Carrier frequency offset (CFO) introduces spectrum misalignment of the transmit and receive filters, causing energy loss and distortion of received signal. A recent study presents an accurate signal model that incorporates these characteristics. However, CFO estimators in the existing literature were developed based on the signal models without these characteristics. This paper generalizes the accurate signal model with CFO to include timing offset and sampling offset and then studies what effects the accurate signal model bring to the CFO estimation and how to address them. We investigate different existing CFO estimators and find some of them lose their optimality under the accurate signal model. We also develop two new integer normalized CFO (ICFO) estimators under the accurate signal model using only one OFDM symbol; one of them is a pilot-aided estimator using both pilot and data and the other one is a blind estimator using data only. Compared with the existing approaches, both of the new estimators make better use of channel and energy loss information, thus yielding more accurate estimation. Furthermore, the proposed methods overcome the existing approaches' limitation to phase shift keying modulation. Simulation results corroborate the advantages of the new estimators. Analytical performance results are also provided for the proposed estimators.
Abstract-Vehicle to vehicle (V2V) communication has gained renewed interest among research community which is further signified by the allocation of dedicated spectrum and an IEEE standard for their usage in recent years. However, 802.11p, the current IEEE standard for vehicular communications, has a relatively low multiplicity (the number of links that it can simultaneously support). In this work, we propose a new vehicular communication scheme based on sectored antennas in order to improve the multiplicity performance. We first show that a sectored antenna based vehicular communication system has higher multiplicity than 802.11p. To further enable multiple transmit sectors to communicate with a receive node simultaneously in a single receive sector, using an OFDMA system design, we propose a new sector-specific pilot design and corresponding channel estimation over time and frequency domains of the channels corresponding to various sectors. Finally, the performance characteristics of the proposed scheme are shown through simulations.
Fractional frequency reuse (FFR) and exploitation of the channel state information at the transmitter (CSIT) are effective approaches to improve the spectrum efficiency of the outer coverage region. When channels vary within a physical transmission frame, the above improvement is substantially suppressed. To remedy this, this paper develops new FFR patterns for multi-cell OFDMA systems with frequency or time division duplexing (FDD/TDD) in time-varying channels. Simulation results show notable performance gains of the proposed schemes over the existing ones.
The combined Block Diagonalization and Geometric Mean Decomposition (BD-GMD) is a precoding scheme that is asymptotically optimal for downlink Multi-User Multiple-Input-Multiple-Output(MU-MIMO)systems. BD-GMD eliminate the interference between different user channels in MU-MIMO system under ideal condition, which leads to an improvement of the quality of users’ channel and brings a promotion of system throughput. In a downlink MU-MIMO system, there are a large number of users, the base station need to select a subset of users to serve for the reason that the number of simultaneously supportable users with BD-GMD is limited by the number of transmitting and receiving antennas. In the past, we paid our attention mostly on how to maximize the total throughput. However, the fairness is ignored may lead to “starvation”, which means that some subscribers are not able to get service in unacceptable long time. We introduce proportional fair scheduling schemes (PFS) into the system to guarantee the fairness, and also propose new schemes. The proposed algorithms update the adaptive parameters indicating past channel quality in each time slot. According to current and past channel information, we can make better multi-user scheduling and improve the system performance while maintaining the users’ fairness.
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