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We propose a wideband spectrum sensing technique to detect and localize wireless radio frequency (RF) signals of interest in time and frequency when uninteresting signals cause RF interference (RFI). Specifically, we adopt and downscale the existing Faster-RCNN (FRCNN) framework to achieve better signal detection and localization than the state-of-the-art. For experimental evaluation, we present a data generation framework for Wi-Fi as the signals of interest and the Bluetooth and microwave oven signals as the RFI. Experiments reveal that (i) the downscaled FRCNN model can achieve up to a mean average precision (mAP) of 0.8, significantly outperforming the state-of-the-art, (ii) feature extraction with the VGG-13 architecture gives the best mAP with pretrained weights and configured as trainable, (iii) for signal detection in real RF traces, when compared to training purely with synthetic RF data, a better mAP can be achieved by training with a mixture of synthetic and real RF traces or by finetuning the synthetically-trained weights with an additional round of training on a small amount of real RF traces, and (iv) the mAP performance decreases as the signal to noise ratio (SNR) is lowered.

Hybrid precoding, a combination of digital and analog precoding, is an alternative to traditional precoding methods in massive MIMO systems with a large number of antenna elements and has shown promising results recently. In this paper, we implement a parallel framework to make hybrid precoding competitive in fast-fading environments. A low-complexity algorithm which exploits the block diagonal phase–only nature of the analog precoder in a partially connected structure is proposed to arrive at a hybrid precoding solution for a multi-carrier single-user system using orthogonal frequency division multiplexing (OFDM). The original problem is broken down into two subproblems of finding the magnitude and the phase components which are solved independently. A per-RF chain power constraint is introduced instead of the sum power constraint over all antennas which are much more practical in real systems. An alternating version of the same algorithm is proposed for increased spectral-efficiency gains. Complexity and run-time analysis demonstrate the advantage of the proposed algorithm over existing hybrid precoding schemes for partially connected structure in an OFDM setting. The simulation results reveal certain insights about the partially connected structure and the tradeoffs that have to be made to make it workable in a real wideband system.

We propose a supervised machine learning (ML) approach based on Gaussian process (GP) regression to position users in a distributed massive multiple-input multiple-output (DM-MIMO) system from their uplink received signal strength (RSS). The proposed approach serves as a proof-of-concept that we can localize users by training an ML model with noise-free RSS and using the trained model to estimate the test user locations from their noisy RSS. We consider two GP methods for localization, namely, the conventional GP (CGP) and the numerical approximation GP (NaGP). We find that the CGP provides unrealistically small $2\sigma $ error-bars on the location estimates. Therefore, we derive the true predictive distribution and employ NaGP to obtain realistic $2\sigma $ error-bars on the location estimates. Next, we derive a Bayesian Cramer–Rao lower bound (BCRLB) on the root-mean-squared-error (RMSE) performance of the two GP methods. Numerical studies reveal that: 1) the NaGP indeed provides realistic $2\sigma $ error-bars on the estimated locations; 2) both the CGP and NaGP achieve RMSEs that are close to the BCRLBs; 3) the presence of correlated shadowing improves the RMSE performance; and 4) extrapolation to the zero input noise scenario can significantly improve the RMSE achieved by the NaGP.

We jointly optimize resource allocation with the dynamic activation of energy harvesting base stations in a two-tier orthogonal frequency-division multiple access-based heterogeneous network. We consider both energy harvesting constraints and interference constraints along with time-variation in channel condition, user activity, and energy arrival. We optimize the trade-off between throughput performance of the small cell (or hotspot) users and the associated power cost by maximizing the net reward, where positive reward is associated with achievable throughput of the hotspot users and negative reward with the corresponding non-renewable power consumption. Quality-of-service requirements of hotspot users as well as macrocell users are considered in the optimization problem. Assuming the availability of non-causal information, we propose offline resource allocation algorithm using discrete binary particle swarm optimization and dual decomposition technique. Assuming the availability of statistical information of future values, we propose dynamic programming-based online algorithm. Finally, we propose simple and greedy online algorithm assuming lack of any kind of future information. Numerical results demonstrate the performances of the proposed offline, dynamic programming-based online, and greedy online algorithms and highlight the scenarios, where the performance of the proposed algorithms is significantly better than the baseline schemes.

We study the downlink resource allocation problem for simultaneous wireless information and power transfer (SWIPT) in small cells underlaying a macrocell in a two-tier heterogeneous network. For SWIPT, we consider both time-switching and power-splitting approaches. We determine downlink transmit power of small cell base stations along with time-switching/power-splitting variables for SWIPT to jointly optimize energy harvesting rate and achievable throughput of small cell users while ensuring minimum throughput of macrocell user. In the time-switching approach, the resource allocation problem is formulated as a mixed-integer non-linear programming (MINLP) problem. The formulated MINLP problem is solved by relaxing the binary integer constraint and then identifying the condition at which the obtained solution satisfies that constraint. In both the time-switching and power-splitting approaches, in the presence of non-negligible co-tier interference, the formulated problem is solved sub-optimally by iteratively maximizing the minorant of the non-convex objective function. A special case with negligible co-tier interference is considered in the time-switching approach and the optimal solution is obtained by using convex optimization techniques. Numerical results demonstrate significant gain in energy harvesting rate when macrocell users have flexible interference tolerance levels in time-switching approach, highlight the improvement in energy harvesting rate in the presence of co-tier interference signal, and reveal interesting trade-off in achievable throughput and energy harvesting rate.

