In this paper, we propose a sequential spectrum sensing algorithm for cognitive radio systems, which we term the sequential shifted chi-square test (SSCT). SSCT has the following attractive features for practical implementations. First, SSCT employs a simple test statistic and thus has a low implementation complexity. Secondly, SSCT is a sequential detection algorithm and is capable of achieving performance comparable to fixed sample size detection algorithms such as energy detection but with much reduced sensing time. Thirdly, SSCT is essentially a non-coherent detection algorithm in the sense that it does not require any deterministic knowledge of the primary signals. Lastly, SSCT is able to strike a desirable trade-off between sensing performance and sensing time particularly in the signal-to-noise ratio mismatched case. To evaluate sensing performance, we derive the exact false-alarm probability for SSCT, and develop numerical integration algorithms to compute misdetection probability and the average sample number. We further demonstrate the performance of SSCT with several numerical examples.

While conventional cognitive radio (CR) system is striving at providing best possible protections for the usage of primary users (PU), little attention has been given to ensure the quality of service (QoS) of applications of secondary users (SU). When loading real-time applications over such a CR system, we have found that existing spectrum sensing schemes create a major hurdle for real-time traffic delivery of SU. For example, energy detection based sensing, a widely used technique, requires possibly more than 100 ms to detect a PU with weak signals. The delay is intolerable for real-time applications with stringent QoS requirements, such as voice over internet protocol (VoIP) or live video chat. This delay, along with other delays caused by backup channel searching, channel switching, and possible buffer overflow due to the insertion of sensing periods, makes supporting real-time applications over CR system very difficult if not impossible. In this paper, we present the design and implementation of a sensing-based CR system - RECOG, which is able to support realtime communications among SUs. We first redesign the conventional sensing scheme. Without increasing the complexity or trading off the detection performance, we break down a long sensing period into a series of shorter blocks, turning a disruptive long delay into negligible short delays. To enhance the sensing capability as well as better protect the QoS of SU traffic, we also incorporate an on-demand sensing scheme based on MAC layer information. In addition, to ensure a fast and reliable switching when PU returns, we integrate an efficient backup channel scanning and searching component in our system. Finally, to overcome a potential buffer overflow, we propose a CR-aware QoS manager. Our extensive experimental evaluations validate that RECOG can not only support realtime traffic among SUs with high quality, but also improve protections for PUs.

This paper considers the problem of how to quickly and accurately determine the availability of each spectrum band for a multi-band primary system using one or few sensors. Such problem is referred to as spectrum scanning. Two cases of practical interest are studied: 1) a single sensor case in which only one spectrum band is observed at one time; and 2) a multiple sensor case in which multiple spectrum bands are observed simultaneously. For each case, scenarios with and without a scanning delay constraint are investigated. Using mathematical tools from optimal stopping theory, optimal spectrum scanning algorithms are developed to minimize a cost function that strikes a desirable trade-off between detection performance and sensing delay. In the non delay-constrained case, it is shown that the optimal scanning algorithm is a concatenated sequential probability ratio test (C-SPRT). In the delay-constrained case, the optimal scanning algorithm has a high implementation complexity and truncation algorithms are developed as alternative low complexity options. Numerical examples are provided to illustrate the effectiveness of the proposed algorithms.

We propose a robust close-to-capacity dirty-paper coding (DPC) design framework in which multi-level low density parity check (LDPC) codes and trellis coded quantization (TCQ) are employed as the channel and source coding components, respectively. The proposed design framework is robust in the sense that it yields close to capacity solutions in the high-, medium-, and low-rate regimes. This is in contrast to existing practical DPC schemes that perform well only in one or two of these regimes, but not all three. We design codes for transmission rates of 0.5, 1.0, 1.5, and 2.0 bits/sample (b/s) using one, two, three, and four LDPC levels; at a block length of 2×105, the codes perform 0.95, 0.58, 0.55, and 0.54 dB from the corresponding information theoretic limits, respectively. We also propose a low-complexity decoding scheme that does not involve iterative message passing between the source and channel decoders; the low-complexity scheme performs only 1.08, 0.85, and 0.79 dB away from the theoretical limits at transmission rates of 1.0, 1.5, and 2.0 b/s, respectively.

This paper investigates the issue of how to balance the tradeoff between sensing performance and sensing costs/rewards for cooperative sensing in a multichannel cognitive radio system. Two cases of practical interest are studied. In the first case, the number of available sensors is assumed to be sufficient for detecting available frequency channels. For this case, we study the problem of selecting appropriate sensors to minimize the cost of detecting all the available channels subject to sensing performance constraints. The problem can be solved by using a branch-and-bound algorithm. In the second case, the number of available sensors is assumed to be insufficient for detecting all the available channels. For this case, we study the problem of selecting appropriate channels to maximize the sensing rewards subject to sensing performance constraints. Since the computational complexity of solving this problem optimally is fairly high, we propose a greedy algorithm as a low complexity solution to the problem. We further validate the effectiveness of the proposed algorithm via Monte-Carlo simulations.

