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Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals.

Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

In this paper, we study the handover (HO), and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station, and some millimeter-wave (mmWave) small base stations. We establish an HO management, and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO, and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task, and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after the training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works.

In this paper, we study the resource allocation problem for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long-packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and an unavoidable decoding error rate. Thus conventional resource allocation based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we propose to use the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Building on that, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to transmission reliability and various practical constraints. Since the formulated problem belongs to a non-convex mixed integer nonlinear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. We further propose a low-complexity algorithm based on the principles of block coordinate descent (BCD) and concave-convex procedure (CCCP). Finally, the simulation results show that the proposed low-complexity algorithm can achieve similar performance as that of the optimal solution and outperform the other baseline schemes significantly.

We investigate the interference-aware spectrum reuse for heterogeneous small cell networks (SCNs), by specially considering dense-user deployment and stochastic-environment uncertainties. Most existing approaches, which are lack of evolving coordinations and rely on precise channel state information, tend to be inefficient in the context of dense SCNs with uncertainties. To improve the performance, by introducing a reliable metric of successful transmission probability to characterize the individual utility, we adopt a correlated equilibrium (CE)-based game to formulate spectrum reuse, and propose a distributed regret-matching learning algorithm to achieve the CE solutions. Eliminating the dependence on definite information and with general CE points consideration, our new scheme is feasible under the varying environment and can obtain more promising solutions than the state-of-art reinforcement learning methods by encouraging players to coordinate their strategies. Numerical simulations demonstrate the advantages of our proposed scheme.

Recently, classifying the modulation schemes of signals using deep neural network has received much attention. In this paper, we introduce a general model of deep neural network (DNN)-based modulation classifiers for single-input single-output (SISO) systems. Its feasibility is analyzed using maximum a posteriori probability (MAP) criterion and its robustness to uncertain noise conditions is compared to that of the conventional maximum likelihood (ML)-based classifiers. To reduce the design and training cost of DNN classifiers, a simple but effective pre-processing method is introduced and adopted. Furthermore, featuring multiple recurrent neural network (RNN) layers, the DNN modulation classifier is realized. Simulation results show that the proposed RNN-based classifier is robust to the uncertain noise conditions, and the performance of it approaches to that of the ideal ML classifier with perfect channel and noise information. Moreover, with a much lower complexity, it outperforms the existing ML-based classifiers, specifically, expectation maximization (EM) and expectation conditional maximization (ECM) classifiers which iteratively estimate channel and noise parameters. In addition, the proposed classifier is shown to be invariant to the signal distortion such as frequency offset. Furthermore, the adopted pre-processing method is shown to accelerate the training process of our proposed classifier, thus reducing the training cost. Lastly, the computational complexity of our proposed classifier is analyzed and compared to other traditional ones, which further demonstrates its overall advantage.

The state-of-the-art goodness-of-fit (GoF) test algorithms for spectrum sensing directly make decisions based on temporal samples or energies of the observations, which perform well under the condition that the received primary user (PU) signals are independent. However, when the received PU signals are highly correlated, these methods cannot achieve satisfactory performance. In this case, the correlation feature is the main character of the signals and the maximum eigenvalue of the sample covariance matrix is a versatile statistic reflecting the correlation feature. Motivated by this, we make full use of the correlation feature involved in the eigenvalues to improve the GoF detection performance. Specifically, we first study the GoF test in eigenvalue domain and design a semi-blind maximum eigenvalue-based GoF detection scheme using the ratio of the maximum eigenvalue to the noise power. Considering that the accurate knowledge of the noise power is not always available in practice, we next design a totally blind maximum eigenvalue-based GoF detection method, which only uses the ratio of the maximum eigenvalue to the trace. Utilizing the recent results of random matrix theory, we provide a theoretical analysis of the proposed methods, including the probabilities of false alarm, detection thresholds, and probabilities of detection. Finally, simulation results show that the proposed algorithms outperform the related GoF detection methods in terms of the detection performance.