In this paper, we consider the resource allocation in two-tier wireless systems in which small-cell base stations harvest energy from renewable sources in addition to the conventional power grid. Moreover, we assume that each cell has access to an energy storage system with a limited battery capacity. We introduce new mechanisms that enable an efficient allocation of the available energy over time across the network. In doing so, we take into account the time-varying fading channel, the inter-cell interference and the fluctuations of harvested energy. In particular, we propose convergent offline algorithms to maximize the network energy efficiency while satisfying an average sum-rate constraint at each cell. Furthermore, we extend the resource allocation scheme by enabling an energy cooperation between the cells. By cooperating, they can exchange their harvested energy through a smart-grid power infrastructure. Using numerical simulations, we verify the convergence of the proposed algorithms and analyze the efficacy of the resource allocation and energy cooperation schemes.

Adaptive link selection for buffer–aided relaying can provide significant performance gains compared to conventional relaying with fixed transmission schedule, when perfect channel state information (CSI) is available for link selection. However, in practice, link selection may have to be performed based on outdated CSI, because of infrequent feedback of CSI and/or feedback delay. In this paper, we study the effect of outdated CSI on the error rate performance of adaptive link selection for a three node decode–and–forward (DF) relay network with fixed–rate transmission. In particular, we propose two protocols for link selection based on whether the reliability of the CSI estimates is known or not. For both protocols, we provide a unified error-rate analysis in terms of a decision threshold $\beta$ , which can be adjusted to maintain buffer stability, and derive asymptotic approximations, which reveal the diversity and coding gains. Since packet transmission delay is unavoidable for opportunistic link selection, we analyze the average delay and throughput considering both finite and infinite buffer size. The average delay and throughput are functions of the decision threshold $\beta$ , which can be optimized to minimize the error rate while satisfying average delay and/or throughput constraints. Numerical results manifest that even with outdated CSI, adaptive link selection provides a significant coding gain advantage over conventional DF relaying. Furthermore, we show that a diversity gain of two can be achieved for perfect CSI and the optimum error rate can be approached with small delay and/or high throughput.

We propose relay-based wireless-powered uplink cellular networks in which users first harvest energy from RF transmissions of base station/relay nodes and then use that energy for uplink transmission. Given the limited total transmission time and available energy at the relay node, we propose different resource allocation frameworks for the proposed relay-based networks considering two different relay-based harvest-then-transmit scenarios. We first propose iterative algorithm to determine time and relay node power allocation (for downlink wireless charging and uplink data transmission/relaying) for both scenarios. We then perform joint optimal time and power allocation for one scenario. Resource allocation results show that most of the available resources (transmission time and relay node energy) are allocated for wireless energy harvesting which is similar to that observed for downlink simultaneous wireless information and power transfer (SWIPT) in the existing literature. Simulation results on comparison of different relay-based scenarios reveal interesting insights and demonstrate remarkable improvement of different performance metrics of wireless-powered cellular networks in the presence of relay node.

In this paper, we consider the robust transceiver design for a multi-hop full-duplex decode-and-forward (DF) relay system employing single-carrier transmission with frequency-domain equalization (SC-FDE). We take into account the effect of imperfect channel state information (CSI) where the CSI errors are modeled as Gaussian random variables with known statistics. The design of the precoding at the transmitter and the equalization at the receiver is formulated as an optimization problem with the objective to minimize two relevant performance metrics, namely, the sum mean-square error (MSE) and the maximum MSE across the different hops, subject to separate transmit power constraints for the nodes. We show that the equalization filters can be optimized individually at the receiving nodes and take the form of robust Wiener filters. However, due to the loopback/backward interference, the transmit signals in the different hops are coupled and the transmit precoding problem leads to a nonconvex power allocation problem in the frequency domain. To find the optimal power allocation, we first employ a sequential geometric programming (sGP) approach, which uses the condensation technique to transform the objective function into a posynomial and then solves a sequence of standard GP problems. The sGP approach requires global channel knowledge at a central node and the involved subproblems admit only numerical solutions. To gain further insight into the structure of the problem, we also consider an alternating optimization (AO) approach for power allocation where convex programming problems and difference of convex programming problems are solved in an alternating manner. The resulting AO algorithm admits closed-form solutions in each iteration step and requires less signaling overhead compared to the centralized sGP scheme. Numerical results for the MSEs and achievable rates of the proposed robust schemes are provided, showing that the proposed sGP and AO algorithms yield significant performance gains compared to conventional half-duplex relay systems and nonrobust full-duplex designs.

In this paper, we study the robust design of the relay beamforming (rBF) and destination equalization (dEQ) filters for broadband multiuser multi-relay networks employing single-carrier frequency-division multiple access (SC-FDMA) and orthogonal frequency-division multiple access (OFDMA). Thereby, we consider the realistic case where only imperfect channel state information is available for rBF and dEQ filter optimization. Our goal is to maximize a lower bound for the weighted achievable bit rate (ABR) of the network, subject to either individual relay power constraints (Ind-PCs) or an aggregate relay power constraint (Agg-PC). We first derive the optimal dEQ filters and the phases of the optimal rBF filter coefficients, which are independent of the power constraints. For the Agg-PC, the amplitude optimization of the rBF filter coefficients is decomposed into two subproblems, which correspond to the optimization of the power allocation across the relays and the power allocation across the users and subcarriers, respectively. We obtain a closed-form structural solution for the first subproblem by fixing the powers across users and subcarriers, and the global optimal solution for the second subproblem. For the Ind-PCs, the corresponding optimization problem is formulated as a reverse-convex problem with convex constraints. Subsequently, the constrained convex concave procedure is applied to approximate the original non-convex problem with a sequence of convex problems, which can be efficiently solved using convex optimization techniques. Simulation results validate the excellent performance of the proposed robust rBF and dEQ filter designs and show their superiority compared to conventional non-robust and naive relaying schemes.