In a cognitive radio (CR) network, cooperative spectrum sensing is a viable sensing technique to enhance spectral utilisation efficiency of secondary users (SUs) while ensuring the quality of service (QoS) of primary users (PUs). Intuitively, the more SUs are involved in sensing, the more sensing accuracy the CR can achieve, whereas the more sensing overhead the SUs consume, the less throughput the CR network can achieve. In this study, the authors investigate overhead-throughput trade-off over Rayleigh-fading channels in a cooperative CR network that consists of a number of the SUs employing energy detectors and a single decision fusion centre. Considering the trade-off, the authors prove that there is an optimal set of the sensing length and the number of SUs that maximise the throughput of an SU network. They further extend their analysis to a two-stage cooperative sensing mechanism where the second-stage fine sensing is triggered whenever any SU reports the presence of a PU after the first-stage detection. Numerical results showed that compared with the single-stage sensing, the two-stage sensing scheme achieves higher throughput via a reduction of the false alarm probability.

Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit sum-power constraint. An alternative approach is based on minimax duality, which can be applied to the case of the sum-power constraint or per-antenna power constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance constraint (LTCC) case, which includes sum-power and per-antenna power constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance constraints in the MIMO BC.

One of the major challenges in cognitive radio networks is to maintain the quality of service (QoS) for secondary transmissions while ensuring the protection of primary users PUs); the latter requirement is normally enabled through inband spectrum sensing. To maintain QoS of secondary traffic, a secondary user (SU) has to quickly switch to another empty spectrum band when a PU returns to its currently occupied spectrum by SU. The issue of how to quickly and accurately identify a free spectrum band when the operating spectrum band becomes unavailable is of practical interest; out-of-band spectrum sensing is a mechanism that can be leveraged for this purpose. In this paper, we propose a novel progressive out-of-band spectrum sensing (PG-Sensing) scheme that consists of two components, 1) progressive scanning which adaptively selects a subset of the available candidate spectrum bands which have a higher chance of being empty, and 2) multiband sequential shifted chi-squared test (SSCT) based searching that searches only the set of selected spectrum bands in the first step. The proposed PGsensing scheme significantly reduces the time to resume secondary transmissions thus allowing SUs to reliably and quickly switch to a free spectrum band when necessary while maintaining good QoS for PUs as well as SUs.

We propose a close-to-capacity dirty-paper coding framework which employs multi-level low density parity-check (LDPC) and trellis coded quantization. The proposed coding framework is robust in the sense that it performs close to capacity in the high as well as the low rate regimes. This is in contrast to existing practical DPC schemes which perform well at one of these regimes, but never both. In order to evaluate the performance of our scheme, we consider its application to a cognitive radio channel. At a block length of 2 × 105, the designed dirty-paper coding scheme operates within 0.95, 0.58 and 0.6 dB of the theoretical limit at transmission rates of 0.5, 1.0 and 1.5 bits/sample, respectively. As far as the authors are aware, this is the best performance reported in the literature so far.

The problem of sequentially finding an independent and identically distributed sequence that is drawn from a probability distributionQ1by searching over multiple sequences, some of which are drawn fromQ1and the others of which are drawn from a different distributionQ0, is considered. In the problem considered, the number of sequences with distributionQ1is assumed to be a random variable whose value is unknown. Within a Bayesian formulation, a sequential decision rule is derived that optimizes a trade-off between the probability of false alarm and the number of samples needed for the decision. In the case in which one can observe one sequence at a time, it is shown that the cumulative sum (CUSUM) test, which is well-known to be optimal for a non-Bayesian statistical change-point detection formulation, is optimal for the problem under study. Specifically, the CUSUM test is run on the first sequence. If a reset event occurs in the CUSUM test, then the sequence under examination is abandoned and the rule switches to the next sequence. If the CUSUM test stops, then the rule declares that the sequence under examination when the test stops is generated byQ1. The result is derived by assuming that there are infinitely many sequences so that a sequence that has been examined once is not retested. If there are finitely many sequences, the result is also valid under a memorylessness condition. Expressions for the performance of the optimal sequential decision rule are also developed. The general case in which multiple sequences can be examined simultaneously is considered. The optimal solution for this general scenario is derived.

Energy detection is an attractive spectrum sensing method for cognitive radio. The design of energy detection relies on two critical assumptions: 1) noise power is perfectly and {\it a prior} known; and 2) the test statistics in energy detection can be accurately modeled as independent and identically distributed (i.i.d.) Gaussian random variables. In practice, noise power varies from time to time. This renders difficulty in estimating noise power and incurs an inaccuracy in modeling the test statistics. This paper studies how to realize energy detection using software-defined radio in a real environment. The noise power variation in a real environment is investigated. A histogram based method is proposed to determine the threshold of energy detection. Our experimental study shows the effectiveness of the histogram based method.

