Optimal pilot placement for frequency offset estimation and data detection in burst transmission systems

In this letter, we address the problem of pilot design for Carrier Frequency Offset (CFO) and data detection in digital burst transmission systems. We consider a quasi-static flat-fading channel. We find that placing half of the pilot symbols at the beginning of the burst and the other half at the end of the burst is optimal for both CFO estimation and data detection. Our findings are based on the Cram´er-Rao bound and on empirical evaluations of the bit error rate for different pilot designs. The equal-preamble-postamble pilot design is shown to provide a significant gain in performance over the conventional preambleonly pilot design

Blind frequency-offset estimator for OFDM systems transmitting constant-modulus symbols

We address the problem of carrier frequency offset (CFO) synchronization in OFDM communications systems in the context of frequency-selective fading channels. We consider the case where the transmitted symbols have constant modulus, i.e., PSK constellations. A novel blind CFO estimation algorithm is developed. The new algorithm is shown to greatly outperform a recently published blind technique that exploits the fact that practical OFDM systems are not fully loaded. Further, the proposed algorithm is consistent even when the system is fully loaded. Finally, the proposed CFO estimator is obtained via a one-dimensional search, the same as with the existing virtual subcarrier-based estimator, but achieves a substantial gain in performance (10-dB SNR or one order of magnitude in CFO MSE)

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)

Block synchronisation for joint channel and DC-offset estimation using data-dependent superimposed training

In this paper, we propose a new (single-step) block synchronisation algorithm for joint channel and DC-offset estimation for data-dependent superimposed training (DDST). While a (two-step) block synchronisation algorithm for DDST has previously been proposed in [5], due to interference from the information-bearing data it performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were then estimated in [5] in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by exploiting the special structure of the channel output’s cyclic mean vector. The result is a BER performance superior to the DDST synchronisation algorithm first published in [5]

Synchronization in wireless communications

The last decade has witnessed an immense increase of wireless communications services in order to keep pace with the ever increasing demand for higher data rates combined with higher mobility. To satisfy this demand for higher data rates, the throughput over the existing transmission media had to be increased. Several techniques were proposed to boost up the data rate: multicarrier systems to combat selective fading, ultra wide band (UWB) communications systems to share the spectrum with other users, MIMO transmissions to increase the capacity of wireless links, iteratively decodable codes (e.g., turbo codes and LDPC codes) to improve the quality of the link, cognitive radios, and so forth

Breaking the Area Spectral Efficiency Wall in Cognitive Underlay Networks

In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network. The developed framework explicitly accommodates channel, topological and medium access uncertainties. The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks. To this end, we highlight two available degrees of freedom, i.e., shaping medium access or transmit power. While from the primary user's perspective tuning either to control the interference is equivalent, the picture is different for the secondary network. We show the existence of an area spectral efficiency wall under both adaptation schemes. We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance. But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks. We explore several design parameters for both adaptation schemes. Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies

A Secure Optimum Distributed Detection Scheme in Under-Attack Wireless Sensor Networks

We address the problem of centralized detection of a binary event in the presence of fraction falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidth constrained under-attack spatially uncorrelated distributed wireless sensor network (WSN). The SNs send their one-bit test statistics over orthogonal channels to the fusion center (FC), which linearly combines them to reach to a final decision. Adopting the modified deflection coefficient as an alternative function to be optimized, we first derive in a closed-form the FC optimal weights combining. But as these optimal weights require a-priori knowledge that cannot be attained in practice, this optimal weighted linear FC rule is not implementable. We also derive in a closed-form the expressions for the attacker “flipping probability” (defined in paper) and the minimum fraction of compromised SNs that makes the FC incapable of detecting. Next, based on the insights gained from these expressions, we propose a novel and non-complex reliability-based strategy to identify the compromised SNs and then adapt the weights combining proportional to their assigned reliability metric. In this way, the FC identifies the compromised SNs and decreases their weights in order to reduce their contributions towards its final decision. Finally, simulation results illustrate that the proposed strategy significantly outperforms (in terms of FC’s detection capability) the existing compromised SNs identification and mitigation schemes