This paper addresses a joint downlink and uplink aware cell association problem in a multi-tier heterogeneous network in which base stations (BSs) have finite number of resource blocks (RBs) to distribute among the users. An optimization problem is defined to maximize the sum of weighted utility of long term data rate in downlink and uplink through cell association and RB distribution while maintaining quality of service (QoS). Separate outage requirements are considered as QoS constraints for downlink and uplink of a user. Using outage QoS constraints renders the problem suitable for fast fading environments. We propose a distributed scheme for the cell association problem. As users cannot measure the uplink attributes by listening to the reference signals of BSs, a limited amount of feedback is added to the reference signals of the BSs to inform the users of the uplink interference and make the distributed algorithm design possible. Moreover, by assigning different weights to the downlink and uplink data rates, the proposed scheme can be downlink oriented, uplink oriented, or both downlink and uplink aware. Comparing the proposed scheme with the downlink oriented maximum signal to interference plus noise ratio (SINR) scheme, significant uplink rate gains are observed in the simulations as well as for the downlink rates.

To support the surge in wireless data traffic, the spectrum and energy efficiencies of cellular networks should be largely increased. Heterogeneous two-tier architecture has been identified as one key solution. However, small-cell deployment raises questions about the resulting energy efficiency and interference mitigation. Therefore, we propose an energy-efficient and cognitive spectrum sharing scheme between primary macrocell and secondary small cells. Specifically, the small cells allocate their transmission power to maximize their total energy efficiency while respecting some interference constraints imposed by macrocell users. We solve this centralized optimization in two steps. First, assuming that the small-cell transmissions are noninterfering, the solution of this nonconvex optimization is characterized using a convex parametric approach. Using this characterization, we derive an algorithm based on Newton method, which converges to a global optimal solution. Second, when the small-cell transmissions are not necessarily orthogonal, we derive an algorithm, which converges at least to a local optimum, using the minorization-maximization principle and Newton method. Through simulations, we validate the convergence of these algorithms and compare their performance with existing schemes. We also analyze the effects of the interference and of the number of users on the energy efficiency.

We develop robust resource-allocation schemes for a cognitive radio network (CRN), where the secondary users (SUs) try to communicate with each other from different small cell primary user (PU) networks. User cooperation technique is considered for communication among the SUs since PUs are in close proximity and there are tight interference constraints on the PU bands. Power allocation and relay selection schemes are optimized with the provision of quality of service to each SU considering different channel uncertainty models. We incorporate the channel outage events that have resulted from the imperfect channel state information under slow-fading channels in our resource optimization algorithms. We maximize the system goodput of the CRN while satisfying the interference constraints of the PU bands both probabilistically and for the worst case scenario. The original probabilistic optimization problem is approximated and transformed into a convex deterministic form, and a closed-form analytical solution for power allocation is derived. The closed-form power allocation solution helps us to develop an efficient relay selection scheme based on Hungarian algorithm. Simulation results reveal the effectiveness of our proposed schemes and the implications of ignoring the imperfectness among different channels when developing resource-allocation algorithms for CRNs.

In this paper, an energy-efficient resource allocation scheme is designed for orthogonal frequency-division multiple-access cellular wireless networks with multiuser cooperation. Joint relay selection, subcarrier allocation and pairing, and power-allocation algorithms are developed with the objective of maximizing the energy efficiency of the system considering the quality-of-service requirements of the users. The optimization problem is a mixed-integer nonlinear program (MINLP), which is generally very difficult to solve in its original form. The energy efficiency metric is a fractional and nonlinear function, which complicates the problem further. We provide a novel optimization framework to solve such nonlinear and nonconvex optimization problems. The MINLP optimization problem is reformulated to a convex problem by relaxing the integer variables and by introducing the "Dinkelbach" method to tackle the nonlinear fractional objective function. We prove that our proposed solution of the relaxed problem is optimal and has integer values. Based on the dual-decomposition method, we propose a solution to the joint optimization problem, which is optimal and computationally efficient with polynomial-time complexity. Numerical results demonstrate the effectiveness of our proposed scheme.

In this paper, we consider physical layer security provisioning in multicell massive multiple-input-multiple-output (MIMO) systems. Specifically, we consider secure downlink transmission in a multicell massive MIMO system with matched-filter precoding and artificial noise (AN) generation at the base station (BS) in the presence of a passive multiantenna eavesdropper. We investigate the resulting achievable ergodic secrecy rate and the secrecy outage probability for the cases of perfect training and pilot contamination. Thereby, we consider two different AN shaping matrices, namely, the conventional AN shaping matrix, where the AN is transmitted in the null space of the matrix formed by all user channels, and a random AN shaping matrix, which avoids the complexity associated with finding the null space of a large matrix. Our analytical and numerical results reveal the following, in multicell massive MIMO systems employing matched-filter precoding: 1) AN generation is required to achieve a positive ergodic secrecy rate if the user and the eavesdropper experience the same path loss; 2) even with AN generation, secure transmission may not be possible if the number of eavesdropper antennas is too large and not enough power is allocated to channel estimation; 3) for a given fraction of power allocated to AN and a given number of users, in case of pilot contamination, the ergodic secrecy rate is not a monotonically increasing function of the number of BS antennas; and 4) random AN shaping matrices provide a favorable performance/complexity tradeoff and are an attractive alternative to conventional AN shaping matrices.

In this paper, we study the application of bit—interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM) to reap the benefits of wireless multiuser network coding in practical frequency—selective fading channels. We propose a mapping based symbol level network coding (SLNC) scheme for a cooperative diversity system comprising multiple sources, multiple relays, and one common destination. A simple cooperative maximum—ratio combining scheme is used at the destination and is shown to successfully exploit both the full spatial and the full frequency diversity offered by the channel for arbitrary numbers of sources, arbitrary numbers of relays, and arbitrary linear modulation schemes. To gain analytical insight for system design, we derive a closed—form upper bound for the asymptotic worst—case pairwise error probability (PEP) and obtain the diversity gain of the considered SLNC scheme for BICM—OFDM systems. These analytical results reveal the influence of the various system parameters, such as the number of sources, the free distance of the code, and the frequency diversity of the involved links, on performance. Furthermore, we propose two different relay selection schemes for the considered system: a) bulk selection, i.e., a single best relay is selected to transmit on all sub—carriers, and b) per—subcarrier selection, where a best relay is selected on each sub—carrier. Last but not least, we exploit the derived PEP expression for selecting a subset of sources from the set of active sources when the number of active sources is larger than the number of available orthogonal relay channels. We study the achievable diversity gain for the proposed relay and source subset selection schemes. Numerical results corroborate the derived diversity gain expressions and confirm the performance gains.