In a cognitive radio (CR) network, secondary users (SUs) are allowed to opportunistically access a licensed spectrum that is not currently being occupied by primary users. This paper is concerned with the problem of how to quickly and accurately locate an unoccupied channel or determine that there is no unoccupied channel, from multiple (yet finite) candidate channels for a SU with a single detector. To design channel search algorithms, we propose a design criterion that minimizes average searching time subject to constraints on the error probabilities for a multichannel system. Relying on the proposed design criterion, we develop two efficient channel search algorithms that are based on a sequential application of the sequential probability ratio test and energy detection to the candidate channels.

We implement a dirty-paper coded framework for the cognitive radio channel. We assume that the cognitive user has non-causal knowledge about the primary user's transmissions. Thus the secondary receiver can employ dirty-paper coding to counter the effect of any interference from the primary user. In addition, we consider a situation where the introduction of the cognitive user should not affect the performance of the primary system -- nor should the primary system have to change its encoding/decoding process. For the primary user we use a low-density parity-check code and a 4-ary pulse amplitude modulation format. For the cognitive user, we propose a dirty-paper coding scheme which employs trellis-coded quantization as the source code and an irregular repeat-accumulate code as the channel code. At a transmission rate of 1.0 bits/sample, the designed dirty-paper coding scheme operates within 1.23 dB of the theoretical limit.

This paper studies the achievable rates of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channel with multiple single-/multi-antenna eavesdroppers. By assuming Gaussian input, the maximum achievable secrecy rate is obtained with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The maximum secrecy rate computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by existing methods. To handle this difficulty, this paper explores a new relationship between the secrecy channel and the recently developed cognitive radio (CR) channel, in which the secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called "interference temperature (IT)" constraints. By constructing an auxiliary multi-antenna CR channel that has the same channel responses as the secrecy channel, this paper shows that the optimal transmit covariance to achieve the maximum secrecy rate is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Thereby, finding the optimal complex transmit covariance matrix for the secrecy channel becomes equivalent to searching over a set of real IT constraints in the auxiliary CR channel. Based on this relationship, efficient algorithms are proposed to solve the non-convex secrecy rate maximization problem by transforming it into a sequence of convex CR spectrum sharing capacity computation problems, under various setups of the secrecy channel.

In this paper, differential modulation and demodulation in a multiple relay system using either the decode-and-forward (DF) protocol or the selection relaying (SR) protocol, are investigated. For the DF protocol, the detectors at the destination take the average bit error probabilities (BEPs) of all the source-relay transmissions into account. For a DF single relay system, the exact BEP and its approximation at high signal-to-noise ratio (SNR) are obtained. The approximation of the BEP at high SNR shows explicitly the diversity order and the different effects of the source-relay link and the relay-destination link on the end-to-end error performance. For a DF multiple relay system, a Chernoff upper bound on the BEP and a high SNR approximation for the BEP are obtained. For the SR protocol, some computational complexity is shifted from the destination to all the relays. Each relay computes the instantaneous BEPs of the source-relay transmissions, and uses the instantaneous BEPs to decide whether to transmit or remain silent. The destination performs simple maximal ratio combining (MRC) reception whose error performance is analyzed at high SNR. It shows from an error probability perspective that the SR protocol offers a space diversity order equal to the number of all the potential cooperating nodes.

Cognitive radio that supports a secondary and opportunistic access to licensed spectrum shows great potential to dramatically improve spectrum utilization. Spectrum sensing performed by secondary users to detect unoccupied spectrum bands, is a key enabling technique for cognitive radio. This paper proposes a truncated sequential spectrum sensing scheme, namely the sequential shifted chi-square test (SSCT). The SSCT has a simple test statistic and does not rely on any deterministic knowledge about primary signals. As figures of merit, the exact false-alarm probability is derived, and the miss-detection probability as well as the average sample number (ASN) are evaluated by using a numerical integration algorithm. Corroborating numerical examples show that, in comparison with fixed-sample size detection schemes such as energy detection, the SSCT delivers considerable reduction on the ASN while maintaining a comparable detection performance.

This paper studies two-user causal cognitive radio channels, in which the secondary user has causal knowledge about the message being sent by the primary user. An inner bound on the capacity region of such channels is established by employing a coding strategy consisting of block Markov superposition and dirty paper encoding, and backward decoding. An illustrative example in the Gaussian case is provided.

