This paper presents performance analysis and cross-layer design approaches for hybrid ARQ (HARQ) protocols in wireless networks, which employ adaptive modulation and coding (AMC) in conjunction with adaptive cooperative diversity and are subject to time-correlated fading channels. We first consider a point-to-point scenario, i.e., non-cooperative HARQ with AMC. Utilizing a Markov channel model which accounts for the temporal correlation in the successive transmission of incremental redundancy by the HARQ protocol, we derive the system throughput and the packet loss probability based on a rate compatible punctured convolutional code family. Next, we consider a cooperative HARQ (CHARQ) scheme in which a relay node, also equipped with AMC, retransmits redundancy packets when it is able to decode the source information packet correctly. For this scenario, we also derive the throughput and packet loss performance. Finally, we present a cross-layer AMC design approach which takes into account the hybrid ARQ protocol at the link layer. The results illustrate that including AMC in the HARQ protocols leads to a substantial throughput gain. While the performance of the AMC with HARQ protocol is strongly affected by the channel correlation, the CHARQ protocol provides noticeable performance gains over correlated fading channels as well

Solving a large-scale system of linear equations is a key step at the heart of many algorithms in machine learning, scientific computing, and beyond. When the problem dimension is large, computational and/or memory constraints make it desirable, or even necessary, to perform the task in a distributed fashion. In this paper, we consider a common scenario in which a taskmaster intends to solve a large-scale system of linear equations by distributing subsets of the equations among a number of computing machines/cores. We propose an accelerated distributed consensus algorithm, in which at each iteration every machine updates its solution by adding a scaled version of the projection of an error signal onto the nullspace of its system of equations, and where the taskmaster conducts an averaging over the solutions with momentum. The convergence behavior of the proposed algorithm is analyzed in detail and analytically shown to compare favorably with the convergence rate of alternative distributed methods, namely distributed gradient descent, distributed versions of Nesterov's accelerated gradient descent and heavy-ball method, the block Cimmino method, and ADMM. On randomly chosen linear systems, as well as on real-world data sets, the proposed method offers significant speed-up relative to all the aforementioned methods. Finally, our analysis suggests a novel variation of the distributed heavy-ball method, which employs a particular distributed preconditioning, and which achieves the same theoretical convergence rate as the proposed consensus-based method.in engineering and the sciences. In particular, we consider the setting in which a taskmaster intends to solve a large-scale system of equations with the help of a set of computing machines/cores (Figure 1). This problem can in general be cast as an optimization problem with a cost function that is separable in the data (but not in the variables) 1 . Hence, there are general approaches to construct distributed algorithms for this problem, such as distributed versions of gradient descent and its variants (e.g. Nesterov's accelerated gradient [15], heavy-ball method [16], etc.), where each machine computes the partial gradient corresponding to a term in the cost and the taskmaster then aggregates the partial gradients by summing them, as well as the so-called Alternating Direction Method of Multipliers (ADMM) and its variants [3]. Among others, some recent approaches for Distributed Gradient Descent (DGD) have been presented and analyzed in [23], [17] and [21], and also coding techniques for robust DGD in the presence of failures and straggler machines have been studied in [10,20]. ADMM has been widely used [7,5,22] for solving various convex optimization problems in a distributed way, and in particular for consensus optimization [13,18,12], which is the relevant one for the type of separation that we have here.

In this work we study a Multiple-Input Multiple-Output wireless system where the channel state information is partially available at the transmitter through a feedback link. Based on Singular Value Decomposition, the MIMO channel is split into independent sub-channels which allows separate, and therefore, efficient decoding of the transmitted data signal. Effective feedback of the required spatial channel information entails efficient quantization/encoding of a Haar unitary matrix. The parameter reduction of anparameters is performed through Givens decomposition. We prove that Givens matrices of a Haar unitary matrix are statistically independent. Subsequently, we derive the Probability Distribution Function (PDF) of the corresponding matrix elements. Based on these analyses, an efficient quantization scheme is proposed. 2The performance evaluation is provided for a scenario where the rates allocated to each independent channel are selected according to its corresponding gain. The results indicate a significant performance improvement compared to the performance of MIMO systems without feedback at the cost of a very low-rate feedback link.

A nano abnormality detection scheme (NADS) in molecular nano-networks is studied. This is motivated by the fact that early detection of diseases such as cancer play a crucial role in their successful treatment. The proposed NADS is in fact a two-tier network of sensor nano-machines (SNMs) in the first tier and a data-gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells (abnormality) by variations in input and/or parameters of a nano-communications channel (NCC). The noise of SNMs as their nature suggest is considered correlated in time and space and herein assumed additive Gaussian. In the second step, the SNMs transmit micro-scale messages over a noisy micro communications channel (MCC) to the DGN, where a decision is made upon fusing the received signals.We find an optimum design of detectors for each of the NADS tiers based on the end-to-end NADS performance. The detection performance of each SNM is analyzed by setting up a generalized likelihood ratio test. Next, taking into account the effect of the MCC, the overall performance of the NADS is analyzed in terms of probabilities of misdetection and false alarm. In addition, computationally efficient expressions to quantify the NADS performance is derived by providing respectively an approximation and an upper bound for the probabilities of misdetection and false alarm. This in turn enables formulating a design problem, where the optimized concentration of SNMs in a sample is obtained for a high probability of detection and a limited probability of false alarm. The results indicate that otherwise ignoring the spatial and temporal correlation of SNM noise in the analysis, leads to an NADS that noticeably underperforms in operations.

Abstract-Recent studies indicate the feasibility of in-band fullduplex (FD) wireless communications, where a wireless radio transmits and receives simultaneously in the same band. Due to its potential to increase the capacity, analyzing the performance of a cellular network that contains full-duplex devices is crucial. In this paper, we consider maximizing the weighted sum-rate of downlink and uplink of a single cell OFDMA network which consists of an imperfect FD base-station (BS) and a mixture of half-duplex and imperfect full-duplex mobile users. To this end, the joint problem of sub-channel assignment and power allocation is investigated and a two-step solution is proposed. A heuristic algorithm to allocate each sub-channel to a pair of downlink and uplink users with polynomial complexity is presented. The power allocation problem is convexified based on the difference of two concave functions approach, for which an iterative solution is obtained. Simulation results demonstrate that when all the users and the BS are perfect FD nodes the network throughput could be doubled, Otherwise, the performance improvement is limited by the inter-node interference and the self-interference. We also investigate the effect of the self-interference cancellation capability and the percentage of FD users on the network performance in both indoor and outdoor scenarios.

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In this paper, a link adaptation and untrusted relay assignment (LAURA) framework for efficient and reliable wireless cooperative communications with physical layer security is proposed. Using sharp channel codes in different transmission modes, reliability for the destination and security in the presence of untrusted relays (low probability of interception) are provided through rate and power allocation. Within this framework, several schemes are designed for highly spectrally efficient link adaptation and relay selection, which involve different levels of complexity and channel state information requirement. Analytical and simulation performance evaluation of the proposed LAURA schemes are provided, which demonstrates the effectiveness of the presented designs. The results indicate that power adaptation at the source plays a critical role in spectral efficiency performance. Also, it is shown that relay selection based on the signal to noise ratio of the source to relays channels provides an interesting balance of performance and complexity within the proposed LAURA framework. Index TermsAmplify-and-forward relaying, cooperative communications, link adaptation, physical layer security, relay selection, untrusted relay.

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