In this thesis, we consider the problem of distributed detection problem in cooperative communication networks when the channel state information (CSI) is unknown. The amplify-and-forward relay strategy is considered in this thesis. Since the CSI is assumed to be unknown to the system, the joint detection and estimation approach is considered in this work. The proposed scheme in this work differs from existing joint detection and estimation schemes in that it utilizes a distributed approach, which exploits node cooperation and achieves a better system performance in cooperative communication networks. Moreover, by contrast to the existing channel estimation and symbol detection schemes, the proposed scheme is mainly developed based on the assumption that the data communication from the source to each relay node is to undergo a correlated fading channel. We derive the joint detection and estimation rules for our problem using the expectation-maximum (EM) algorithm. Simulation results show that the proposed scheme can perform well. Moreover, the obtained results show that the proposed iteration algorithm converges very fast, which implies the proposed scheme can work well in real-time applications.

Interference dominated wireless communications systems are considered. Joint detection methods are applied to combat the negative effects of the temporal and spatial interference. However, practical joint detectors are not commonly used due to their high complexity. Therefore, there is a constant need to deliver reduced complexity solutions that approach substantial fraction of the channel capacity. In the first part of this thesis it is shown that simple transmission technique employing repetition coding and interleaving combined with interference cancellation is an asymptotically optimal processing strategy when high interference is experienced, for instance due to the relatively high ratio of the number of signals to the number of orthogonal dimensions. Surprisingly, strong, capacity achieving codes exhibit inferior performance and are not well suited for iterative interference cancellation due to their sharp threshold characteristics. Motivated by this observation partitioned modulation is introduced and applied to a multiuser spread spectrum system, which inherently encompasses a repetition coding mechanism. The detection of the resulting signals employs a parallel interference cancellation approach, where the repetition code exchanges information iteratively with the canceller. Precise signal-to-noise ratio evolution of the proposed receiver as a function of demodulation iterations is given. It is shown, that for equal received power system, partitioned demodulator outperforms linear minimum mean squared error processor at a fraction of complexity. This receiver processing for a wide range of parameters delivers estimates that coincide with the optimal processing based on exhaustive search. For unequal received signal powers these advantages are even more visible and for a particular exponential power allocation the proposed system reaches the capacity of the channel. The analytical investigations are verified using computer simulations. In the second part of this dissertation, multi-user MIMO systems compliant with 3GPP LTE standard are considered. Turbo near-far resistant interference cancellation receiver is proposed. It jointly removes multi-user, multi-antenna and inter-symbol interference and outperforms traditional demodulation/decoding method adopted in the LTE standard. Semi-analytical method of predicting the performance of this joint receiver for any system setup is outlined. This approach makes it possible to tune up the performance of the system without running extensive bit-error-rate simulations. / Communications

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<p> Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this thesis, a new algorithm is developed to jointly detect and track more than two targets from a single detection. This method involves the use of tracking data in the detection process. For this purpose, target states are transformed into the detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which has proven to be effective in nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. This in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. Hypothesis testing is then used to find the correct detection event. The particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data and to validate the extraction and tracking of more than two unresolved targets.</p> / Thesis / Master of Applied Science (MASc)