We investigate the trade-off between performance and energy efficiency in cooperative cognitive underlay systems. By cooperating, cognitive base stations mitigate the interference and serve their users more effectively at the cost of spending more power. With a flexible cooperation scheme, we thus jointly optimize the clustering and the beamforming to minimize the overall power consumption while satisfying the secondary users' quality of service and respecting the primary users' interference limits. We formulate this problem as a mixed-integer nonlinear program and decompose it into a master problem and a beamforming subproblem. Then, we derive an iterative algorithm based on the generalized Benders decomposition method to find an optimal solution. Moreover, we propose simple techniques to speed up its convergence. When the clusters are fixed and non-overlapping, we also propose a decentralized algorithm with limited signaling schemes using the alternating direction method of multipliers. In contrast to previous works, our distributed algorithm handles the total interference power constraints coupling the cognitive base stations. Through simulations, we analyze the effectiveness and convergence of the proposed algorithms and show the benefits of the cooperation in cognitive wireless networks.

User cooperation although improves performance of wireless systems, it requires incentives for the potential cooperating nodes to spend their energy acting as relays. Moreover, these potential relays are better informed than the source about their transmission costs, which depend on the exact channel conditions on their relay-destination links. This results in asymmetry of available information between the source and the relays. In this paper, we use contract theory to tackle the problem of relay selection under asymmetric information in OFDM-based cooperative wireless system that employs decode-and-forward (DF) relaying. We first design incentive compatible offers/contracts, consisting of a menu of payments and desired signal-to-noise-ratios (SNR)s at the destination and then the source broadcasts this menu to nearby mobile nodes. The nearby mobile nodes who are willing to relay notify back the source with the contracts they are willing to accept in each subcarrier. We show that when the source is under a budget constraint, the problem of relay selection in each subcarrier in order to maximize the capacity is a nonlinear non-separable knapsack problem. We propose a heuristic relay selection scheme to solve this problem. We compare the performance of our overall mechanism and the heuristic solution with a simple relay selection scheme and selected numerical results showed that our solution performs better and is close to optimal. The overall mechanism introduced in this paper is simple to implement, requires limited interaction with potential relays and hence requires minimal signalling overhead., 30 Pages, 8 figures, 3 tables, journal

In this letter, we investigate power allocation schemes for OFDM-based cognitive radio (CR) systems while taking errors due to imperfect sensing of primary user (PU) into account. The interference is introduced into PU band by CR user not only because of co-existence in side-by-side bands but also by missed detection due to imperfect sensing. We propose an optimal scheme such that the transmission capacity of CR users is maximized while keeping the total interference within a prescribed threshold and keeping the total power within a budget. As the optimal scheme can have a high complexity, low complexity suboptimal schemes have also been proposed. The performance of the proposed schemes is compared to the schemes that do not take sensing errors into consideration. Presented numerical results demonstrate the strength of our proposed schemes.

In this paper, we propose joint relay selection and power allocation algorithms that can work robustly under imperfect channel knowledge for a decode-and-forward (DF) cellular relay network. The objective is to minimize the uplink transmit power of the network taking each user's target data rate as the quality of service (QoS) constraint in the presence of imperfect channel state information (CSI). We consider the worst-case optimization approach, in which QoS constraint is satisfied for all users assuming both probabilistic and deterministic channel estimation error models. In this optimization framework, equivalent convex formulations are derived for the nonlinear optimization problems that are often combinatorially hard to solve in their original forms. After relay selection, efficient centralized as well as distributed power allocation algorithms scalable with respect to the size of the networks are developed. We also consider the case of power constrained networks where the objective is to provide QoS in the presence of limited power budgets on source and relays. The robust optimization problem is reformulated accordingly and efficient solution is provided. Numerical results show the effectiveness of the proposed algorithms and demonstrate the implications of ignoring channel estimation errors while developing relay selection and power allocation algorithms.

In this paper, we study a cooperative diversity scheme for wireless systems employing network coding and the combination of bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). The considered system comprises multiple sources, one relay, and one destination. The relay decodes the signal received from all sources and performs network coding before forwarding the signal to the destination. We propose a simple cooperative maximum-ratio combining scheme for the destination which can successfully exploit the full spatial and frequency diversity offered by the channel for arbitrary numbers of sources and arbitrary linear modulation schemes. Furthermore, we propose techniques to reduce the signaling overhead and the decoding complexity at the destination. To gain insight for system design, we derive a closed-form upper bound for the asymptotic worst-case pairwise error probability and the diversity gain of the considered network coded cooperative BICM-OFDM system. These analytical results reveal the influence of various system parameters, including the number of sources, the free distance of the code, and the frequency diversity of the involved links, on performance. Based on the derived analytical results, we develop schemes for optimal relay placement and power allocation. Simulation results corroborate the derived analytical results and confirm the effectiveness of the developed optimization framework.

