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In this paper, we consider the channel allocation problem for throughput maximization in cognitive radio networks with hardware-constrained secondary users. Specifically, we assume that secondary users (SUs) exploit spectrum holes on a set of channels where each SU can use at most one available channel for communication. We present the optimal brute-force search algorithm and its complexity for this non-linear integer optimization problem. Since the optimal solution has exponential complexity with the numbers of channels and SUs, we develop two low-complexity channel assignment algorithms that can efficiently utilize spectrum opportunities on these channels. In the first algorithm, SUs are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm, that can improve the throughput performance compared to the nonoverlapping channel assignment counterpart. In addition, we design a distributed MAC protocol for access contention resolution and integrate it into the overlapping channel assignment algorithm. We also analyze the saturation throughput and the complexity of the proposed channel assignment algorithms. Moreover, we have presented several potential extensions including greedy channel assignment algorithms under max-min fairness criterion and throughput analysis considering sensing errors. Finally, numerical results are presented to validate the developed theoretical results and illustrate the performance gains due to the proposed channel assignment algorithms.

In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrumsensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.

Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.

In this paper, we consider the joint design of data
compression and 802.15.4-based medium access control (MAC)
protocol for smartgrids with renewable energy. We study the setting
where a number of nodes, each of which comprises electricity
load and/or renewable sources, report periodically their injected
powers to a data concentrator. Our design exploits the correlation
of the reported data in both time and space to perform efficient
data compression using the compressed sensing (CS) technique
and efficiently engineer the MAC protocol so that the reported
data can be recovered reliably within minimum reporting time.
Specifically, we perform the following design tasks: i) we employ
the two-dimensional (2D) CS technique to compress the reported
data in the distributed manner; ii) we propose to adapt the
802.15.4 MAC protocol frame structure to enable efficient data
transmission and reliable data reconstruction; and iii) we develop
an analytical model based on which we can obtain the optimal
parameter configuration to minimize the reporting delay. Finally,
numerical results are presented to demonstrate the effectiveness
of our design.

The ever growing demand of Internet of Things (IoT) imposes great challenges in the existing cellular systems and calls for novel approaches for wireless network design. In this paper, we develop a joint energy and computation optimization paradigm in an IoT network. The tasks collected at local IoT devices can be computed at hierarchical mobile edge computing facilities. Both non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) are used for computation offloading. The system model considers both long-term and short-term system behaviors and makes the best decision for energy consumption and computation efficiency. The Long Short Term Memory (LSTM) network is applied to predict the long-term workload, based on which the number of active process units in the edge layer is optimized. In the short-term model, resource optimization problem is formulated. Due to the dynamic arrival workload and non-convex features of the problem, the Lyapunov optimization approach and SCALE method are applied to solve this problem. Simulation results show that our proposed scheme can significantly improve the delay and energy consumption performance.