As the era of Internet of Things is approaching, huge number of devices need to be included in the network, and the long standby time of devices should also be satisfied. To solve these problems, in this paper, a relay-aided multiple access (RAMA) transmission scheme based on network coding is considered in joint transmission with two base stations (BSs). In the scheme, the near user acts as a two-way relay node between the BS and the far user based on machine-to-machine communications for both downlink and uplink transmissions. At first for two users, non-orthogonal multiple access (NOMA) is employed to compare with the proposed RAMA scheme in terms of the achievable sum rates. From the analysis, we can show that for NOMA and RAMA, one outperforms the other under different conditions. Then, we consider the case of multiple user pairs, for which an adaptive RAMA-NOMA transmission scenario is developed to make full use of their respective advantages. In the scenario, an optimal power allocation scheme based on the branch and bound (BB) algorithm and a low-complexity user pairing scheme based on a greedy algorithm are proposed. Numerical results show that our proposed adaptive RAMA-NOMA transmission scenario can achieve a higher sum rate compared with RAMA or NOMA only scheme, and under the optimal power allocation based on BB, the performance of our proposed user pairing scheme is close to that of the optimal pairing method using an exhaustive search, and significantly outperforms the random selection method.

As compared to the microwave communication networks, the theoretical analyses of system performances such as the cell coverage and the cell average data rate are more difficult due to the unique propagation path loss model for mmWave cellular communication networks. In this paper, based on the unique transmission states of the distance-dependent piecewise propagation probability functions, the analytical six-state analytical mixture distribution of the propagation loss including the distance-dependent path loss, shadowing and small-scale fading is obtained. For each state, a novel Kullback–Leibler divergence based Gaussian approximation method is proposed to model the distribution of the propagation loss. The distribution of the propagation loss including the large-scale fading and small-scale fading is approximated via a six-state Gaussian mixture model, then the closed-form expression for the signal-to-noise ratio can be easily obtained. The cell coverage is expressed as the weighted sum of the error functions, and the cell average data rate is expressed as the weighted sum of the moments of the propagation loss for the six different states. Numerical results show that the cell coverage mainly depends on the none line-of-sight transmission and the cell average data rate mainly depends on the line-of-sight transmission.

Internet of Things (IoT) is a promising paradigm to provide massive wireless connections in the future communications. One main feature of IoT is short packet communication (SPC), which adopts finite block-length codewords for data transmissions. Different from the long packet transmission (i.e., infinite block-length codewords) in conventional wireless networks, SPC suffers from a significant packet error rate even when the transmission rate is smaller than the Shannon capacity. To enhance the transmission efficiency as well as the reception reliability, we introduce cooperative relaying to the IoT network and propose a cooperative IoT protocol. Then, we analyze the average throughput of the cooperative IoT protocol with an approximated closed-form expression. Based on the closed-form expression, we design both optimal and suboptimal transmission rates to maximize the average throughput. Numerical and simulation results have validated the correctness of the theoretical analysis, and show that the maximum average throughput with the optimal/suboptimal design almost overlaps with the simulation results. Besides, we compare the proposed protocol with the automatic repeat request (ARQ) mechanism in terms of both the optimal transmission rate and the maximum average throughput, and observe that the proposed protocol outperforms the ARQ mechanism, especially for a medium/large number of cooperative users.

Ambient backscatter (AmBack) communication, also called “modulation in the air,” has drawn growing interest by both academia and industry recently. In this paper, we investigate and analyze an AmBack system, where the user is equipped with an AmBack circuit at the transmitter and maximum ratio combing at the receiver. By harvesting power from the surrounding radio frequency source, the circuit operation and the signal backscattering can be supported. However, if there is not enough harvested power for the circuit operation, the signal backscattering will be suspended. Different from previous works on AmBack, an adaptive scheme is proposed to opportunistically exploit the residual or full user battery power for insufficient harvested power so that the signal backscattering is always available. However, there is no exact closed-form expression for the outage probability, and its approximation is obtained to facilitate our analysis. For comparison, the traditional non-adaptive scheme is also analyzed, and a closed-form expression for the outage probability is derived. In order to get more insight into both schemes, asymptotic outage performance is also derived when the number of receiver antennas/the noise power is sufficiently large/low. Simulation results demonstrate the tightness and the correctness of the derived outage probabilities, and show that the proposed adaptive scheme can significantly outperform the traditional non-adaptive scheme.

