Selected mapping (SLM) schemes are commonly employed to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. It has been shown that the computational complexity of the traditional SLM scheme can be substantially reduced by adopting conversion vectors obtained by using the inverse fast Fourier transform (IFFT) of the phase rotation vector in place of the conventional IFFT operations [21]. Unfortunately, however, the elements of these phase rotation vectors of the conversion vectors in [21] do not generally have an equal magnitude, and thus a significant degradation in the bit error rate (BER) performance is incurred. This problem can be remedied by utilizing conversion vectors having the form of a perfect sequence. This paper presents three novel classes of perfect sequence, each of which comprises certain base vectors and their cyclic-shifted versions. Three novel low-complexity SLM schemes are then proposed based upon the unique structures of these perfect sequences. It is shown that while the PAPR performances of the proposed schemes are marginally poorer than that of the traditional SLM scheme, the three schemes achieve an identical BER performance and have a substantially lower computational complexity.

One of the major drawbacks of multi-carrier systems is the high peak-to-average power ratio (PAPR) of the transmitted signals. In this paper, the proposed novel low-complexity selective mapping (SLM) schemes are applicable to interleaved-4 orthogonal frequency division multiple access (OFDMA) uplink systems for PAPR reduction. The novel scheme just needs one inverse fast Fourier transform (IFFT) block because that the phases of the transmitted signals in frequency domain are rotated by circular convolution with conversion vectors in time domain. Moreover, a special set of conversion vectors are proposed in novel scheme, which are not only computed with low complexity but also reduce the PAPR effectively. In proposed scheme, different conversion vectors and appropriate subcarriers mapping are picked up for different users. The scheme supplies a practicable low-complexity method for PAPR reduction in interleaved-4 OFDMA uplink systems. Besides, the bit error rate (BER) performance is as good as the SLM scheme.

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technology has been used widely in many wireless communication systems. Signals will be distorted when they are transmitted in wireless channels. For the reason that wireless channel is time or location variant, we have to estimate the channel impulse response and use the channel state information to compensate the channel distortion. In order to estimate the state of the channel, let the known training symbols put in front of the data symbols and use training symbols to estimate channel response. A typical channel estimate for MIMO OFDM systems is treated as spatially uncorrelated. However in many realistic scenarios, the channel tends to be spatially correlated. Indeed, we have no prior knowledge of the channel spatial correlation. So consider the spatial correlation, the channel can estimate accurately. And it is important that how to combine spatial correlation and channel estimation to reduce the estimation error. In the paper we propose a iterative channel spatial correlation and channel estimation algorithm. At first, channel spatial correlation estimation is obtained by synchronize symbols. The receiver uses the estimated channel to help the detection/decision of data symbol. And then the channel estimation treats the detected signals as known data to perform a next stage channel estimation iteratively. By utilizing the iterative channel estimation and signal detection process we can reduce the estimation error caused by channel spatial correlation estimation. The accuracy of the channel estimation can be improved by increasing the number of iteration process. Simulation results demonstrate the iterative spatial correlation and channel estimation algorithm can provide better mean-square-error performance.

Enabling multi-hop wireless mesh networks with multi-input multi-output (MIMO) functionality boosts network throughput by transmitting over multiple orthogonal spatial channels (spatial multiplexing) and by performing interference cancellation, to allow links within interference range to be concurrently active. Furthermore, if the channel is in a deep fade, then multiple antenna elements at the transmitter and/or receiver can be used to transmit a single stream, thereby improving signal quality (diversity gain). However, there is a fundamental trade-off between boosting individual link performance and reducing interference, which must be modeled in the process of optimizing network throughput. This is called the diversity-multiplexing-interference suppression trade-off. Optimizing network throughput therefore, requires optimizing the trade-off between the amounts of diversity employed on each link, the number of streams multiplexed on each link and the number of interfering links allowed to be simultaneously active in the network. We present a set of efficient heuristics for one-shot link scheduling and stream allocation that approximately solve the problem of optimizing network throughput in a single time slot. We identify the fundamental problem of verifying the feasibility of a given stream allocation. The problems of general link scheduling and stream allocation are very closely related to the problem of verifying feasibility. We present a set of efficient heuristic feasibility tests which can be easily incorporated into practical scheduling schemes. We show for some special MIMO network scenarios that feasibility is of polynomial complexity. However, we conjecture that in general, this problem, which is a variation of Boolean Satisablility, is NP-Complete.

