Vlatko Lipovac

On the performance of MSK signal transmission over a multipath channel with small time dispersion

In this paper, the performance of MSK signal transmission over a mobile radio channel with small time dispersion is analyzed. In contrast to existing investigations, we made no restrictive assumptions about the impulse response of the channel or the sampling time and derive analytical expressions for the error floor and the optimum sampling instant. We found that the error probability depends not only on the delay spread, but also on the squared mean delays of echoes that are advanced or delayed with respect to the sampling point and on the power balance between them. Furthermore, we show that in the general case, the optimum sampling instant is not identical to the mean delay. Comparison of our results with Monte Carlo simulations show very good agreement.

RSS-based localization in wireless sensor networks using SOCP relaxation

This paper addresses the problem of locating a single source from noisy received signal-strength (RSS) measurements in wireless sensor networks (WSNs). To overcome the non-convexity of the maximum likelihood (ML) optimization problem, we provide an efficient convex relaxation that is based on the second order cone programming (SOCP), for both cases of known and unknown source transmit power, and we use a simple iterative procedure to solve the problem when the transmit power and the path loss exponent (PLE) are simultaneously unknown. Simulation results demonstrate that the new approach outperforms the existing ones in terms of the estimation accuracy, while in terms of the complexity, it represents a good balance when compared to the existing approaches.

Efficient estimator for distributed RSS-based localization in wireless sensor networks

We address the received signal strength (RSS) based target localization problem in large-scale cooperative wireless sensor networks (WSNs). Using the noisy RSS measurements, we formulate the localization problem based on the maximum likelihood (ML) criterion. Although ML-based solutions have asymptotically optimal performance, the derived localization problem is non-convex. To overcome this difficulty, we propose a convex relaxation leading to second-order cone programming (SOCP) estimator, which can be solved efficiently by interior-point algorithms. Moreover, we investigate the case where target nodes limit the number of cooperating nodes by selecting only those neighbors with the highest RSS. This simple procedure can reduce the energy consumption of an algorithm in both communication and computation phase. Our simulation results show that the proposed approach outperforms significantly the existing ones in terms of the estimation accuracy and convergence. Furthermore, the new approach does not suffer significant performance degradation when the number of cooperating nodes is reduced.

Verification of OFDM error floor in time-dispersive LTE FDD DL channel

In this paper, the state-of-the-art laboratory environment is presented that is aimed for experimental verification of the earlier obtained analytical OFDM error floor model but now in concrete LTE FDD downlink channel conditions, which are for this purpose hardware and software simulated in the Communications Systems Laboratory of the University of Dubrovnik. At this point, the very preliminary verification of the earlier derived error floor formula is reported as the test results achieved by means of the industry-standard simulation tool closely match the ones coming out of the earlier model-specific basic Monte-Carlo simulations.

Convex optimization-based beamforming in cognitive radio multicast transmission

A novel algorithm for transmit beamforming to single cochannel multicast group is presented in this paper. We consider the max-min fairness (MMF) based beamforming problem where the maximization of the smallest receiver signal-to-noise ratio (SNR) over the secondary users subject to constraints on the transmit power and interference caused to the primary users. It is shown that this problem, which is nonconvex NP-hard, can be approximated by a convex second-order cone programming (SOCP) problem. Then, an iterative algorithm which successively improves the SOCP approximation is presented. Simulation results show the superior performance of the proposed approach, together with a reduced computational complexity, as compared to the state-of-the-art approach.

Improved Model for Estimation of Spatial Averaging Path Length

In mobile communication systems, the transmitted RF signal is subject to mutually independent deterministic path loss and stochastic multipath and shadow fading. As at each spatial location mostly the composite signal samples are measured, their components are distinguished by averaging out the multipath-caused signal level variations, while preserving just the ones due to shadowing. The prerequisite for this is the appropriateness of the local area averaging path length that enables obtaining the local mean (composed of mean path loss and shadow fading) and the multipath fading as difference between the composite signal sample and the local mean. However, the so far reported analytical approaches to estimation of the averaging path length are based on considering either the multipath or just the shadow fading, with applicability limited to only specific topologies and frequencies. Therefore, in this paper, the most widely used Lee analytical method is generalized and improved by considering multipath and shadowing concurrently, so providing the general closed-form elementary-function based estimation of the optimal averaging path length as a function of common multipath and shadow fading parameters characterizing particular propagation environment. The model enables recommendations for the optimal averaging length for all propagation conditions facing the mobile receiver.

Estimation of radio signal spatial local mean

Accurate estimation of RF signal spatial local mean is of crucial importance in wireless communication systems planning, design and operation, specifically in coverage planning tools, channel access and power control, handoff algorithms, etc. However, so far, no universally applicable and verified approach for calculation of relevant averaging parameters has been developed. In this paper, comprehensive overview of spatial averaging problem is presented, including available methods for solving it. With this regard, closed-form expressions for averaging parameters calculation are delivered based on the approaches that are considered most accurate. Finally, the validity domains of these expressions are tested, indicating their applicability areas under specific propagation conditions.

Design of good constellations for single-subcarrier intensity-modulated optical systems

We address the problem of constellation design for single-subcarrier intensity-modulated direct-detected optical systems. We seek to design constellations that minimize the average electrical power, average optical power and peak optical power for a given minimum Euclidean distance between constellation points. The constellation design is formulated as a nonconvex optimization problem with a convex cost function, nonconvex quadratic constraints and second-order cone constraints. It is shown that this problem can be approximated by a second-order cone programming (SOCP) problem. We propose an iterative procedure in which the SOCP approximation is refined in each iteration. The new constellations are compared to the state-of-the-art constellations in terms of power efficiency. Our 32- and 64-point constellations outperform the corresponding QAM-based and face-centered cubic lattice constellations.

Analysis of OFDM error floor in indoor channels by stochastic modeling

In this paper, we propose a model for estimating the error floor in a small-time-dispersion environment - typically indoor, where both channel and overall OFDM symbol are represented stochastically. The developed novel model for the error floor prediction involves modified common channel time dispersion parameters as well as the ones characterizing the OFDM signal. The validity of the model was confirmed by the results of the corresponding Monte-Carlo simulations.

Suppressing the OFDM CFO-Caused Constellation Symbol Phase Deviation by PAPR Reduction

The well-known major drawbacks of the Orthogonal Frequency-Division Multiplexing (OFDM), namely, the transmitter versus receiver Carrier Frequency Offset (CFO), and the Peak-to-Average Power Ratio (PAPR) of the transmitted OFDM signal, may degrade the error performance, by causing Intercarrier Interference (ICI), as well as in-band distortion and adjacent channel interference, respectively. Moreover, in spite of the utmost care given to CFO estimation and compensation in OFDM wireless systems, such as wireless local networks or the mobile radio systems of the fourth generation, e.g., the Long-Term Evolution (LTE), still some residual CFO remains. With this regard, though so far the CFO and the PAPR have been treated independently, in this paper, we develop an Error Vector Magnitude (EVM) based analytical model for the CFO-induced constellation symbol phase distortion, which essentially reveals that the maximal CFO-caused squared phase deviation is linear with the instantaneous (per-OFDM-symbol) PAPR. This implies that any PAPR reduction technique, such as simple clipping or coding, indirectly suppresses the CFO-induced phase deviation, too. The analytically achieved results and conclusions are tested and successfully verified by conducted Monte Carlo simulations.