The conventional Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC)- multiple-access channel (MAC) duality has previously been applied to solve non-convex BC capacity computation problems. However, this conventional duality approach is applicable only to the case in which the base station (BS) of the BC is subject to a single sum-power constraint. An alternative approach is the minimax duality, established by Yu in the framework of Lagrange duality, which can be applied to solve the per-antenna power constraint case. This paper first extends the conventional BC-MAC duality to the general linear transmit covariance constraint (LTCC) case, and thereby establishes a general BC-MAC duality. This new duality is then applied to solve the BC capacity computation problem with multiple LTCCs. Moreover, the relationship between this new general BC-MAC duality and the minimax duality is also presented, and it is shown that the general BC-MAC duality has a simpler form. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

We propose an efficient common phase error (CPE) mitigation scheme for orthogonal frequency division multiplexing (OFDM) based ultra-wideband (UWB) systems. We consider the phase errors resulting from both residual carrier frequency offset and random phase noise. The CPE estimator employs a pilot-tone based and channel frequency response weighted approach for achieving low-complexity intra-OFDM-symbol estimation. We demonstrate that the error involved in the intra-OFDM-symbol based CPE estimate can be effectively reduced by further introducing an inter-OFDM-symbol smoothing technique, for which a choice can be made between using 1st-order and 2nd-order low-pass filtering by analyzing their respective error reduction performance. Analytical results and numerical examples show the effectiveness of the proposed scheme in different multi-path fading scenarios and signal-to-noise ratio regimes.

The performance of a two-hop amplify-and-forward relay link in flat Rayleigh fading channels is analyzed. Closed-form expressions are obtained for the distribution of the instantaneous signal-to-noise ratio (SNR). An expression for the error probability is presented in general, and the bit-error rate (BER) associated with binary differential PSK (BDPSK) and the symbol-error rate (SER) associated with M-ary amplitude-shift keying (ASK) modulation schemes are derived in particular. These theoretical results are well supported by simulation results. In principle, the analysis approach, which is different from and much simpler than that reported in [9], can be generalized and applied to relay systems that include more hops.

In a cognitive radio (CR) network, cooperative spectrum sensing plays an important role in ensuring the quality of service (QoS) of primary users (PUs) and improving spectral utilization efficiency. Intuitively, the more secondary users (SUs) are involved in sensing, the more sensing accuracy the CR can achieve, whereas the more sensing overhead the SUs consume, the less throughput the CR network can achieve. In this paper, we investigate overhead-throughput tradeoff over fading channels in a cooperative CR network that consists of a number of the SUs employing energy detectors and a single decision fusion center. We propose a design strategy to maximize the throughput of the SU network by choosing appropriate sensing length and the number of the SUs reporting to a decision fusion center. Moreover, we extend our analysis to a two-stage cooperative sensing mechanism where the second-stage fine sensing is triggered whenever any SU reports the presence of a PU after the first-stage detection. Our numerical results show that compared with the single stage sensing, the two-stage sensing scheme achieves higher throughput via the reduction of the probability of false alarm.

Cognitive radio is an emerging technology that shows great promise to dramatically improve the efficiency of spectrum utilization. This paper considers a cognitive radio model, in which the secondary network is allowed to use the radio spectrum concurrently with primary users (PUs) provided that interference from the secondary users (SUs) to the PUs is constrained by certain thresholds. The weighted sum rate maximization problem is studied under interference power constraints and individual transmit power constraints, for a cognitive multiple access channel (C-MAC), in which each SU having a single transmit antenna communicates with the base station having multiple receive antennas. An iterative algorithm is developed to efficiently obtain the optimal solution of the weighted sum rate problem for the C-MAC. It is further shown that the proposed algorithm, although developed for single channel transmission, can be extended to the case of multiple channel transmission. Corroborating numerical examples illustrate the convergence behavior of the algorithm and present comparisons with other existing alternative algorithms.

In a fading environment, the statistical distribution of the mutual information of a multiple-input multiple-output (MIMO) system depends on the joint distribution of the eigenvalues of a Wishart matrix, and is quite complex in general. We obtain here simple expressions for the distributions of the determinant and the trace of a Wishart matrix. Based on the obtained distributions, we derive some simple and tight bounds on the complementary cumulative distribution function (CCDF) of the mutual information of a MIMO system in Rician fading environments. The bounds obtained on the CCDF of mutual information provide further insights into the channel mutual information, and show the effects of the system parameters on the mutual information distribution explicitly. In addition, results for the Rayleigh channels are obtained as a special case.

Maximum likelihood (ML) carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiple-access (OFDMA) uplink with generalized carrier-assignment scheme (GCAS) is a complex multi-parameter estimation problem. The computational complexity of ML solution based on a multi-dimensional exhaustive search is prohibitive. The existing sub-optimal solutions reduce the complexity by replacing the multi-dimensional search with a sequence of single-dimensional searches. However, these solutions suffer from either poor estimation accuracy or being still of fairly high complexity. In this paper, we propose a new approach called divide-and-update frequency estimator (DUFE), for CFO estimation. Compared with the existing approaches, the proposed DUFE has lower computational complexity while maintaining high estimation accuracy similar to that of the exact ML solution. Performance and complexity comparisons are provided, along with numerical results to illustrate the effectiveness of the proposed method.