Energy consumption in wireless communication system is rapidly increasing due to growing wireless multimedia access. Combating adverse effects of excessive energy consumption demands for energy-aware system design, leading to a new research paradigm called green communication. In this paper, we propose user selection and power allocation schemes for a multi-user, multi-relay cooperative cellular system in order to minimize the cost of transmission. In the proposed schemes, the cost function is first formulated to optimize the weighted sum powers of base and relay stations. It is then extended to a more general multi-objective scheme which jointly optimizes the sum power and throughput keeping a balance between them. In both of the schemes, quality-of-service is guaranteed in terms of end-to-end signal-to-noise ratio. To make the proposed schemes realistic, we assume the presence of estimation errors in channel state information. An algorithm to enhance fairness among users in these schemes is also presented. Simulation results are presented to confirm the performance of proposed schemes in terms of energy efficiency, system throughput, outage probability, and fairness to end users.

This paper investigates a decode-and-forward two-hop relaying system consisting of one source, one relay and one destination, in which orthogonal frequency division multiplexing is used. The relay forwards the message received from the source on a subset of available subcarriers in the second time slot. Firstly, a subcarrier pairing and selection algorithm is proposed, assuming that perfect channel state information (CSI) is available at all nodes, then, power is allocated to both the source and relay stations under individual power constraints in order to maximize the capacity. Secondly, subcarrier selection and pairing, and power allocation (PA) under partial CSI assumption along with individual power constraints are addressed. The result is a novel distributed algorithm with low complexity maximizing the expected value of capacity at the source and relay nodes. Finally, the simulation results show that selective relaying combined with subcarrier pairing and PA improves the system capacity to a considerable extent in both perfect and partial CSI cases.

This paper presents a lower-bound on the ergodic sum capacity of single-input single-output (SISO) opportunistic spectrum sharing multiple access channel (MAC) in Rayleigh fading channels with imperfect channel estimates. Unlike the previous studies, this paper considerers the channel estimation errors for both sets of SISO channels between the secondary user (SU) transmitters and the SU receiver and between the SU transmitters and the primary user receivers. By considering these channel estimation errors, an optimal power allocation policy for each SU transmitter is derived to achieve a lower-bound of the ergodic sum capacity of opportunistic spectrum sharing MAC, subject to a set of SUs transmit power constraints and a set of interference power constraints at each PU receiver. Furthermore, convex optimization techniques are exploited to derive an efficient algorithm to obtain the optimal power allocation for each SU transmitter. Numerical results are presented to illustrate the influences of the channel estimation errors on the ergodic sum capacity.

Dynamic resource availability and lack of central control unit offer many challenges while designing medium access control (MAC) protocol for a distributed cognitive radio network (DCRN). In this paper, we propose a novel MAC design for DCRN which provides an efficient approach to address quality of service (QoS) requirements of delay sensitive applications by defining higher priority to such applications during channel reservation. It also combats other major challenges such as efficient spectrum utilization, multi-channel hidden terminal problem (MHTP) and collision with primary user (PU) due to sensing error at SU. We develop an analytical framework to study the performance of the proposed protocol. We then compare the performance of proposed protocol with those of two existing protocols. Comparison results show that proposed MAC outperforms the existing protocols by providing better throughput and reducing DCRN users' collision probability with PUs in presence of sensing error. The results achieved from the analytical model and validated by simulations show that our simple yet efficient design identifies and fulfils the QoS requirements of delay sensitive applications, achieves excellent spectrum utilization, shows superb robustness in presence of sensing errors and handles MHTP effectively.

In this letter, we investigate the relay and power allocation problem for OFDM-based cognitive radio (CR) systems with single antennae. We propose a method where the capacity of CR user employing relays is maximized while total transmission power is kept within a budget and the interference introduced to the primary user (PU) band is kept within a prescribed threshold. The optimization problem is a mixed-integer problem, which is NP-hard. Hence in this letter, we have proposed three sub-optimal schemes. The presented numerical results show that the performance of proposed suboptimal schemes is close to the optimal solution.

This paper presents a blind parameter estimation algorithm for orthogonal frequency division multiplexing (OFDM) signal affected by a time-dispersive channel, timing offset, carrier frequency offset and additive Gaussian noise. Unlike the previous studies, this paper presents the second-order cyclostationarity of OFDM signal considering the effect of time-dispersive channel. The cyclostationarity properties of received OFDM signal in time-dispersive channel is exploited to estimate the OFDM parameters. These parameters includes OFDM symbol period, useful symbol period, cyclic prefix factor, number of subcarriers and carrier frequency offset. Simulations are performed to investigate the performance of OFDM parameter estimation algorithm in diverse channel conditions.

We investigate cooperative sensing schemes for a cognitive radio (CR) network operating in multiple primary bands. The CR spectrum sensors use energy detection to identify the presence of the primary signals. We consider a parallel fusion architecture in which all the sensing devices send their binary quantized sensing information to an access point, which then applies a fusion rule to determine the presence of a primary user in each band. In this paper, we first propose schemes to assign sensors to various primary bands. We then study efficient quantization schemes when `OR' fusion rule is used in each band at the access point. We show that the optimal quantization scheme is, in general, non-convex and propose a suboptimal solution based on convex restriction of the original problem. We further study quantization schemes for general k-out-of-N fusion rule. We compare the performance of the proposed schemes using simulations.

We study the end-to-end capacity of a cooperative relaying scheme using OFDM modulation, under power constraints for both the base station and the relay station. The relay uses an amplify-and-forward cooperative relaying technique to retransmit messages on a subset of the available subcarriers. The power used in the base station and the relay station transmitters is allocated in such a manner that the overall system capacity is maximized. The subcarrier selection and power allocation are obtained based on convex optimization formulations and an iterative algorithm. The proposed technique outperforms nonselective relaying schemes over a range of relay power budgets.

We develop a queueing analytic framework to study the data link layer quality-of-service performance measures for cognitive radio users in an infrastructure-based dynamic spectrum access environment. In order to allocate the available spectrum white spaces among the cognitive radio users in a spectrum overlay scenario, an opportunistic scheduling scheme is considered. The queueing model considers bursty traffic arrival pattern at the cognitive radio user ends, finite buffer size, activity of primary users (i.e., dynamic channel availability), and correlated channel fading. We present a step-by-step procedure to derive the delay distribution, average throughput, and packet loss rate for the cognitive radio users. The proposed framework facilitates cross-layer design for improved QoS experience in cognitive radio networks. Usefulness of the developed analytical model is demonstrated through example applications.

