over two preassigned transmit phases. The proposed EB algorithm can be efficiently implemented in practical WET systems even with a large number of distributed ETs, and is analytically shown to converge quickly to the optimal EB design as the number of feedback intervals per ET increases. Numerical results are provided to evaluate the performance of the proposed algorithm as compared to other distributed EB designs.
In this article, we study the waveform scheduling problem for a wireless power transfer (WPT) system consisting of a power beacon (PB) and multiple energy-harvesting-empowered Internet of Things (EH-IoT) devices. In each time slot, each device requests power to the PB if it needs power, and the PB transmits a WPT signal for which the waveform is designed based on the harvested power satisfaction rate of the power-requesting devices. Under this setup, we formulate an optimization problem that maximizes the average number of EH-IoT devices whose power requests are satisfied. We first solve this problem, assuming that the perfect channel state information (CSI) of all devices is known at the PB. Since the problem is difficult to solve even with perfect CSI, we transform it into a more tractable problem via proper approximations and propose an efficient algorithm to solve it. Next, to tackle the issue that it is practically difficult for the PB to acquire the perfect CSI of each device, we propose a contextual learning-based WPT waveform scheduling algorithm, requiring only 1-bit feedback from each device at one time. Numerical results show that our proposed waveform scheduling algorithm provides a higher satisfaction rate than existing algorithms under perfect CSI, and that with limited CSI feedback achieves performance close to the case with perfect CSI.
The line-of-sight (LoS) dominant air-ground channels have posed critical interference issues in cellular-connected unmanned aerial vehicle (UAV) communications. In this paper, we propose a new base station (BS) cooperative beamforming (CB) technique for the cellular downlink to mitigate the strong interference caused by the co-channel terrestrial transmissions to the UAV. Besides the conventional CB by cooperatively transmitting the UAV's message, the serving BSs of the UAV exploit a novel CB-based interference transmission scheme to effectively suppress the terrestrial interference to the UAV. Specifically, the co-channel terrestrial users' messages are shared with the UAV's serving BSs and transmitted via CB so as to cancel their resultant interference at the UAV receiver. To optimally balance between the CB gains for UAV signal enhancement and terrestrial interference cancellation, we formulate a new problem to maximize the UAV's receive signal-to-interference- plus-noise ratio (SINR) by jointly optimizing the power allocations at all of its serving BSs for transmitting the UAV's and co-channel terrestrial users' messages. First, we derive the closed-form optimal solution to this problem in the special case of one serving BS for the UAV and draw useful insights. Then, we propose an algorithm to solve the problem optimally in the general case. Simulation results show that the proposed CB scheme with interference transmission and cancellation (ITC) significantly improves the UAV's downlink SINR as compared to the conventional CB without applying ITC.
Driven by the recent advancement in unmanned aerial vehicle (UAV) technology, this paper proposes a new wireless network architecture of \emph{coordinate multipoint (CoMP) in the sky} to harness both the benefits of interference mitigation via CoMP and high mobility of UAVs. Specifically, we consider uplink communications in a multi-UAV enabled multi-user system, where each UAV forwards its received signals from all ground users to a central processor (CP) for joint decoding. Moreover, we consider the case where the users may move on the ground, thus the UAVs need to adjust their locations in accordance with the user locations over time to maximize the network throughput. Utilizing random matrix theory, we first characterize in closed-form a set of approximated upper and lower bounds of the user's achievable rate in each time episode under a realistic line-of-sight (LoS) channel model with random phase, which are shown very tight both analytically and numerically. UAV placement and movement over different episodes are then optimized based on the derived bounds to maximize the minimum of user average achievable rates over all episodes for both cases of full information (of current and future episodes) and current information on the user's movement. Interestingly, it is shown that the optimized location of each UAV at any particular episode is the weighted average of the ground user locations at the current episode as well as its own location at the previous and/or next episode. Finally, simulation results are provided to validate and compare the performance of the proposed UAV placement and movement designs under different practical application scenarios.
Entering the low-carbon economy area,shipping enterprises must combine their own industry characteristics with low-carbon concept for direct the comprehensive operation to meet more opportunities and higher requirements.By recognizing the status quo of human resources management in shipping groups under the current conditions,the paper analysis the significance of putting the low-carbon ideas into the human resources management with the perspectives of job analysis,recruitment,training,performance evaluation,compensation management,labor relations,and make use of green human resource management and other new concepts and methods to guide human resource management innovation in shipping companies in low-carbon economic environment.
No abstract is provided for this article.
In this paper, we study a multi-antenna-based cognitive radio (CR) system that is able to operate concurrently with the primary radio (PR) system. We propose a novel CR transmission frame structure consisting of three stages, including a new environment learning stage in addition to the conventional channel training and data transmission stages. During the environment learning stage, the CR terminals blindly learn the spatial knowledge of the PR-CR channels, based on which cognitive beamforming is designed at CR transceivers to restrict the interference to and from the PR, respectively, in the subsequent channel training and data transmission stages. Considering the learning and training errors from the first two stages, we derive a lower bound on the ergodic capacity achievable for the CR link subject to a predefined interference-power constraint at the PR and the CR's own transmit power constraint. We then characterize a general learning/training/throughput (LTT) tradeoff associated with the proposed scheme, pertinent to transmit power allocation between training and transmission stages, as well as time allocation among learning, training, and transmission stages.
This paper studies capacity-approaching transmission schemes for the multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) channel, under the assumption that the channel state information (CSI) is completely known at the receiver but only partially available at the transmitter via a limited-rate feedback channel. A vertical Bell Labs layered space-time (V-BLAST)-based transmission structure is considered, where multiple data streams are independently encoded at the transmitter (i.e., horizontal encoding) and successively decoded at the receiver by the zero-forcing-based generalized decision feedback equalizer. A closed-loop V-BLAST extension is presented whereby transmit powers, rates, and antenna mappings for multiple data streams at different OFDM tones are jointly optimized at the receiver and then returned to the transmitter via the feedback channel. Two low-complexity algorithms for optimization of feedback parameters are proposed: one is based on the Lagrange dual-decomposition method and the other is a greedy algorithm. Antenna and tone grouping techniques by exploiting the MIMO-OFDM channel space-frequency correlations are also proposed to reduce the feedback complexity. Simulation results show that by only a moderate amount of feedback, the proposed closed-loop V-BLAST scheme improves substantially the throughput of the conventional open-loop V-BLAST scheme without feedback and, furthermore, approaches closely the MIMO-OFDM channel capacity achievable by the eigenmode transmission that requires the complete CSI at the transmitter.