Prior works on performance analysis for cognitive radio networks often assume perfect channel state information (CSI), which may not be readily available. This paper investigates the effects of the outdated CSI on the performance of a dual-hop multi-antenna multi-relay spectrum sharing system. To exploit the benefits of available multiple antennas, collaborative zero-forcing beamforming is considered at the secondary user (SU) source node with the outdated CSI and maximum ratio combining diversity reception at the destination node. The $N$ th best relay selection strategy is adopted at the relays. Analytical outage probability and bit-error rate expressions are derived for the SU. Moreover, we examine the high signal-to-noise ratio regime and present closed-form expressions for the asymptotic outage probability and asymptotic bit-error rate with and without feedback delay. In addition, we derive the closed-form ergodic capacity expression for the special case of our considered system, and analyze the effects of delay on the capacity. Furthermore, the closed-form outage probability expression for the primary user is obtained and the impact of delay on the interference on the primary user is investigated. Monte Carlo simulations are carried out to verify our analysis.

Cooperative communication is a promising technique to mitigate channel impairment and improve spectral efficiency. Due to the mobility of relay nodes (RNs) and the unfavourable effects of wireless channels, channel conditions may change as time passes. Therefore, how to design proper long-term incentive mechanisms for RNs in such dynamic communication environments is an essential issue. This paper investigates the contract incentive method under asymmetric information scenario, where the relay channel condition and cost are the RNs’ private information. To capture the dynamic characteristic of the RNs’ cooperative information during the long-term cooperation, two dynamic contract mechanisms are introduced into long-term relay incentive with different RNs’ relay information structures. The optimal contract is derived to maximize the source's expected utility for both independent asymmetric information and correlated asymmetric information . Numerical results are presented to evaluate the effectiveness of the proposed dynamic contract-based incentive mechanism.

In this paper, we propose a novel receiver design for orthogonal frequency division multiplexing (OFDM) transmission over time-varying channels, where the receiver employs a turbo message passing (TMP) framework for a joint channel estimation and the data detection. We show that the proposed TMP-based receiver is able to make more efficient use of the prior information about the channel and the data for intercarrier interference cancellation than the existing iterative receivers. The proposed TMP algorithm can be further combined with a soft-input soft-output decoder for the joint channel estimation, data detection, and decoding. Numerical results are presented to demonstrate the performance superiority of our proposed TMP-based OFDM receiver.

Short packet communications (SPC) will play an important role in future Internet-of-Things networks. Conventional pilot-assisted transmission (PAT) needs significant overhead to obtain accurate channel state information (CSI) for further symbol detection and bit recovery, thereby reducing the spectral efficiency of the transmission. In this paper, a machine learning framework called Label-Assisted Transmission is proposed, in which the received signals are grouped into clusters through clustering algorithms and known labels are transmitted for cluster-symbol/bits mapping. This novel framework supports bit recovery directly without requiring the bit-symbol mapping information. When such mapping information is available, modulation constrained (MC) clustering algorithms are proposed, which exploit the unique characteristics of digital communication signals. For frequency-flat channels, this novel design needs only one known label regardless of the modulation size and the missing labels can be reconstructed using a proposed label reconstruction scheme. For frequency-selective channels with $L$ -tap time domain channel responses, only $L$ known labels are needed to reconstruct the missing labels if orthogonal frequency division multiplexing technology is adopted. The proposed clustering receiver works well even when the number of clusters is much larger than the number of received samples. The performance of the proposed framework is analyzed empirically through extensive simulations, which verify that the proposed scheme approaches the maximum likelihood detector with perfect CSI.

