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This article addresses the problem of channel estimation and pilot allocation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) networks for sparse doubly selective (DS) channels. In DS channels, due to significant Doppler shifts, channels vary fast, a huge number of coefficients must be estimated, and inter-carrier interference (ICI) must be overcome, which make the problem very challenging. To address these challenges, we reframe the issue as a distributed compressive sensing (DCS) coefficient estimation problem within the basis expansion model (BEM) for channel representation. This reduces the number of channel coefficients significantly as in the new problem, the jointly sparse BEM coefficients are estimated. To further improve estimation performance, we first employ a genetic algorithm to optimize pilot locations based on the mutual coherence of the measurement matrix as the criterion. Secondly, taking the advantage of the complex exponential (CE)-BEM, the correlation of the received signal is used to estimate the channel’s most significant lags (MSLs). Using optimized pilot locations and MSLs, we propose a novel algorithm, called semi-blind block orthogonal matching pursuit (SB-BOMP) for channel estimation. Extensive simulation results confirm that the proposed algorithm outperforms the traditional ones over DS channels.

Big data is generated from various sources, such as the Internet of things, social media, databases, wearables, smart cars, and so on, and is characterized by five V’s: volume, value, variety, velocity, and veracity. Transmitting big data to secondary users (SUs) over a cognitive cloud radio access network (CRAN) offers multiple benefits and critical challenges. To address these limitations, we have designed two deadline-aware, non-preemptive algorithms that maximize the sum of weighted data transferred by the network over admission, time scheduling, spectrum, and remote radio head (RRH) allocation decisions. Each data request can have a different size, target bit error rate (BER), minimum signal-to-noise ratio (SNR) requirement, and deadline, incorporating the simultaneous provision of various types of big data and ordinary data jointly. Furthermore, our formulation considers all five V’s of big data. The first algorithm we propose is an offline batch scheduling (OFB) algorithm, which assumes that all data requests are available at the time of optimization. While this sub-optimal algorithm has a lower complexity and can be implemented in larger networks than the global optimum algorithm, it is not practical for real-time applications since it requires collecting all data requests beforehand for joint scheduling. Thus, our second one is a sub-optimal online real-time scheduling (ONR) algorithm that performs admission and resource allocation on-the-fly using predictions of upcoming data requests and future availability of spectrum channels. After deriving these two algorithms, we conduct a thorough performance analysis and derive bounds on their objective values compared to the global optimum. We then demonstrate their effectiveness in achieving higher weighted sums of transferred data and prioritizing SUs with big data requests over existing alternatives through extensive numerical comparisons.

This paper develops an efficient channel estimation algorithm based on second-order statistics of time division duplex (TDD) multiuser massive multiple-input multiple-output (MIMO) systems. The algorithm uses the received signal correlation to determine the most significant lags (MSLs) of the received signal. We first employ these MSLs to propose a novel set containing the channel’s four most significant taps (MSTs). Then, by using them, we propose an efficient semi-blind iterative algorithm called enhanced modified-subspace pursuit (EM-SP). It uses the set mentioned above and two theoretical results (Lemma 1 and Theorem 1) to estimate an arbitrary number of MSTs efficiently. Simulation results show that the normalized mean square error (NMSE) of the proposed EM-SP algorithm is much smaller than that of the subspace pursuit (SP) and orthogonal matching pursuit (OMP) algorithms at the cost of 0.3 % and 2 % more computational complexity for the channels with three and six nonzero paths, respectively. Moreover, the NMSE of it is very close to that of the optimal genie-aided least square algorithm.

