Grant-access based legacy networks are unable to deliver Uplink (UL) dominated sporadic transmission of short packets with low signalling overhead, which are the requirements of Massive Machine Type Communication (mMTC). Hence, grant-free Non-Orthogonal Multiple Access (NOMA) techniques are more suited for mMTC. Due to the inherent sparsity in the user activity pattern in an mMTC system, the existing literature revolves around Compressive Sensing based Multi User Detection (CS-MUD) algorithms, which employ random or Zadoff-Chu spreading sequences for NOMA. In this paper we explore the utility of sinusoidal spreading sequences, which allow some non-iterative algorithms to be employed in the context of active user detection, channel estimation and data detection in a UL grant-free mMTC system. These algorithms do not need prior knowledge about the user behaviour statistics, noise statistics, and the level of sparsity in the user activity, which might be difficult to obtain at the Base Station (BS). We present a comprehensive performance and complexity analysis to assess the feasibility of our proposal. Extensive numerical trials demonstrate the utility of sinusoidal codes for mMTC.

Sparse Code Multiple Access (SCMA) is considered to be one of the potential non-orthogonal multiple access (NOMA) techniques mainly targeting uplink transmission to support massive connectivity. In this work, an efficient high dimensional uplink SCMA codebook design is proposed using the Equiangular Tight Frames. Codebook design problem is posed as construction of generalized Equiangular Tight Frames where mutual coherence among codewords are minimized toward the optimal bound. The minimization problem is handled using gradient-based alternating projection technique under average symbol energy constraint. An important feature of the proposed design is monotonically improved Bit Error Rate (BER) performance with scaling of the proposed codebook.

A Zadoff-Chu (ZC) sequence is uncorrelated with a non-zero cyclically shifted version of itself. However, this alone is insufficient to mitigate inter-code interference in LTE initial uplink synchronization. The performance of the state-of-the-art algorithms vary widely depending on the specific ZC sequences employed. We develop a systematic procedure to choose the ZC sequences that yield the optimum performance. It turns out that the procedure for ZC code selection in LTE standard is suboptimal when the carrier frequency offset is not small.

Initial uplink synchronization is an integral part of wireless communication systems. It enables the base station (BS) to detect new subscriber stations (SS) willing to commence communication. It also enables the BS to estimate the uplink channel parameters of these SSs. Accurate estimation of channel parameters is crucial, as they ensure the uplink signals from all the SSs arrive at the BS synchronously at similar power levels. However, this detection and estimation problem turns out to be very challenging when multiple users initiate the synchronization procedure at the same time. We address this issue by exploiting the underlying sparsity of the estimation problem. We propose a fast sparse signal recovery approach that shows a clear improvement in detection and estimation performance compared to other state-of-the-art methods. The proposed method can be integrated into any OFDM based standard.

A maximum a posteriori (MAP) estimation algorithm is given for reconstructing sparse signals, where a part of the support, and an approximate estimate of the sparse signal are known. This method is useful, e.g., in magnetic resonance image (MRI) sequence, natural video sequences, etc, where it is required to recursively reconstruct a sequence of mutually correlated sparse signals or images. Here we use the last signal as an a priori estimate of the current signal. The priori information is often inaccurate, and we adopt MAP estimation framework to deal with this issue. Simulation studies are performed, and the algorithm is applied to reconstruct MRI image sequences.

We show the powerful sparse signal recovery approach FOCUSS is a convex-concave procedure. It follows a Newton-like decent direction by retaining the positive definite component of the Hessian matrix. This motivates an improved implementation of FOCUSS.

We propose a joint sparse signal recovery approach to coherent spectral analysis of irregularly sampled signals. These signals share the same frequencies, and are sampled asynchronously. Two types of solution procedures are considered. First one is a convex optimization approach, which optimizes a mixedl2,1-norm. The other method minimizes an approximation ofl2,0-norm and the resulting algorithm can be implemented using a few FFTs and IFFTs. We demonstrate the effectiveness of the sparse recovery approach using simulation experiments. In particular, thel2,0approximation approach is very fast. In addition, it offers increased resolution, improved robustness to noise, and works well with limited number of data samples.

We present a frequency estimation method based on a sparse representation of irregular samples with an overcomplete basis. We enforce sparsity by imposing penalties based on an approximate ℓ0- norm. A number of recent theoretical results on compressed sensing justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the frequency spectrum that exhibits super-resolution. Our formulation leads to an optimization problem, which we solve efficiently in an iterative algorithm. The simulation results demonstrate that that the proposed algorithm outperforms several other state-of-art methods.

lscr0Norm based signal recovery is attractive in compressed sensing as it can facilitate exact recovery of sparse signal with very high probability. Unfortunately, direct lscr0norm minimization problem is NP-hard. This paper describes an approximate lscr0norm algorithm for sparse representation which preserves most of the advantages of lscr0norm. The algorithm shows attractive convergence properties, and provides remarkable performance improvement in noisy environment compared to other popular algorithms. The sparse representation algorithm presented is capable of very fast signal recovery, thereby reducing retrieval latency when handling high dimensional signal.

Multiple description coding (MDC) offers an elegant approach to data transmission over lossy packet-based networks. This paper proposes an MDC decoder for Compressed Sensing (CS) based MDC. Our decoder minimizes ¿0norm of the total variation of the image in a recursive manner, making it effective when different descriptions experience different time delays in the network. The proposed approach brings in a significant performance improvement in reconstruction accuracy and reconstruction time.

This paper presents a new approach, known as symmetry axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the axis of symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.

We address the problem of finding a set of sparse signals that have nonzero coefficients in the same locations from a set of their compressed measurements. A mixed lscr2,0norm optimization approach is considered. A cost function appropriate to the joint-sparse problem is developed, and an algorithm is derived. Compared to other convex relaxation based techniques, the results obtained by the proposed method show a clear improvement in both noiseless and noisy environments.