Dynamic spectrum sharing is considered as one of the key features in the next-generation communications. In this correspondence, we investigate the dynamic tradeoff between the sensing performance and the achievable throughput, in the presence of time-varying fading (TVF) channels. We first establish a unified dynamic state-space model (DSM) to characterize the involved dynamic behaviors, where the occupancy states of primary user (PU) and the fading channel gains are modeled as two Markov chains. On this basis, a promising dynamic sensing schedule framework is proposed, whereby the sensing duration is adaptively adjusted based on the estimated real-time TVF channel. We formulate the sensing-throughput tradeoff problem mathematically, and further show that there exists the optimal sensing duration maximizing the throughput for the secondary user (SU), which will change dynamically with channel gains. Relying on our designed recursive sensing paradigm that is able to blindly acquire varying channel gains as well as the PU states, the sensing duration can be then adjusted in line with the evolving channel gains. Numerical simulations are provided to validate our dynamic sensing schedule algorithm, which can significantly improve the SU's throughput by reconfiguring the sensing duration according to dynamic channel conditions.

© 1967-2012 IEEE. As a response to the challenge of data traffic explosion in wireless networks, content caching in device-to-device (D2D) communication networks has emerged as a promising solution. However, in practical deployment, D2D content caching has its own problems. In particular, not all of the user devices are willing to share the content with others due to numerous concerns, such as security, battery life, and social relationship. In this paper, we consider the factor of social relationship in the deployment of D2D content caching. First, we apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in our analysis to obtain the average download delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, we develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance but also takes into account the social relationship between D2D users. Our simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, our work sheds insights on the design of D2D caching in the practical deployment of 5G networks.

Wideband spectrum sensing is a highly desirable feature in cognitive radio systems when the aim is to increase the probability of exploring spectral opportunities. Sub-Nyquist sampling has attracted significant interest for wideband spectrum sensing, while existing algorithms can only work with a sparse spectrum. In this paper, we propose a sub-Nyquist wideband spectrum sensing algorithm that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters (ADCs) based on sparse fast Fourier transform. To lower signal spectrum sparsity while maintaining the channel state information, we preprocess the received signal through a proposed permutation and filtering algorithm. The proposed wideband spectrum sensing algorithm subsamples the time-domain signal and then directly estimates its frequency spectrum. We derive and verify the proposed algorithm by numerical analysis and test it on real-world TV white space signals. The results show that the proposed algorithm achieves high detection performance on sparse and nonsparse wideband signals with reduced runtime and implementation complexity in comparison with the conventional wideband spectrum sensing algorithms.

The fifth generation and future wireless networks are expected to support massive machine-to-machine (M2M) communications. Due to the sporadic nature, massive M2M communications can be well supported by low-activity code division multiple access (LA-CDMA). In the literature, maximum a posteriori detector has been proposed to detect the active users in LA-CDMA when the user activity factor is known and small. However, such user activity factor is usually unknown and could be large in practice, which makes the multiuser detection for LA-CDMA a challenging task. In this paper, we first formulate the LA-CDMA uplink using single measurement vector (SMV) model and multiple measurement vector (MMV) model, then, propose novel Bayesian inference algorithms to recover the transmitted signals. For SMV model, we first introduce sparse Bayesian learning (SBL) that exploits the sparsity of the transmitted signals, then, add on the known finite-alphabet constraints and introduce Gaussian mixture model method to recover the transmitted signals. For MMV model, pattern coupled SBL (PCSBL) algorithm is introduced that takes into consideration the neighbor coherence of each device, then the block SBL is introduced through exploiting the row sparsity property and the column coherence of each device. The four Bayesian inference methods make use of various priors and hyperparameters, which can be autonomously learned through the training process via expectation maximization (EM) or variational EM iterative algorithms. Furthermore, the proposed Bayesian methods do not require the knowledge of user activity factor. Simulation results have shown that the proposed Bayesian inference methods outperform the classical algorithms.

