Cognitive Orthogonal Precoder for Two-tiered Networks Deployment

In this work, the problem of cross-tier interference in a two-tiered (macro-cell and cognitive small-cells) network, under the complete spectrum sharing paradigm, is studied. A new orthogonal precoder transmit scheme for the small base stations, called multi-user Vandermonde-subspace frequency division multiplexing (MU-VFDM), is proposed. MU-VFDM allows several cognitive small base stations to coexist with legacy macro-cell receivers, by nulling the small- to macro-cell cross-tier interference, without any cooperation between the two tiers. This cleverly designed cascaded precoder structure, not only cancels the cross-tier interference, but avoids the co-tier interference for the small-cell network. The achievable sum-rate of the small-cell network, satisfying the interference cancelation requirements, is evaluated for perfect and imperfect channel state information at the transmitter. Simulation results for the cascaded MU-VFDM precoder show a comparable performance to that of state-of-the-art dirty paper coding technique, for the case of a dense cellular layout. Finally, a comparison between MU-VFDM and a standard complete spectrum separation strategy is proposed. Promising gains in terms of achievable sum-rate are shown for the two-tiered network w.r.t. the traditional bandwidth management approach.Comment: 11 pages, 9 figures, accepted and to appear in IEEE Journal on Selected Areas in Communications: Cognitive Radio Series, 2013. Copyright transferred to IEE

Multiple Beamforming with Perfect Coding

Perfect Space-Time Block Codes (PSTBCs) achieve full diversity, full rate, nonvanishing constant minimum determinant, uniform average transmitted energy per antenna, and good shaping. However, the high decoding complexity is a critical issue for practice. When the Channel State Information (CSI) is available at both the transmitter and the receiver, Singular Value Decomposition (SVD) is commonly applied for a Multiple-Input Multiple-Output (MIMO) system to enhance the throughput or the performance. In this paper, two novel techniques, Perfect Coded Multiple Beamforming (PCMB) and Bit-Interleaved Coded Multiple Beamforming with Perfect Coding (BICMB-PC), are proposed, employing both PSTBCs and SVD with and without channel coding, respectively. With CSI at the transmitter (CSIT), the decoding complexity of PCMB is substantially reduced compared to a MIMO system employing PSTBC, providing a new prospect of CSIT. Especially, because of the special property of the generation matrices, PCMB provides much lower decoding complexity than the state-of-the-art SVD-based uncoded technique in dimensions 2 and 4. Similarly, the decoding complexity of BICMB-PC is much lower than the state-of-the-art SVD-based coded technique in these two dimensions, and the complexity gain is greater than the uncoded case. Moreover, these aforementioned complexity reductions are achieved with only negligible or modest loss in performance.Comment: accepted to journa

Channel Estimation in Coded Modulation Systems

With the outstanding performance of coded modulation techniques in fading channels, much research efforts have been carried out on the design of communication systems able to operate at low signal-to-noise ratios (SNRs). From this perspective, the so-called iterative decoding principle has been applied to many signal processing tasks at the receiver: demodulation, detection, decoding, synchronization and channel estimation. Nevertheless, at low SNRs, conventional channel estimators do not perform satisfactorily. This thesis is mainly concerned with channel estimation issues in coded modulation systems where different diversity techniques are exploited to combat fading in single or multiple antenna systems. First, for single antenna systems in fast time-varying fading channels, the thesis focuses on designing a training sequence by exploiting signal space diversity (SSD). Motivated by the power/bandwidth efficiency of the SSD technique, the proposed training sequence inserts pilot bits into the coded bits prior to constellation mapping and signal rotation. This scheme spreads the training sequence during a transmitted codeword and helps the estimator to track fast variation of the channel gains. A comprehensive comparison between the proposed training scheme and the conventional training scheme is then carried out, which reveals several interesting conclusions with respect to both error performance of the system and mean square error of the channel estimator. For multiple antenna systems, different schemes are examined in this thesis for the estimation of block-fading channels. For typical coded modulation systems with multiple antennas, the first scheme makes a distinction between the iteration in the channel estimation and the iteration in the decoding. Then, the estimator begins iteration when the soft output of the decoder at the decoding iteration meets some specified reliability conditions. This scheme guarantees the convergence of the iterative receiver with iterative channel estimator. To accelerate the convergence process and decrease the complexity of successive iterations, in the second scheme, the channel estimator estimates channel state information (CSI) at each iteration with a combination of the training sequence and soft information. For coded modulation systems with precoding technique, in which a precoder is used after the modulator, the training sequence and data symbols are combined using a linear precoder to decrease the required training overhead. The power allocation and the placement of the training sequence to be precoded are obtained based on a lower bound on the mean square error of the channel estimation. It is demonstrated that considerable performance improvement is possible when the training symbols are embedded within data symbols with an equi-spaced pattern. In the last scheme, a joint precoder and training sequence is developed to maximize the achievable coding gain and diversity order under imperfect CSI. In particular, both the asymptotic performance behavior of the system with the precoded training scheme under imperfect CSI and the mean square error of the channel estimation are derived to obtain achievable diversity order and coding gain. Simulation results demonstrate that the joint optimized scheme outperforms the existing training schemes for systems with given precoders in terms of error rate and the amount of training overhead

Robust Lattice Alignment for K-user MIMO Interference Channels with Imperfect Channel Knowledge

In this paper, we consider a robust lattice alignment design for K-user quasi-static MIMO interference channels with imperfect channel knowledge. With random Gaussian inputs, the conventional interference alignment (IA) method has the feasibility problem when the channel is quasi-static. On the other hand, structured lattices can create structured interference as opposed to the random interference caused by random Gaussian symbols. The structured interference space can be exploited to transmit the desired signals over the gaps. However, the existing alignment methods on the lattice codes for quasi-static channels either require infinite SNR or symmetric interference channel coefficients. Furthermore, perfect channel state information (CSI) is required for these alignment methods, which is difficult to achieve in practice. In this paper, we propose a robust lattice alignment method for quasi-static MIMO interference channels with imperfect CSI at all SNR regimes, and a two-stage decoding algorithm to decode the desired signal from the structured interference space. We derive the achievable data rate based on the proposed robust lattice alignment method, where the design of the precoders, decorrelators, scaling coefficients and interference quantization coefficients is jointly formulated as a mixed integer and continuous optimization problem. The effect of imperfect CSI is also accommodated in the optimization formulation, and hence the derived solution is robust to imperfect CSI. We also design a low complex iterative optimization algorithm for our robust lattice alignment method by using the existing iterative IA algorithm that was designed for the conventional IA method. Numerical results verify the advantages of the proposed robust lattice alignment method