Recently, there has been an increasing demand for OFDM system operating in high mobility environment. In such situation, wireless channel is both frequency-selective and time-varying, a.k.a. doubly-selective, making it hard for the receiver to keep track of the channel state information (CSI). Moreover, the statistical information of channel, e.g., tap positions, channel length, Doppler shifts and noise power, is generally unknown to the receiver. In this thesis, two kinds of mobile OFDM systems are investigated for data detection and channel estimation. Different from previous works, which highly depend on the statistical information of the doubly selective channel to deliver accurate channel estimation and data detection results, we focus on more practical scenarios with unknown channel orders and Doppler frequencies. Firstly, point-to-point OFDM system with high mobility is considered. Due to the unknown channel characteristics, we formulate the channel using GCE-BEM with a large oversampling factor. The resulted GCE-BEM coefficients are sparse on delay-Doppler domain and contain only a few nonzero elements. To enable the identification of nonzero entries, sparsity enhancing Gaussian priors with Gamma hyperpriors are adopted. An iterative algorithm is developed under variational inference (VI) framework. The proposed algorithm iteratively estimate the channel, recover the unknown data using Viterbi algorithm and learn the channel and noise statistical information, using only limited number of pilot subcarrier in one OFDM symbol. Secondly, we investigate multihop amplify-and-forward (AF) OFDM system, where system structure is generally unknown to the receiver due to the variable number of hops and relaying paths in high mobility environment. We notice that in AF relaying systems, the composite source-relay-destination channel is sufficient for data detection. Then we integrate the multilink, multihop channel matrices into one composite channel matrix, which turns out to have the same structure as the point-to-point OFDM channel. The reformulated system model is more concise and a similar iterative algorithm to that of the point-to-point case can be derived to estimate the composite channel and detect data. This means that the proposed framework applies to OFDM system under high mobility regardless of the system structure. Simulation results show that the performance of the proposed algorithm is very close to that of the optimal channel estimation and data detection algorithm, which requires specific information of system structure, channel tap positions, channel lengths, Doppler shifts as well as noise powers. It is worth noting that, the close-to-ideal performance of the proposed algorithms is achieved with none of the above information. / published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

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published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Στις υποθαλάσσιες επικοινωνίες χρησιμοποιούνται ακουστικά κύματα για τη μεταφορά της πληροφορίας. Κατά τη διαδικασία της μετάδοσης εμφανίζονται προβλήματα που σχετίζον- ται με τη καθυστέρηση διάδοσης, την εξασθένηση του σήματος, τις πολλαπλές οδεύσεις λόγω ανάκλασης καθώς και άλλα προβλήματα που παρουσιάζονται στις ασύρματες επι- κοινωνίες. Στην παρούσα εργασία αναλύεται και εξετάζεται η πλειονότητα αυτών. Στο πρώτο κεφάλαιο γίνεται μία συνοπτική περιγραφή του φαινομένου των διαλείψεων καθώς και η ανάλυση βασικών εννοιών, τις οποίες συναντάμε σε επόμενα κεφάλαια. Στο δεύτερο κεφάλαιο περιγράφεται η πολύπλεξη OFDM, η οποία χρησιμοποιείται στις περισσότερες εφαρμογές των υποθαλάσσιων επικοινωνιών. Στο τρίτο κεφάλαιο αναλύεται μαθηματικά η τυφλή εκτίμηση για συστήματα OFDM και αναφέρονται επίσης προσαρμοστικοί αλγόριθ- μοι για την εκτίμηση του καναλιού με βάση την θεωρία η οποία παρουσιάζεται. Τέλος στο τέταρτο κεφάλαιο παρατίθεται κώδικας matlab, στον οποίο γίνεται χρήση των αλγορίθμων των τρίτου κεφαλαίου με σκοπό την εκτίμηση της κρουστικής απόκρισης του καναλιού, ενώ πέραν της γενικής περίπτωσης εξετάζονται επιπλέον λύσεις sparse μορφής. / --