With the ever-growing number of mobile users and the rapid growth of wireless data service requirement, quality of experience (QoE) has emerged as an essential indicator for users, service providers, and operators. Meanwhile, to improve coverage and serve users, a lot of small cell base stations (SBSs) must be installed, and a great amount of energy is consumed. However, as far as is known, there are few works that have studied the combinatorial problem of QoE and energy aware SBS management, which jointly implements power selection, load management (SU allocation), and channel allocation. This paper investigates the problem of QoE and energy aware SBS management, which consists of power selection, load management, and channel allocation. In this paper, we resort to cloud technologies to solve such a complicated combinatorial problem and employ an iterative approach in which two subproblems are alternatively assigned and optimized at each iteration, i.e., 1) transmission power and load joint management and 2) channel allocation. We propose a two-dimensional-action extended weakly acyclic game theoretical scheme to optimize the two subproblems distributedly and iteratively. We define a novel two-dimensional-action pure strategy Nash equilibrium (2D-NE) and prove that at least one 2D-NE exists in the proposed game. With the help of cloud, we propose two kinds of better response algorithms to achieve 2D-NE of the proposed game $G_w$ . Moreover, simulation results show that the proposed approach could achieve a good QoE-energy utility performance and a high QoE energy efficiency.

In cognitive radio networks, a secondary user (SU) equipped with radio frequency (RF) energy-harvesting circuits can not only harvest the RF energy from the primary transmitter, but also recycle its self-power when transmitting. In this paper, we are concerned with the following design metrics: SU's harvested energy, SU's energy efficiency, and SU's harvesting efficiency, which is defined as the ratio of the average energy harvested by SU over its average energy consumption. We are interested in two tradeoff designs: one is the tradeoff between energy efficiency and harvested energy and the other is the tradeoff between energy efficiency and harvesting efficiency. Multiobjective optimization is used to solve the tradeoff problems. To simplify the original problems, we propose two schemes to obtain the lower bounds of the objective functions. The sensing threshold, sensing time, and transmit power of SU are jointly optimized to solve the tradeoff problems. Efficient algorithms are proposed to derive these design parameters. Simulation results are presented to validate the effectiveness of the proposed algorithms, to show the two tradeoff designs, and to validate the effects of system parameters on these tradeoffs.

Channel rendezvous is a vital step to form a cognitive radio network (CRN). It is intractable to guarantee rendezvous for secondary users (SUs) within a short finite time in asynchronous, heterogeneous, and anonymous CRNs. However, most previous heterogeneous algorithms rely on explicit SUs' identifiers (IDs) to guide rendezvous, which is not fully distributed. In this paper, we exploit the mathematical construction of sunflower sets to develop a single-radio sunflower set (SSS)-based pairwise rendezvous algorithm. We propose an approximation algorithm to construct disjoint sunflower sets. Then, the SSS leverages the variant permutations of elements in sunflower sets to adjust the order of accessing channels instead of SUs' IDs, which is more favorable for anonymous SUs in distributed environments. We also propose a multiradio-sunflower-set-based pairwise rendezvous algorithm to bring additional rendezvous diversity and accelerate the rendezvous process. Moreover, for the case with more than two SUs, we propose a multiuser collaborative scheme in which SUs cooperatively exchange and update their channel-hopping sequences until rendezvous. We derive the theoretical upper and lower bounds of rendezvous latency of the proposed algorithms. Extensive simulation comparisons with the state-of-the-art blind-rendezvous algorithms are conducted, incorporating the metrics of maximum and expected time-to-rendezvous. The simulation results show that our algorithms can achieve rendezvous faster than previous works.