We develop a queuing analytic model to study cross-layer effects on quality of service (QoS) performance for downlink transmission in a Vertical Bell Laboratories Layered Space-Time Architecture (V-BLAST) multiple input multiple output (MIMO) wireless system exploiting multiuser diversity. As in multiuser single input single output (SISO) systems, multiuser diversity has been shown to have a great potential in improving system throughput in multiuser MIMO systems as well. In a V-BLAST MIMO system, the transmit antennas can carry parallel streams and multiple users can be scheduled for transmissions at the same time. We consider a multiuser diversity scheme that can effectively exploit this extra dimension in multiuser scheduling. To investigate the cross-layer aspect of the performance improvement, we perform a queuing analysis to derive buffer statistics, queuing delay distribution, packet throughput and loss rate experienced in the data link layer when this multiuser diversity technique is deployed in the system. We also present selected numerical results to show how the queuing model can help us to relate these important QoS measures to relevant physical layer and traffic parameters. Usefulness of the developed analytical model is also demonstrated through example applications.

In this paper, we analyze the effect of the wormhole attack on shortest-path routing protocols for wireless ad hoc networks. Using analytical and simulation results, we show that a strategic placement of the wormhole when the nodes are uniformly distributed can disrupt/control on average 32% of all communications across the network. We also analyze a scenario in which several attackers make wormholes between each other and a case where two malicious nodes attack a target node in the network. We show how to evaluate the maximum effect of the wormhole attack on a given network topology. Then, we compute the maximum effect of the wormhole attack on grid topology networks and show that the attackers can disrupt/control around 40% to 50% of all communications when the wormhole is strategically placed in the network. Finally, to defend against the wormhole attack, we propose a timing-based countermeasure that avoids the deficiencies of existing timing-based solutions. Using the proposed countermeasure, the nodes do not need synchronized clocks, nor are they required to predict the sending time or to be capable of fast switching between the receive and send modes. Moreover, the nodes do not need one-to-one communication with all their neighbors and do not require to compute a signature while having to timestamp the message with its transmission time.

In this paper, we investigate an optimal power loading algorithm for an OFDM-based cognitive radio (CR) system. The downlink transmission capacity of the CR user is thereby maximized, while the interference introduced to the primary user (PU) remains within a tolerable range. We also propose two suboptimal loading algorithms that are less complex. We also study the effect of a subcarrier nulling mechanism on the performance of the different algorithms under consideration. The performance of the optimal and suboptimal schemes is compared with the performance of the classical power loading algorithms, e.g., water-filling and uniform power but variable rate loading schemes that are used for conventional OFDM-based systems. Presented numerical results show that for a given interference threshold, the proposed optimal scheme allows CR base station (BS) to transmit more power in order to achieve a higher transmission rate than the classical loading algorithms. These results also show that although the proposed suboptimal schemes have certain degradation in performance compared to the optimal scheme, they outperform the classical loading algorithms. We also present numerical results for nulling mechanism. Finally, we investigate the effect of imperfect channel gain information at the transmitter.

One of the major challenges in direct sequence-ultra wideband (DS-UWB) receiver design is intersymbol interference (ISI). Several equalization schemes to eliminate ISI in DS-UWB systems have been proposed in the literature. It was shown that frequency-domain (FD) equalization techniques can offer better trade off between performance and complexity compared to time- domain equalization schemes for DS-UWB systems on highly dispersive channels. In this paper, we derive low-complexity FD minimum mean square error turbo equalization schemes for single-user binary phase shift keying (BPSK) and quaternary bi-orthogonal keying (4BOK) DS-UWB systems. For multiuser DS-UWB systems, we combine FD turbo equalization schemes with soft interference cancelation to obtain multiuser FD turbo detectors. The bit error rate performance gain due to turbo detection is shown to be significant, particularly for multiuser DS-UWB systems.

We investigate the packet-level and the connection-level performances in a power-constrained (e.g., solar-powered) wireless mesh router with packet scheduling and admission control. The system model under consideration is compatible with the IEEE 802.16a standard with orthogonal frequency division multiple access (OFDMA) air interface. In the medium access control (MAC) layer, transmission frames are grouped into super-frames and the allocation of subchannel and number of frames to each connection (i.e., scheduling) is performed to satisfy the traffic requirement of each connection and also the power supply constraint at a mesh router/802.16a base station (BS). A queueing analytical model based on discrete-time Markov chain (DTMC) is used to analyze the packet-level performances. Based on this scheduling, router capacity in terms of the maximum number of ongoing connections is obtained. Subsequently, a threshold-based admission control method is proposed for both relay and local connections in a router so that connection-level performances are satisfied. To this end, we optimize the admission control over multiple time periods in which the amount of supplied power and the traffic load at a mesh router are time-dependent.