In airborne communication systems such as airborne cell-extender repeaters the receiver faces the challenge of demodulating the signal of interest (SOI) in the presence of excessive amounts of Co-Channel Interference (CCI) from a large number of sources. This results in the overloaded environment where the number of near-equal power co-channel interferers exceeds the number of antenna array elements. This thesis first analyzes the interference environment experienced by an airborne cellular repeater flying at high altitudes. Link budget analysis using a two-ray propagation model shows that the antenna array mounted on an airborne receiver has to recover the SOI out of hundreds of co-channel interfering signals. This necessitates use of complex overloaded array signal processing techniques. An extensive literature survey on narrowband signal extraction algorithms shows that joint detection schemes, coupled with antenna arrays, provide a solution for narrowband overloaded array problem where as traditional beamforming techniques fail. Simulation results in this thesis investigates three "promising" overloaded array processing algorithms, Multi-User Decision Feedback Equalizer (MU-DFE), Iterative Least Squares with Projection (ILSP), and Iterative Least Squares with Enumeration (ILSE). ILSE is a non-linear joint maximum-likelihood detector, is shown to demodulate many more signals than elements even when the users are closely spaced and the channel is blindly estimated. Multi-user time delay estimation is one of the most important aspects of channel estimation for overloaded array processing. The final chapter of the thesis proposes a low-complexity data-aided time-delay estimation structure for embedding in a Per Survivor Processing (PSP) trellis for overloaded array processing. An extensive analysis proves that the multi-user delay estimation is separable, which leads to the proposed multi-user algorithm that estimates the user delays with a bank of simple data-aided synchronization loops to reduce the complexity. This thesis shows simulation results for the single-user case where the low-complexity Delay Locked Loop (DLL) structure, working at a low oversampling rate of 2 samples per symbol, estimates and compensates for any integer or non-integer sample delay within ±Tsym(symbol period). Two extensions to this technique are proposed to provide efficient multi-user delay estimation. The first multi-user structure employs a bank of DLLs, which compensate for the timing offset of each user simultaneously. This multi-user algorithm is suitable for CDMA-type applications, where each user has a distinct PN-code with good auto- and cross-correlation properties. We show that for spreading gain of 31, the presence of an interpolator enables us to reduce the oversampling factor from 4 to 2 samples per chip. Thus, the requirements of the A/D converter are relaxed without sacrificing system performance. Furthermore, we show that the proposed scheme meets the requirements of multi-user interference cancellation techniques for residual worst-case timing errors, i.e., residual timing error < 0.2 Tc, as reported in [200]. Finally, the thesis recommends a similar multi-user structure for narrowband TDMA-type system, which is based on bank of DLLs with whitening pre-filters at the front end of each branch. / Master of Science

An antenna array is overloaded when the number of cochannel signals in its operating environment exceeds the number of elements. Conventional space-time array processing for narrow-band signals fails in overloaded environments. Overloaded array processing (OAP) is most difficult when signals impinging on the array are near equal power, have tight excess bandwidth, and are of identical signal type. In this thesis, we first demonstrate how OAP is theoretically possible with the joint maximum likelihood (JML) receiver. However, for even a modest number of interfering signals, the JML receiverà ­s computational complexity quickly exceeds the real-time ability of any computer. This thesis proposes an iterative joint detection technique, Spatially Reduced Search Joint Detection, (SRSJD), which approximates the JML receiver while reducing its computational complexity by several orders of magnitude. This complexity reduction is achieved by first exploiting spatial separation between interfering signals with a linear pre-processing stage, and second, performing iterative joint detection with a (possibly) tail-biting and time"-varying trellis. The algorithm is sub-optimal but is demonstrated to well approximate the optimum receiver in modest signal to interference ratios. SRSJD is shown to demodulate over 2M zero excess bandwidth synchronous QPSK signals with an M element array. Also, this thesis investigates a temporal processing technique similar to SRSJD, Temporally Reduced Search Joint Detection (TRSJD), that separates co-channel, asynchronous, partial response signals. The technique is demonstrated to separate two near equal power QPSK signals with r= .35 root raised-cosine pulse shapes." / Master of Science

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Several optimal detection frameworks for wideband/multichannel spectrum sensing in cognitive radio networks are proposed. All frameworks search for multiple secondary transmission opportunities over a number of narrowband channels, enhancing the secondary network performance while respecting the primary network integrity and keeping the interference limited. Considering a periodic sensing scheme with either uniform or non-uniform channel sensing durations, the detection problems are formulated as joint optimization of the sensing duration(s) and individual detector parameters to maximize the aggregate achievable secondary throughput capacity given some bounds/limits on the overall interference imposed on the primary network. It is demonstrated that all the formulated optimization problems can be solved using convex optimization if certain practical constraints are applied. Simulation results attest that the proposed frameworks achieve superior performance compared to contemporary frameworks. To realize efficient implementation, an iterative low-complexity algorithm which solves one of the optimization problems with much lower complexity compared to other numerical methods is presented. It is established that the iteration-complexity and the complexity-per-iteration of the proposed algorithm increases linearly with the number of optimization variables (i.e. the number of narrowband channels). / Communication

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