Existing cooperative spectrum-sharing protocols either separate the primary and the secondary transmissions or allow the secondary user (SU) to transmit only in the time slot when it is relaying the primary user's signal. In this paper, we propose a new cooperative spectrum-sharing protocol that multiplexes the secondary transmissions with both the primary and the relay transmissions. This way, a pair of SUs can bidirectionally communicate with each other while one of them is serving as a relay to assist in the transmission from the primary transmitter to the primary receiver. Specifically, the transmission is on a two-time-slot basis. In the first time slot, in addition to the primary transmitter's transmission, the SU who does not act as the relay also transmits. In the second time slot, the SU who acts as the relay will split its power to relay the primary signal to the primary receiver while transmitting its own signal to the other SU. In particular, it is considered that the relay SU is equipped with multiple antennas, while all the other terminals have a single antenna each. Based on the proposed protocol, we consider two decode-and-forward (DF) schemes and one amplify-and-forward (AF) scheme. We evaluate the achievable rate regions of the rate of the primary link versus the sum rate of the secondary links of the proposed schemes. The Pareto boundary of the achievable rate region is found by optimizing the transmit power, the beamforming vectors, and the power splitting factor at different nodes to maximize the sum rate of the secondary links for a given rate of the primary link. It is demonstrated through numerical results that our proposed bidirectional protocol allows the SUs to utilize the spectrum in a more efficient way without sacrificing the primary link's rate, as compared with the existing one-directional scheme in the literature.

In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with minimum eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for eigenvalue-based detectors such as maximum eigenvalue detection (MED) and maximum-minimum eigenvalue (MME) detection. Finally, the throughput is maximized using eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust eigenvalue-based techniques, which are insusceptible to noise uncertainty.

In this paper, we consider medium access control (MAC) protocol design for random-access cognitive radio (CR) networks. A two-level opportunistic spectrum access strategy is proposed to optimize the system performance of the secondary network and to adequately protect the operation of the primary network. At the first level, secondary users (SUs) maintain a sufficient detection probability to avoid interference with primary users (PUs), and the spectrum sensing time is optimized to control the total traffic rate of the secondary network allowed for random access when the channel is detected to be available. At the second level, two MAC protocols called the slotted cognitive radio ALOHA (CR-ALOHA) and cognitive-radio-based carrier-sensing multiple access (CR-CSMA) are developed to deal with the packet scheduling of the secondary network. We employ normalized throughput and average packet delay as the network metrics and derive closed-form expressions to evaluate the performance of the secondary network for our proposed protocols. Moreover, we use the interference and agility factors as the performance parameters to measure the protection effects on the primary network. For various frame lengths and numbers of SUs, the optimal performance of throughput and delay can be achieved at the same spectrum sensing time, and there also exists a tradeoff between the achievable performance of the secondary network and the effects of protection on the primary network. Simulation results show that the CR-CSMA protocol outperforms the slotted CR-ALOHA protocol and that the PUs' activities have an influence on the performance of SUs for both the slotted CR-ALOHA and CR-CSMA.

This paper studies the problem of robust downlink beamforming design in a multiuser multiple-input-single-output (MISO) cognitive radio network (CR-Net) in which multiple secondary users (SUs) coexist with multiple primary users (PUs) of a single-cell primary radio network (PR-Net). It is assumed that the channel-state information (CSI) for all relevant channels is imperfectly known, and the imperfectness of the CSI is modeled using a Euclidean ball-shaped uncertainty set. Our design objective is to minimize the transmit power of the SU-Transmitter (SU-Tx) while simultaneously achieving a lower bound on the received signal-to-interference-plus-noise ratio (SINR) for the SUs and imposing an upper limit on the interference power (IP) at the PUs. The design parameters at the SU-Tx are the beamforming weights, and the proposed methodology to solve the problem is based on the worst-case design scenario through which the performance metrics of the design are immune to variations in the channels. The original problem is a separable homogeneous quadratically constrained quadratic problem (QCQP), which is an NP-hard problem, even for uncertain CSI. We reformulate our original design problem to a relaxed semidefinite program (SDP) and then investigate three different approaches based on convex programming. Finally, simulation results are provided to validate the robustness of the proposed methods.