We address the issue of optimal coding rate scheduling for adaptive type-I hybrid automatic repeat request wireless systems. In this scheme, the coding rate is varied depending on channel, buffer and incoming traffic conditions. In general, we consider the hidden Markov model for both time-varying flat fading channel and bursty correlated incoming traffic. It is shown that the appropriate framework for computing the optimal coding rate allocation policies is partially observable Markov decision process (POMDP). In this framework, the optimal coding rate allocation policy maximizes the reward function, which is a weighted sum of throughput and buffer occupancy with appropriate sign. Since polynomial amount of space is needed to calculate the optimal policy even for a simple POMDP problem, we investigate maximum-likelihood, voting and Q-MDP policy heuristic approaches for the purpose of efficient and real-time solution. Our results show that three heuristics perform close to completely observable system state case if the fading and/or traffic state mixing rate is slow. On the other hand, when the channel fading is fast, Q-MDP heuristic is the most throughput-efficient among considered heuristics. Also, its performance is close to the optimal coding rate allocation policy of fully observable system state case. We also explore the performances of the proposed heuristics in the bursty correlated traffic case and show that maximum-likelihood and voting heuristics consistently outperform the non-adaptive case

We address the issue of optimal packet scheduling over correlated fading channels which trades off between minimization of three goals: average transmission power, average delay and average packet dropping probability. We show that the problem forms a weakly communicating Markov decision process and formulate the problem as both unconstrained and constrained problem. Relative value iteration (RVI) algorithm is used to find optimal deterministic policy for unconstrained problem, while optimal randomized policy for constrained problem is obtained using linear programming (LP) technique. Whereas with RVI only a finite number of scheduling policies can be obtained over the feasible delay region, LP can produce policies for all feasible delays with a fixed dropping probability and is computationally faster than the RVI. We show the structure of optimal deterministic policy in terms of the channel and buffer state and form a simple log functional suboptimal scheduler that approximately follows the optimal structure. Performance results are given for both constant and bursty Poisson arrivals, and the proposed suboptimal scheduler is compared with the optimal and channel threshold scheduler. Our suboptimal scheduler performs close to the optimal scheduler for every feasible delay and is robust to different channel parameters, number of actions and incoming traffic distributions.

The problem of adopting space-time block coding (STBC) in the uplink of direct sequence code division multiple access (DS-CDMA) systems is addressed. A novel system architecture is proposed for space-time (ST)-coded uplink transmissions over multipath fading channels with multiple user interference (MUI)-free reception. This proposed system is a combination of single-carrier time-reversal zero-padding (SC-TR-ZP)-based STBC with chip-interleaved block-spread (CIBS)-CDMA. Simulation results show that a substantial performance improvement can be achieved by adopting ST coding compared to the original CIBS-CDMA scheme without ST coding. Optimal maximum likelihood sequence estimation (MLSE) may be computationally prohibitive for long channels and/or with high-level modulation. Hence, the performance of different decision feedback sequence estimation (DFSE) schemes is investigated for the proposed ST-coded uplink system. In the case of whitened DFSE (WDFSE), a linear prediction (LP)-based approach is adapted for designing a whitening prefilter. Furthermore, a new scheme, which is a combination of linear equalization (LE) and modified unwhitened DFSE (MUDFSE) is proposed. The proposed combined LE-MUDFSE (Comb. LE-MUDFSE) scheme is very attractive as error-floor behavior appearing in other unwhitened DFSE schemes is eliminated. The simulation results indicate that a substantial performance improvement over the minimum mean-square error (MMSE) equalizer can be achieved by using either Comb. LE-MUDFSE scheme or WDFSE scheme.

This paper addresses the problem of dynamic rate and power adaptation for forward link data transmission using high-order modulation and multicode formats in cellular wideband code division multiple access (WCDMA) networks. A novel framework for dynamic joint adaptation of modulation order, number of code channels (hence transmission rate), and transmission power is proposed for downlink data transmission in a cellular WCDMA system where different users have similar frame error rate (FER) requirements. Based on a general downlink signal-to-interference ratio (SIR) model, the problem of optimal dynamic rate and power adaptation is formulated, for which the rate and power allocation can be found by an exhaustive search. Two heuristic-based dynamic rate and power allocation schemes are proposed. Performance of dynamic joint rate and power adaptation under the proposed framework is evaluated for a random micromobility model using computer simulations. Also, an analytical approach to evaluate the throughput performance of dynamic rate and power adaptation using high-order modulation and multicode formats is presented.

Semiblind multiuser detection for the reverse link of a multicell code-division multiple-access (CDMA) system is considered. In such a system, although active users are present in the home cell and neighboring cells, the base station receiver only knows the signature information of the users in its own cell. Three new semiblind linear multiuser detectors are proposed based on parallel interference cancellation approach. Compared with some known semiblind detectors, the proposed detectors share a rather simple implementation structure and involve reduced latency for processing, which are of particular importance in a time-varying CDMA system. Numerical results are presented to compare the detection performance of the proposed detectors with that of some existing semiblind multiuser detectors.

This letter models and analyzes an adaptive radio link level error control protocol using Reed–Solomon codes for wireless networks. Results show that the proposed dynamic rate adaptive strategy provides a much improved throughput relative to a conventional type-I and type-II hybrid-automatic repeat request (ARQ) protocols.

This paper addresses the problem of dynamic link adaptation under multiple quality-of-service (QoS) constraints in cellular wideband code-division multiple-access (WCDMA) systems. A novel dynamic joint rate and power adaptation framework is proposed for downlink data transmission in a multicell variable spreading factor (VSF) WCDMA system where the different classes of users have different signal-to-interference ratio (SIR) requirements. Based on a general downlink SIR model, the problem of optimal dynamic rate and power adaptation under multiple SIR constraints is also formulated, for which the rate and power allocation can be found by an exhaustive search. Two schemes, namely, near-optimal and suboptimal schemes, are proposed for implementation-friendly dynamic rate and power adaptation. Performance of dynamic joint rate and power adaptation under the proposed framework is evaluated under random micro-mobility model with uncorrelated long-term fading and a directional micro-mobility model with correlated long-term fading in a cellular WCDMA environment.

The usage of multichannel receivers that are followed by diversity combining can significantly improve the performance of wireless communications links. In this paper, we investigate in some detail three popular techniques for diversity combining, namely maximal ratio combining (MRC), equal gain combining, and selection combining. The main goal is to investigate under which circumstances it can be guaranteed that a certain technique does indeed decrease the probability of error of a multichannel receiver. The analyzed fading model is quite general with the only assumption that fading gains are scaled exchangeable random variables. The crucial mathematical tool used to produce the results of this paper is the powerful theory of stochastic majorization. Among other results, it was shown that the average bit-error probability of the MRC receiver with equally distributed average powers always decreases with the addition of a new diversity branch if the total power is fixed.