This paper considers a spectrum sharing based cognitive radio (CR) communication system, which consists of a secondary user (SU) having multiple transmit antennas and a single receive antenna and a primary user (PU) having a single receive antenna. The channel state information (CSI) on the link of the SU is assumed to be perfectly known at the SU transmitter (SU-Tx). However, due to loose cooperation between the SU and the PU, only partial CSI of the link between the SU-Tx and the PU is available at the SU-Tx. With the partial CSI and a prescribed transmit power constraint, our design objective is to determine the transmit signal covariance matrix that maximizes the rate of the SU while keeping the interference power to the PU below a threshold for all the possible channel realizations within an uncertainty set. This problem, termed the robust cognitive beamforming problem, can be naturally formulated as a semi-infinite programming (SIP) problem with infinitely many constraints.We first transform this problem into a second order cone programming (SOCP) problem and then solve it via a standard interior point algorithm. Then, an analytical solution with significantly reduced complexity is developed from a geometric perspective. It is shown that both algorithms yield the same optimal solution. Simulation examples are presented to validate the effectiveness of the proposed algorithms.

In this paper, we consider a cognitive radio (CR) network, in which the unlicensed (secondary) users are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a weighted sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs are subject to not only a sum power constraint but also interference power constraints. We transform this multiconstraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we propose a duality result for the equivalent problem. Exploiting the duality result, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Simulation results are provided to corroborate the effectiveness of the proposed algorithm.

This paper presents an efficient channel estimation scheme for multi-band orthogonal frequency division multiplexing based ultra-wideband communications. The scheme is based on a simple least-square (LS) algorithm, but enhanced with channel frequency response aided decision-directed detection as well as frequency-domain smoothing. The mean-squared error performance and computational complexity of the scheme are investigated. Numerical examples show that the proposed scheme significantly outperforms the conventional LS solutions and performs comparable to the maximum-likelihood solutions.

Cognitive radio is an emerging technology improving the spectrum utilization efficiency in communication systems. In this paper, we are interested in a multiple-input single-output (MISO) based cognitive radio (CR) network where the secondary user (SU) shares the same frequency band with the primary user (PU). It is assumed that the SU transmitter (SU-Tx) has perfect channel information (CSI) from SU-Tx to SU receiver (SU-Rx), but partial CSI from SU-Tx to PU receiver. We propose a robust design method to determine the optimal transmission covariance of SU-Tx to maximize the rate of the SU while keeping the interference power to the PU less than a threshold with high probability. This problem is formulated as a semi-infinite programming (SIP) problem. Two algorithms are proposed to transform this SIP problem into a finite constraint problem, and it is shown that these algorithms obtain the optimal solution. Simulations are presented to validate the effectiveness of the proposed algorithms.

The cognitive radio channel (CRC) refers to a communication model in which two senders attempt to communicate with their respective receivers simultaneously through a common medium, and one of the senders has complete and a priori (non-causal) knowledge about the message being transmitted by the other. A coding scheme that collectively has advantages of cooperative coding, collaborative coding, and dirty paper coding, is developed for such a channel. With resorting to this coding scheme, a new achievable rate region for the CRC is derived, which includes several previously known rate regions. Furthermore, it is demonstrated by Gaussian numerical examples that the new achievable rate region offers strict improvements over the existing results in the high-interference-gain regime.

Maximum likelihood (ML) carrier-frequency offset (CFO) estimation in the uplink of an orthogonal frequency-division multiple-access (OFDMA) system with a generalized carrier- assignment scheme (GCAS) is a complex multiple-parameter estimation problem. The computational complexity of the ML solution based on a multi-dimensional exhaustive search is prohibitive. The existing solutions reduce the complexity by replacing the multidimensional search with a sequence of single-dimensional searches. However, those solutions suffer from either poor estimation accuracy or is still of high complexity. In this paper, we propose a new approach called as the divide-and-update frequency estimator (DUFE), which outperforms the existing solutions in the sense that it has lower computational complexity while maintaining high estimation accuracy similar to the exact ML solution. Performance and complexity comparisons are provided with numerical results to illustrate the effectiveness of the proposed method.

In this paper, we consider a cognitive radio (CR) network in which the unlicensed (secondary) users (SUs) are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a weighted sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs have not only the sum power constraint but also interference constraints. We first transform this multi- constraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we establish a duality result for the equivalent problem. Our duality result can be viewed as an extension of the previously known results, which depend on either a sum power constraint or per-antenna power constraints. Furthermore, we develop an efficient sub-gradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Computer simulations are also provided to corroborate the effectiveness of the proposed algorithm.

In this paper, we consider the problem of spectrum sensing in cognitive radio, where unlicensed (secondary) users are allowed to share the vacant frequency bands from the licensed (primary) users. We propose two cooperation protocols to improve the detection probability compared to an existing protocol. In the first protocol, secondary users with higher detection probability constantly act as relays to help those with lower detection probability, while in the second protocol, the help-oriented users choose to relay signals according to the decision made during the first time slot. Analytical studies are provided to demonstrate the enhanced performance of our proposed protocols. Finally, simulation examples are presented to corroborate our analytical results.