This paper is concerned with a cognitive cloud radio access network (CRAN) with a special attention to efficient and reliable downlink transmission of big data for secondary users (SUs). Existing approaches either try to maximize the number of accepted SUs or the sum data rate of accepted SUs. The first approach unfairly favors users with small data requests, whereas the second approach allocates most resources to users with better channel conditions. In contrast, this paper develops a novel approach that favors big data requests while simultaneously maintaining a certain degree of fairness among SUs. To this end, we first introduce a novel objective function that allows us to jointly optimize deadline-aware time scheduling, spectrum allocation, SU selection, and remote radio head (RRH) allocation for SUs. Second, we demonstrate that finding the global optimum solution entails the enumeration of all colorful independent sets on a generalized interval graph, which is known to be NP-hard. Third, we propose a dynamic programming (DP) approach, which yields the global optimum solution at a reduced computational cost. Fourth, we analyze the complexity of the proposed DP approach and assess its performance against existing baseline algorithms. Simulation results reveal that our solution favors big data users while incurring only a small degradation in the fairness index. Our proposed solution is practical for small-to-medium size networks. Furthermore, it offers an optimum benchmark for any new sub-optimal low-complexity algorithm.

In this paper, we tackle the problem of optimizing user clustering, power, and resource (time slot or bandwidth) allocation in the downlink of a hybrid non-orthogonal multiple access (NOMA)-orthogonal multiple access (OMA) system. In such a system, users are organized into several clusters under one of the following scenarios: (1) fixed cluster size, (2) fixed number of clusters, and (3) variable number of clusters and variable cluster size. A power domain NOMA (PD-NOMA) scheme is used in each cluster, while OMA is employed for allocating resources to different clusters. The goal is to maximize the minimum success probability (which is equivalent to minimizing the maximum outage probability) among all users to guarantee fairness. We prove that at the optimal solution, all users have the same success probability, which is called the common success probability (CSP). Then, we propose an efficient algorithm for finding the optimal CSP and cluster resource allocation factors simultaneously. The optimal power allocation factors and the optimal decoding order of users in each cluster are then derived in closed-form expressions based on the obtained optimal CSP. Simulation results show considerable performance gains by the proposed scheme, compared to existing schemes in terms of fairness, the minimum success probability of users, and the sum throughput.

In this paper, we investigate joint optimization of power allocation factors (PAFs) and decoding order of users in the downlink of a non-orthogonal multiple access (NOMA) system over Nakagami- $m$ fading channels. The objective is to guarantee fairness among the users in terms of outage probability (or its complement success probability). To this end, we maximize the minimum success probability (Max-MSP) among the users when only statistical channel state information (CSI) is available at the transmitter side. We solve such a problem by first proving that at the optimal solution, all the users have a common success probability (CSP), and then proposing an efficient algorithm for finding CSP of the users. The optimal PAFs and optimal decoding order are derived in closed form based on the CSP and statistical CSI of the users. It is proved that the proposed algorithm always converges to a unique optimal solution. Furthermore, we show that in contrast to Rayleigh fading channels, in Nakagami- $m$ fading channels, the optimal decoding order not only depends on the average signal-to-noise ratio (SNR) of each user but also depends on the variance of the SNR and data rate of that user.

In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU) and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1) interference introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and false alarm probabilities and (3) subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4) minimum Quality of Service (QoS) for each CR subcarrier. We propose one optimal and two suboptimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations.

In this paper، we propose a new algorithm of spectrum sensing based on algorithmic information theory. Since the proposed algorithm is applied in time domain، resulting in an overall reduction on the system computational complexity. In this paper، we investigate two cases of wideband and narrowband spectrum sensing problems. To sense the spectrum، we propose to use some measures based on algorithmic information theory such as Lempel-Ziv Complexity (LZC)، Higuchi fractal dimension (HFD)، and Algorithmic mutual information (algorithmic MI). LZC and HFD are reliable and promising measures that calculate the complexity of a time series signal in a straight forward manner. On the other hand، algorithmic MI calculates the algorithmic mutual information between two time series signals. Our proposed algorithm is blind in the sense that it requires no prior knowledge of the channel، primary users’ signals، and noise variance. In simulation section، it is shown that our proposed algorithm has better performance in contrast with the other complexity based detectors such as Shannon entropy and spectral entropy based detectors.