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.

We explore the problem of resource allocation in a Third Generation Partnership Project (3GPP) long-term evolution (LTE) based cognitive radio network (CRN). The network model consists of a number of service providers (SPs) with fixed licensed spectrum bands. The network offers the wireless services to two types of users: primary and secondary. The primary users (PUs) get prioritized access to the licensed spectrum bands. The secondary users (SUs) are served on the best-effort (non-prioritized) basis. In this paper we consider the specific design features of LTE radio interface associated with the uplink spectrum access, scheduling process, and limited control channel capacity of the LTE system. We establish the relation between the number of users in the system, and the scheduling delay (which is the largest contributor to the packet end-to-end delay in LTE network). Using these results, we propose a simple algorithm to assign the spectrum for the SUs without violating the quality of service (QoS) requirements of the PU, and implement it in an LTE-based CRN. Consistent performance of the algorithm is verified using OPNET-based simulations.

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.

Recently, the IEEE 1900.4 standard specified a policy-based radio resource management (RRM) framework in which the decision making process is distributed between network-terminal entities. The standard facilitates the optimization of radio resource usage to improve the overall composite capacity and quality of service (QoS) of heterogeneous wireless access networks within a composite wireless network (CWN). Hence, the study of different RRM techniques to maintain either a load- or QoS-balanced system through dynamic load distribution across a CWN is pivotal. In this paper, we present and evaluate three primary RRM techniques from different aspects, spanning across predictive versus reactive to model-based versus measurement-based approaches. The first technique is a measurement-based predictive approach, known as predictive load balancing (PLB), commonly employed in the network-distributed RRM framework. The second technique is a model-based predictive approach, known as predictive QoS balancing (PQB), typically implemented in the network-centralized RRM framework. The third technique is a measurement-based reactive approach, known as reactive QoS balancing (RQB), anchored in the IEEE 1900.4 network-terminal distributed RRM framework. Comprehensive performance analysis between these three techniques shows that the IEEE 1900.4-based RQB algorithm yields the best improvement in QoS fairness and aggregate end-user throughput while preserving an attractive baseline QoS property.

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.

Next generation wireless networks are expected to integrate harmoniously in an IP-based core network. Such paradigm shift will amalgamate several distinct radio access technologies into a unifying composite network. However, this creates a fundamental problem for handsets which manifests in high power consumption due to network discovery that is typically carried out by performing scanning operations. Since handsets are naturally constrained by its battery lifetime, this paper analyzes the impact of network discovery on the handsets' power consumption from the network architecture perspective. Particular, we focus on network discovery through the scanning (information pulling) and broadcasting (information pushing) approaches. In this context, we extend a unified analytical model to evaluate the performance comparison of energy efficiency between the legacy and terminal oriented network-assisted (TONA) architectures. Comprehensive numerical and simulation results show that the TONA handover architecture provides an energy saving of 8 35% to promote green handsets under various network load and scanning intervals.

Priority based packet transmission techniques are commonly used in communication networks to support multimedia services. In wireless networks mainly type of service or queue measurement based packet transmission priority techniques are used. This paper introduces a novel two stage traffic prediction and type of service based priority technique for an infrastructure based Wireless Local Area Network. The developed algorithm alters the priority of transmission queues and services in a radio access network based on the predicted traffic volume and the conventional type of service priority technique for multimedia packet transmissions. Simulation results show that our proposed algorithm improves the QoS of multimedia traffic significantly. An OPNET based simulation model has been developed to obtain the performance results for a multiple access points based wireless infrastructure network.

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.

Future wireless networks will enjoy ubiquitous connectivity by taking advantage of the IP core convergence which is seen as the lingua franca of heterogeneous access networks ecosystem. It is expected that the prevalence of WLAN and the advent of IEEE 802.11n standard will continue to offer compelling opportunities and therefore be considered as one of the de-facto wireless access network. However, it is known that wireless network conditions in general are diverse owing to both traffic and wireless channel variations. This raises the importance of exploiting diversity across a multiple access points (multi-AP) WLAN, which requires an advanced network control mechanism to effectuate uniform load distribution, so that QoS of users and composite capacity can be improved. Although various load distribution algorithms for WLAN have been investigated in literature, there is a lack of performance comparison between different algorithms. In this paper, we present a comparison of three dynamic load distribution algorithms, viz. predictive load balancing (PLB), predictive QoS balancing (PQB) and reactive QoS balancing (RQB) for infrastructure-based WLAN with DCF access mechanism based on OPNET simulations.

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.

The hybrid filterbank architecture permits implementing accurate, high speed analog-to-digital converters. However, its design is technically involved since perfect reconstruction of the desired samples cannot be achieved in general. In this paper we propose a design method which generalizes existing approaches by dropping the bandlimited assumption on the input signal. The design minimizes the power of the reconstruction error in the samples, for a given input signal power spectrum. We discuss the use of blind techniques to estimate the analysis filterbank parameters as well as the input power spectrum, and we present simulation results to demonstrate the clear advantage of the proposed design, even under input spectrum uncertainties.

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.

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.

Discovery of differentially expressed genes between normal and diseased patients is a central research problem in bioinformatics. It is specially important to find few genetic markers which can be explored for diagnostic purposes. The performance of a set of markers is often measured by the associated classification accuracy. This motivates our ranking of genes depending on the minimum probability of classification errors (MPE) for each gene. In this work, we use Bayesian decision-making algorithm to compute MPE. A quantile-based probability density estimation technique is used for generating probability density functions of genes. The method is tested on three datasets: colon cancer, leukaemia, and hereditary breast cancer. The quality of the selected markers is evaluated by the classification accuracy obtained using support-vector-machine and a modified naive Bayes classifier. We obtain 96.77% accuracy in colon cancer and 97.06% accuracy in leukaemia, using only five genes in each case. Finally, using just three genes we get 100% accuracy in hereditary breast cancer. We also compare our results with those using the genes ranked by p-value and show that the genes ranked by MPE perform better or equal to those ranked by p-value.

We propose a robust interpolation algorithm for model-based spectral estimation. The interpolation data represent information about the half spectrum function associated with a given signal and are computed from an input-to-state filter. Our algorithm allows a large number of noisy interpolation data to be used to optimally fit a half spectrum function of a fixed order. The algorithm involves solving a set of linear matrix inequalities and is thus numerically efficient.

Rotation of the binary image of a document page for correcting the skew in the case of OCR or signature verification systems entails disfigurement of the shape of the characters, which brings down the performance of the pattern classifier. An elegant algorithm is presented in this paper for distortion-free rotation. Multirate signal processing principles are applied to solve this problem, which was hitherto handled using only heuristics. The resolution of the image is increased during rotation. The rotated image is decimated and thresholded after low pass filtering. Polyphase implementation of the filter is performed for efficient computation. The results show that it solves the problem of mutilations and occasional breaks obtained in simple rotation schemes and further, performs better than the scheme involving interpolation in the original or the rotated domains. Consequent on the better retention of their original shape, the recognition accuracy of originally skewed documents improves by more than 5%.