Maximum likelihood (ML) carrier-frequency offset (CFO) estimation in the uplink of an orthogonal frequency- division multiple-access (OFDMA) system with a generalized carrier- assignment scheme (GCAS) is a complex multiple-parameter estimation problem. The inclusion of multiple-input multiple-output (MIMO) processing renders the problem even more complicated. The computational complexity of the ML solution based on a multi-dimensional exhaustive search is prohibitive. In this paper, we propose to decompose the MIMO-OFDMA CFO estimation into a series of multiple-input single-output (MISO) ML estimations, each of which adopts a new iterative approach. The proposed solution has affordable computational complexity while maintaining high estimation accuracy similar to the exact ML solution. Performance and complexity comparisons are provided with numerical results to illustrate the effectiveness of the proposed method.

This paper proposes an efficient phase error suppression technique for multi-band orthogonal frequency division multiplexing (OFDM) based ultra-wideband (UWB) systems. The proposed scheme consists of a clock recovery loop and a common phase error (CPE) mitigation mechanism. The clock recovery loop performs continuously sampling frequency offset (SFO) estimation and two-dimensional (time and frequency) compensation, while the CPE mitigation copes with the phase errors caused by the residual carrier frequency offset and SFO. Both SFO and CPE estimation use the pilot-tone based approaches, each of which employs a robust error reduction scheme and involves neither angle calculation nor division, and thus they are of low-complexity. Corroborating numerical examples show the effectiveness of the proposed solution even under considerably low signal-to-noise ratio multipath channel conditions.

Cognitive radio (CR) has been proposed as a strategy to enhance the spectrum utilization efficiency. In a CR network, the secondary users (SUs) share the same radio spectrum with the primary user (PU) under the constraint that the interference from the SUs to PU is below a certain threshold. In this paper, we consider the single-input multiple-output multiple access channels (SIMO-MAC) for the secondary network, and study the optimal power allocation strategy for maximizing the weighted sum rate of the SIMO-MAC under interference constraints and peak transmit power constraints. Employing decoupling techniques, we develop an iterative algorithm to obtain optimal power allocation for maximizing weighted sum rate. Simulation results are presented to verify the effectiveness of the proposed algorithm.

We derive a decision-feedback channel estimation receiver for independent and non-identically distributed (i.n.d.) Rayleigh fading channels. The receiver has memory to store signals received over the past several symbol intervals, and then use them to adaptively estimate instantaneous channel gains. The receiver adapts its decision rule based on estimates of the varying channel gains, and hence it is partially coherent. We also obtain the bit error rate (BER) of the channel estimation receiver for binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulations. The BER results are obtained in simple forms which explicitly show the effects of system parameters on the BER. In addition, our results are applicable to the cases where all the branches have arbitrary fade rates.

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.

The interference channel with degraded message sets (IC-DMS) refers to a communication model, in which two senders attempt to communicate with their respective receivers simultaneously through a common medium, and one sender has complete and a priori (noncausal) knowledge about the message being transmitted by the other. A coding scheme that collectively has advantages of cooperative coding, collaborative coding, and dirty paper coding, is developed for such a channel. With resorting to this coding scheme, achievable rate regions of the IC-DMS in both discrete memoryless and Gaussian cases are derived. The derived achievable rate regions generally include several previously known rate regions as special cases. A numerical example for the Gaussian case further demonstrates that the derived achievable rate region offers considerable improvements over these existing results in the high-interference-gain regime.

In this paper, the interference channel with common information (ICC), in which two senders need deliver not only private messages but also certain common messages to theircorrespondingreceivers, is investigated. An achievable rate region for such a channel is obtained by applying a superposition coding scheme that consists of successive encoding and simultaneous decoding. It is shown that the derived achievable rate region includes or extends several existing results for the interference channels with or without common information. The rate region is then specialized to a class of ICCs in which one sender has no private information to transmit, and a class of deterministic interference channels with common information (DICCs). In particular, the derived rate region is found to be the capacity region for this class of DICCs. Last, the achievable rate region derived for the discrete memoryless ICC is extended to the Gaussian case, in which a numerical example is provided to illustrate the improvement of our rate region over an existing result.

In this paper, a decode-and-forward (DF) single relay model in a Rayleigh fading environment is considered. The outage probability and ergodic rate for this model are derived. With the objective of either minimizing the outage probability or maximizing the ergodic rate, optimal and approximately optimal transmission strategies are developed through selecting appropriate transmit signaling and/or spatial power allocation. The derived transmission strategies only require the knowledge of the second-order statistics of the channels at the transmitters, which can be readily acquired in practice. Simulation results demonstrate that the optimal transmit signaling and/or spatial power allocation can offer considerable performance improvements over the equal power allocation strategy.

