288 research outputs found

    Scattered Pilots and Virtual Carriers Based Frequency Offset Tracking for OFDM Systems: Algorithms, Identifiability, and Performance Analysis

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    In this paper, we propose a novel carrier frequency offset (CFO) tracking algorithm for orthogonal frequency division multiplexing (OFDM) systems by exploiting scattered pilot carriers and virtual carriers embedded in the existing OFDM standards. Assuming that the channel remains constant during two consecutive OFDM blocks and perfect timing, a CFO tracking algorithm is proposed using the limited number of pilot carriers in each OFDM block. Identifiability of this pilot based algorithm is fully discussed under the noise free environment, and a constellation rotation strategy is proposed to eliminate the c-ambiguity for arbitrary constellations. A weighted algorithm is then proposed by considering both scattered pilots and virtual carriers. We find that, the pilots increase the performance accuracy of the algorithm, while the virtual carriers reduce the chance of CFO outlier. Therefore, the proposed tracking algorithm is able to achieve full range CFO estimation, can be used before channel estimation, and could provide improved performance compared to existing algorithms. The asymptotic mean square error (MSE) of the proposed algorithm is derived and simulation results agree with the theoretical analysis

    Optimal Training Design for Channel Estimation in Decode-and-Forward Relay Networks With Individual and Total Power Constraints

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    In this paper, we study the channel estimation and the optimal training design for relay networks that operate under the decode-and-forward (DF) strategy with the knowledge of the interference covariance. In addition to the total power constraint on all the relays, we introduce individual power constraint for each relay, which reflects the practical scenario where all relays are separated from one another. Considering the individual power constraint for the relay networks is the major difference from that in the traditional point-to-point communication systems where only a total power constraint exists for all colocated antennas. Two types of channel estimation are involved: maximum likelihood (ML) and minimum mean square error (MMSE). For ML channel estimation, the channels are assumed as deterministic and the optimal training results from an efficient multilevel waterfilling type solution that is derived from the majorization theory. For MMSE channel estimation, however, the second-order statistics of the channels are assumed known and the general optimization problem turns out to be nonconvex. We instead consider three special yet reasonable scenarios. The problem in the first scenario is convex and could be efficiently solved by state-of-the-art optimization tools. Closed-form waterfilling type solutions are found in the remaining two scenarios, of which the first one has an interesting physical interpretation as pouring water into caves

    On channel estimation and optimal training design for amplify and forward relay networks

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    10.1109/GLOCOM.2007.763GLOBECOM - IEEE Global Telecommunications Conference4015-401

    Distributed space-time coding for two-way wireless relay networks

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    In this paper, we consider distributed space-time coding for two-way wireless relay networks, where communication between two terminals is assisted by relay nodes. Relaying protocols using two, three, and four time slots are proposed. The protocols using four time slots are the traditional amplify-and-forward (AF) and decode-and-forward (DF) protocols, which do not consider the property of the two-way traffic. A new class of relaying protocols, termed as partial decode-and-forward (PDF), is developed for the two time slots transmission, where each relay first removes part of the noise before sending the signal to the two terminals. Protocols using three time slots are proposed to compensate the fact that the two time slots protocols cannot make use of direct transmission between the two terminals. For all protocols, after processing their received signals, the relays encode the resulting signals using a distributed linear dispersion (LD) code. The proposed AF protocols are shown to achieve the diversity order of min{N,K}(1- (log log P/log P)), where N is the number of relays, P is the total power of the network, and K is the number of symbols transmitted during each time slot. When random unitary matrix is used for LD code, the proposed PDF protocols resemble random linear network coding, where the former operates on the unitary group and the latter works on the finite field. Moreover, PDF achieves the diversity order of min{N,K} but the conventional DF can only achieve the diversity order of 1. Finally, we find that two time slots protocols also have advantages over four-time-slot protocols in media access control (MAC) layer

    Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization

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    NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.Comment: Submitted for conference publicatio

    Maximum likelihood detection for differential unitary space-time modulation with carrier frequency offset

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    Can conventional differential unitary space time modulation (DUSTM) be applied when there is an unknown carrier frequency offset (CFO)? This paper answers this question affirmatively and derives the necessary maximum likelihood (ML) detection rule. The asymptotic performance of the proposed ML rule is analyzed, leading to a code design criterion for DUSTM by using the modified diversity product. The resulting proposed decision rule is a new differential modulation scheme in both the temporal and spatial domains. Two sub-optimal multiple-symbol decision rules with improved performance are also proposed. For the efficient implementation of these, we derive a modified bound intersection detector (BID), a generalization of the previously derived optimal BID for the conventional DUSTM. The simulation results show that the proposed differential modulation scheme is more robust against CFO drifting than the existing double temporal differential modulation

    To Harvest and Jam: A Paradigm of Self-Sustaining Friendly Jammers for Secure AF Relaying

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    This paper studies the use of multi-antenna harvest-and-jam (HJ) helpers in a multi-antenna amplify-and-forward (AF) relay wiretap channel assuming that the direct link between the source and destination is broken. Our objective is to maximize the secrecy rate at the destination subject to the transmit power constraints of the AF relay and the HJ helpers. In the case of perfect channel state information (CSI), the joint optimization of the artificial noise (AN) covariance matrix for cooperative jamming and the AF beamforming matrix is studied using semi-definite relaxation (SDR) which is tight, while suboptimal solutions are also devised with lower complexity. For the imperfect CSI case, we provide the equivalent reformulation of the worst-case robust optimization to maximize the minimum achievable secrecy rate. Inspired by the optimal solution to the case of perfect CSI, a suboptimal robust scheme is proposed striking a good tradeoff between complexity and performance. Finally, numerical results for various settings are provided to evaluate the proposed schemes.Comment: 16 pages (double column), 8 figures, submitted for possible journal publicatio
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