This paper addresses the design issues of the multiantenna-based cognitive radio (CR) system that is able to concurrently operate with the licensed primary-radio (PR) system. We propose a practical CR transmission strategy consisting of three major stages, namely, environment learning, channel training, and data transmission. In the environment-learning stage, the CR transceivers both listen to the PR transmission and apply blind algorithms to estimate the spaces that are orthogonal to the channels from the PR. Assuming time-division duplex (TDD)-based transmission for the PR, cognitive beamforming is then designed and applied at CR transceivers to restrict the interference to/from the PR during the subsequent channel-training and data-transmission stages. In the channel-training stage, the CR transmitter sends training signals to the CR receiver, which applies the linear-minimum-mean-square-error (LMMSE)-based estimator to estimate the effective channel. Considering imperfect estimations in both learning and training stages, we derive a lower bound on the ergodic capacity that is achievable for the CR in the data-transmission stage. From this capacity lower bound, we observe a general learning/training/throughput tradeoff associated with the proposed scheme, pertinent to transmit power allocation between the training and transmission stages, as well as time allocation among the learning, training, and transmission stages. We characterize the aforementioned tradeoff by optimizing the associated power and time allocation to maximize the CR ergodic capacity.

In cognitive radio networks, the performance of the spectrum sensing depends on the sensing time and the fusion scheme that are used when cooperative sensing is applied. In this paper, we consider the case where the secondary users cooperatively sense a channel using the k -out-of-N fusion rule to determine the presence of the primary user. A sensing-throughput tradeoff problem under a cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection that is provided to the primary user. An iterative algorithm is proposed to obtain the optimal values for these two parameters. Computer simulations show that significant improvement in the throughput of the secondary users is achieved when the parameters for the fusion scheme and the sensing time are jointly optimized.

Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of the received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum-sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and the associated threshold are found based on the statistical theory. The methods do not need any information about the signal, channel, and noise power a priori. In addition, no synchronization is needed. Simulations based on narrow-band signals, captured digital television (DTV) signals, and multiple antenna signals are presented to verify the methods.

With the proliferation of wireless services, personal connectivity is quickly becoming ubiquitous. As the user population demands greater multimedia interactivity, data rate requirements are set to soar. Future wireless systems, e.g., multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), need to cater to not only a burgeoning subscriber pool but also to a higher throughput per user. Furthermore, resource allocation for multiuser MIMO-OFDM systems is vital for the optimization of the subcarrier and power allocations to improve overall system performance. Using convex optimization techniques, this paper proposes an efficient solution to minimize the total transmit power subject to each user's data rate requirement. Using a Lagrangian dual decomposition, the complexity is reduced from one that is exponential in the number of subcarriers M to one that is only linear in M. To keep the complexity low, linear beamforming is incorporated at both the transmitter and the receiver. Although frequency-flat fading has been known to plague OFDM resource allocation systems, a modification, i.e., dual proportional fairness, seamlessly handles flat or partially frequency-selective fading. Due to the nonconvexity of the optimization problem, the proposed solution is not guaranteed to be optimal. However, for a realistic number of subcarriers, the duality gap is practically zero, and optimal resource allocation can be evaluated efficiently. Simulation results show large performance gains over a fixed subcarrier allocation.

In a cognitive radio network, the spectrum that is allocated to primary users can be used by secondary users if the spectrum is not being used by the primary user at the current time and location. The only consideration is that the secondary users have to vacate the channel within a certain amount of time whenever the primary user becomes active. Thus, the cognitive radio faces the difficult challenge of detecting (sensing) the presence of the primary user, particularly in a low signal-to-noise ratio region, since the signal of the primary user might be severely attenuated due to multipath and shadowing before reaching the secondary user. In this paper, a blind sensing algorithm is derived, which is based on oversampling the received signal or by employing multiple receive antennas. The proposed method combines linear prediction and QR decomposition of the received signal matrix. Then, two signal statistics are computed from the oversampled received signal. The ratio of these two statistics is an indicator of the presence/absence of the primary signal in the received signal. Our algorithm does not require the knowledge of the signal or of the noise power. Moreover, the proposed detection algorithm in this paper is blind in the sense that it does not require information about the multipath channel distortions the primary signal has undergone on its way to reaching the secondary user. Simulations have shown that our algorithm performs much better than the commonly used energy detector, which usually suffers from the noise uncertainty problem.