We derive the correct log-likelihood differential detection metric for iterative decoding of LDPC codes transmitted with BDPSK modulation over Rayleigh fading channels. This metric does not require estimation of the amplitudes of the complex channel gains, showing that existing metrics in the literature that require estimation of amplitudes of channel gains are suboptimum. Simulation results confirm that an intentional SNR underestimation further improves BEP.

We consider about a multiple relay system operating under amplify-and-forward (AF) protocol and non-coherent modulation and demodulation. When the relays are under long-term power constraints, a near-maximum likelihood (ML) receiver and a diversity combining receiver are proposed. Both the near- ML receiver and the diversity combining receiver are expressed in simple closed forms and only rely on the second order statistics of the fading coefficients. Moreover, both of the proposed detectors outperform a non-cooperative system which only has a direct link with non-coherent ML detection in a Rayleigh fading environment. However, when the relay output is under a short-term power constraint, the ML detector derived here for the non-coherent AF relay performs the same as the non-cooperative system.

In this paper, we establish the capacity region of a class of deterministic interference channels with common information. For such a class of channels, each sender needs to transmit not only the private information but also certain common information to the corresponding receiver. Moreover, the channel outputs are deterministic with respect to given channel inputs.

In this paper, we study the problem of joint admission control and power allocation for cognitive radio networks. In such a scenario, the quality of service for primary and secondary users is needed to be guaranteed, which can be translated into the following two constraints: the inference temperature constraint for primary users and the minimum signal-to-interference-plus-noise ratio (SINR) constraint for secondary users. Due to the high density or the mobility of the secondary users, not all the secondary users are supportable. The problem of our interest is to select the maximum subset of secondary users given that the above constraints are satisfied. Moreover, because different secondary users have different revenue outputs, the problem becomes how we can find a subset of the secondary users such that the total revenue output of the networks is maximized. It can be shown that finding the optimal removal set is a NP hard problem. Therefore, we transform the original problem into a smooth optimization problem, and solve it by using a gradient descent based algorithm. This algorithm solves the power allocation and admission control jointly, and its superior performance over the existing algorithms is demonstrated through simulations.

In this paper, we derive an optimal spatial power allocation strategy that maximizes the achievable rate of the decode-and-forward (DF) relay channels in a Rayleigh fading environment. Different from prior work, the derived strategy only requires the knowledge of the variances of the channels at the transmitters, which does not require frequent updates. Furthermore, we demonstrate that the optimal spatial power allocation strategy leads considerable performance improvements over the equal power allocation one.

New automatic classification algorithms are proposed for the imperfect rectangular QAM constellation with phase rotation. Our proposed algorithms are developed based on the two-dimensional Radon transform, and can effectively estimate the phase rotation and classify the modulation type of the received signals. Simulation experiments are performed and the results show that our proposed algorithms are successful even when the incoming signals are corrupted by additive white Gaussian noise (AWGN). As compared with the existing classification algorithm, our proposed algorithms can achieve satisfied performance in terms of probability of correct classification (PCC), and are more feasible to be adopted in practice.

In this paper, we employ iteratively decodable codes in a turbolike receiver of a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system. With such a receiver, we compare the decoding complexity and performance of a turbo code with a low- density-parity-check (LDPC) code. For the same level of decoding complexity, we show that LDPC codes perform better than turbo codes in a typical high data rate MIMO-OFDM system.

Application of existing maximum likelihood channel estimation (MLE) in orthogonal frequency division multiplexing (OFDM) systems requires knowledge of the effective length of channel impulse response (ELCIR) for achieving optimum performance. The analysis shows that the mean-squared error (MSE) is linearly related to ELCIR. Tracking the variation in ELCIR is thus very important for conventional MLE. But, incorporating a run-time update of ELCIR into the ML estimator turns out to be computationally expensive. Therefore, a modified ML channel estimator, which combines the ML estimation with a frequency-domain smoothing technique, is proposed. The proposed method introduces no extra complexity, and its performance has been proved using theoretical analysis and simulations to be robust to variation in ELCIR. Numerical results are provided to show the effectiveness of the proposed estimator under time-invariant and time-variant channel conditions.

Signal detector is a key element in a multiple-input multiple-output (MIMO) wireless communication receiver. It has been well demonstrated that nonlinear tree search MIMO detectors can achieve near-optimum detection performance, nevertheless their efficient high-speed VLSI implementations are not trivial. For example, the hardware design of hard- or soft- output detectors for a 4 times 4 MIMO system with 64 quadrature amplitude modulation (QAM) still remains missing in the open literature. As an attempt to tackle this challenge, this paper presents an implementation-oriented breadth-first tree search MIMO detector design solution. The key is to appropriately modify the conventional breadth-first tree search detection algorithm in order to largely improve the suitability for efficient hardware implementation, while maintaining good detection performance. To demonstrate the effectiveness of the proposed design solution, using 0.13-mum CMOS standard cell and memory libraries, we designed a soft-output signal detector for 4 times 4 MIMO with 64-QAM. With the silicon area of about 31 mm2, the detector can achieve above 100 Mb/s and realize the performance very close to that of the sphere decoding algorithm