In this letter, concatenation of trellis-coded modulation (TCM) with differential space-time block codes (DSTBC) is considered under frequency-flat Rayleigh-fading channels with and without perfect interleaver. It is shown that the design criteria of TCM concatenated with DSTBC are exactly the same as that of TCM concatenated with space-time block codes (STBC) in perfectly known channels. They are effective code length over span two symbol intervals and minimum product-sum distance over span two symbol intervals. Based on the design criteria, several new rate-2/3 systematic Ungerboeck's TCM schemes have been found by computer search. They outperform existing optimal TCM schemes designed for additive white Gaussian noise or flat fading channels.

Single-cell and multiple-cell direct-sequence code-division multiple-access (DS-CDMA) systems supporting heterogeneous traffic are investigated when decorrelating detector is used at receiver. Theoretical analyses and numerical examples are presented to study the effect of imperfect power control on the system performance of the reverse link. As to the forward link, the system performance is analyzed and the power allocation problem at base stations is formulated as a constrained optimization problem. Two algorithms, namely, the optimal algorithm and the unit transmission power allocation (UTXPA) algorithm, are proposed to solve this optimization problem. Computer simulations are presented to compare the performance of the proposed algorithms.

To improve the spectral efficiency while meeting the radio link level quality of service requirements such as the bit-error-rate (BER) requirements for the different wireless services, transmission rate and power corresponding to the different mobile users can be dynamically varied in a cellular wideband code-division multiple-access (WCDMA) network depending on the variations in channel interference and fading conditions. This paper models and analyzes the performance of transmission control protocol (TCP) under joint rate and power adaptation with constrained BER requirements for downlink data transmission in a cellular variable spreading factor (VSF) WCDMA network. The aim of this multilayer modeling of the WCDMA radio interface is to better understand the interlayer protocol interactions and identify suitable transport and radio link layer mechanisms to improve TCP performance in a wide-area cellular WCDMA network.

Optimal dynamic rate allocation among mobile stations for variable rate packet data transmission in a cellular wireless network is an NP-complete problem; therefore, suboptimal solutions to this problem are sought for. In this paper, three novel suboptimal dynamic rate adaptation schemes, namely, peak-interference-based rate allocation, sum-interference-based rate allocation, and mean-sense approximation-based rate allocation, are proposed for uplink packet data transmission in cellular variable spreading factor wide-band code division multiple access (WCDMA) networks. The performances of these schemes are compared to the performance of the optimal dynamic link adaptation for which the rate allocation is found by an exhaustive search. The optimality criterion is the maximization of the average number of radio link level frames transmitted per frame time under constrained signal-to-interference-plus-noise ratio (SINR) at the base station receiver. Two different error control alternatives for variable rate packet transmission environment are presented. We demonstrate that the dynamic rate adaptation problem under constrained SINR can be mapped into the radio link level throughput maximization problem with integrated rate and error control. Performance evaluation is carried out under random and directional micromobility models with uncorrelated and correlated long-term fading, respectively, in a cellular WCDMA environment for both the homogeneous (or uniform) and the nonhomogeneous (or nonuniform) traffic load scenarios.

Spectral efficiency presents the ultimate limit on data rate per unit bandwidth of a certain communication system. For direct-sequence code-division multiple access systems, the spectral efficiency has been derived in case of synchronous reception for optimal and linear multiuser receivers, flat-fading and nonfading environments, as well as single and multicell cellular networks. The most pervasive model employed in all these analyses is the large system random signature model. For the decision feedback receivers, previous research handled only the nonfading case while the case of fading channels remained unknown. This paper analyzes the spectral efficiency of the popular conventional decision feedback receiver (CDFR) in flat- and frequency-selective fading channels with and without power ordering. Results show that in the case of power ordering before cancellation and very large system loads, the spectral efficiency of this receiver in fading channels can be even larger than in the case of channels with no fading. We also discuss and identify optimal power control laws for the CDFR with and without power ordering. The power control law which equalizes single-user capacities in the case of power ordering is also discussed.

We propose a measurement-based dynamic random access (RA) code assignment procedure for prioritized packet data transmission in wideband code-division multiple access (WCDMA) networks. This dynamic adaptation process is based on analytical performance results derived for random packet access under Rayleigh fading in WCDMA networks. The performance of the proposed measurement-based RA code assignment procedure with three different adaptation methods is evaluated by using computer simulations. The performance of the proposed scheme is compared with those of a retransmission control-based and static channel allocation-based prioritized packet access scheme. An integrated (physical layer and link layer) delay-throughput performance model is presented for finite population RA WCDMA systems. The proposed dynamic RA code assignment procedure can be used in an adaptive quality of service (QoS) framework for dynamically adjusting the QoS of prioritized RA data traffic in the evolving WCDMA-based differentiated services wireless Internet protocol networks.

The sum capacity of optimally allocated spreading sequences with and without power control is studied for synchronous code-division multiple-access channels subject to frequency flat fading. Continuing upon the recently derived characterization of sum capacity for asymmetric received powers with no power control at the transmitter, we present an asymptotic analysis of the sum capacity in the wideband limit of a large number of users and evaluate possible gains compared with the random spread sequences. Next, we discuss group orthogonal optimal sequence allocation and analyze its potential to decrease the complexity of the receiver while achieving the sum capacity of optimally allocated equal-energy sequences. For the power controlled case, we give a general characterization of sum capacity and identify the optimal power-control law and sequence allocation that achieves this capacity. These results are also analyzed in the asymptotic limit of a large number of users.