In time division synchronous code division multiple access downlink, one computationally efficient receiver for fast time-varying environments is the subblock processing receiver, which utilizes overlap-save fast Fourier transform. In this paper, we first analyze the interferences involved with the subblock processing method proposed by Held and Kerroum and then propose a new subblock processing receiver for fast time-varying channels. The proposed receiver consists of two stages. In the first stage, the entire received chip block is partitioned into overlapping subblocks and they are individually equalized and despread. We then artificially generate the interferences caused by adjacent blocks and the unwanted chip interference within the same subblock and eliminate them from the received data signals. Then, a second subblock processing is performed to detect the transmitted symbols. A practical channel estimator is also introduced to be used with the proposed receiver. Simulation results have shown that the proposed receiver provides a significant performance improvement as compared with the conventional subblock processing method.

Shortened turbo product codes (TPCs) have already been adopted in many standards. In this paper, we study shortened TPCs from two aspects, namely encoding design and decoding algorithm. To obtain different encoding block sizes, shortened-extended Hamming codes are used as the component codes of product codes in the IEEE 802.16 standard. To design a good structure for the shortened TPC, we compute the undetected error probability of its corresponding component codes. The component codes are shortened-extended Hamming codes, and their optimal generator polynomials are selected in terms of their undetected error probability. For the decoding algorithm, we present an efficient Chase decoding algorithm for shortened TPCs in flat fading channels. In the proposed scheme, the reliability factor used in Pyndiah's scheme is not needed; thus, the decoding complexity is greatly reduced by avoiding the normalization operation of the whole code word at each iteration. Simulation results are also presented to verify the performance of the proposed algorithm.

This paper addresses the problem on blindly estimating the channel impulse response (CIR) and the carrier frequency offset (CFO) in the uplink transmissions of multiuser code division multiple access system. Two blind subspace-based CIR and CFO estimation methods, namely, the exact determinant minimization method and the approximate determinant minimization method, are proposed. The performance of the proposed methods is compared with the available solution based on the generalized eigenvalue problem method (GEVPM). The computational complexity of the proposed methods is compared with that of GEVPM and the estimator proposed by Li and Liu. Simulation results are given to show that the proposed methods give better performance than the GEVPM and have a wide CFO acquisition range.

The incorporation of the orthogonal frequency-(multiplexing (OFDM) with the space-time or space-frequency coding is a robust and effective method to achieve a transmit-diversity gain and to suppress an intersymbol interference for the broadband wireless transmissions. This paper is concerned with the downlink-performance improvement using multiple transmit antennas for the OFDM systems with a large delay spread. A delay-spread reduction method using space-frequency block coding and statistical prefiltering (SPF) was proposed, which combines signal prealignment and multiple transmit beamformers designed with the statistical knowledge of the downlink channel state information. The proposed method, which is called the SPF-OFDM, transforms a large delay-spread channel into multiple channels, each with small delay spread. Thus, it does not only shorten the effective excess delay but also preserves the path diversity through the use of the space-frequency coding. Computer simulations have evaluated the effectiveness of the proposed scheme, and comparisons have been made with the conventional solutions

The authors present a chip-level blind frequency domain equalizer (FEQ) for the forward-link channel of a cyclic-prefix code division multiple access system. The FEQ coefficients are obtained without the need of training symbols or knowledge of channel state information. The coefficients are instead acquired by solving a constraint energy minimization problem involving the subspace spanned by the active and passive spreading codewords. They also prove that the random scrambling code sequences is required for the operation of the proposed equalization algorithm. Results from computer simulations are provided to verify the performance of the proposed FEQ