The distribution of the capacity of multiple input multiple output (MIMO) fading depends on the joint distribution of the eigenvalues of a Wishart matrix, and is quite complex in general. We obtain here simple expressions for the distributions of the determinant of a Wishart matrix. Based on the distributions of the determinant and the trace of the Wishart matrix, we derive some simple and tight bounds on the complementary cumulative distribution function (CCDF) of the capacity of MIMO Rayleigh fading channels. The new bounds on capacity CCDF provide further insights into the channel capacity, and show the effects of the system parameters on the capacity distribution explicitly. These bounds can be used to evaluate the mean capacity as well.

Orthogonal frequency-division multiplexing (OFDM) converts a frequency-selective fading channel into parallel flat-fading subchannels, thereby simplifying channel equalization and symbol decoding. However, OFDM's performance suffers from the loss of multipath diversity, and the inability to guarantee symbol detectability when channel nulls occur. We introduce a linear constellation precoded OFDM for wireless transmissions over frequency-selective fading channels. Exploiting the correlation structure of subchannels and choosing system parameters properly, we first perform an optimal subcarrier grouping to divide the set of subchannels into subsets. Within each subset, a linear constellation-specific precoder is then designed to maximize both diversity and coding gains. While greatly reducing the decoding complexity and simplifying the precoder design, subcarrier grouping enables the maximum possible diversity and coding gains. In addition to reduced complexity, the proposed system guarantees symbol detectability regardless of channel nulls, and does not reduce the transmission rate. Analytic evaluation and corroborating simulations reveal its performance merits.

We present a unified approach to designing space-time (ST) block codes using linear constellation precoding (LCP). Our designs are based either on parameterizations of unitary matrices, or on algebraic number-theoretic constructions. With an arbitrary number of N/sub t/ transmit- and N/sub r/ receive-antennas, ST-LCP achieves rate 1 symbol/s/Hz and enjoys diversity gain as high as N/sub t/N/sub r/ over (possibly correlated) quasi-static and fast fading channels. As figures of merit, we use diversity and coding gains, as well as mutual information of the underlying multiple-input-multiple-output system. We show that over quadrature-amplitude modulation and pulse-amplitude modulation, our LCP achieves the upper bound on the coding gain of all linear precoders for certain values of N/sub t/ and comes close to this upper bound for other values of N/sub t/, in both correlated and independent fading channels. Compared with existing ST block codes adhering to an orthogonal design (ST-OD), ST-LCP offers not only better performance, but also higher mutual information for N/sub t/>2. For decoding ST-LCP, we adopt the near-optimum sphere-decoding algorithm, as well as reduced-complexity suboptimum alternatives. Although ST-OD codes afford simpler decoding, the tradeoff between performance and rate versus complexity favors the ST-LCP codes when N/sub t/, N/sub r/, or the spectral efficiency of the system increase. Simulations corroborate our theoretical findings.

This paper proposes novel space-time-frequency (STF) block coding for multi-antenna OFDM transmissions over frequency-selective Rayleigh fading channels. Incorporating subcarrier grouping and choosing appropriate system parameters, we first convert our system into a set of group STF (GSTF) systems. This enables simplification of STF block coding within each GSTF system. We derive design criteria for STF block coding, and exploit existing ST coding techniques to construct STF block codes. The resulting codes are shown capable of achieving both maximum diversity and coding gains, while affording low-complexity decoding. The performance merits of our design is confirmed by corroborating simulations, and compared with existing alternatives.

A novel space-time-frequency (STF) trellis coding scheme is developed for multi-antenna OFDM transmissions over frequency-selective Rayleigh fading channels. Incorporating subcarrier grouping and choosing appropriate system parameters, we first convert our system into a set of group STF (GSTF) systems. This enables simplification of STF block coding within each GSTF system. We derive design criteria for STF trellis coding, and exploit existing ST trellis coding techniques to construct STF trellis codes. The resulting codes are shown capable of achieving maximum diversity gains, while affording low-complexity decoding. The performance merits of our design is confirmed by corroborating simulations, and compared with existing alternatives.

Recent theoretical and experimental studies have shown that with affordable complexity, layered space-time (LST) transmissions can attain very high spectral efficiency in a rich-scattering environment. In this paper, we propose a novel high rate linearly precoded LST system, which allows for any number of transmit and receive antennas, and offers flexibility in trading performance with bandwidth efficiency and decoding complexity. Even with sub-optimum decoding, the system enjoys considerable transmit diversity gains. Its superior performance over existing uncoded V-BLAST and linear dispersion (LD) codes is confirmed by simulations.

We apply algebraic number theoretic tools to designing linear space-time constellation-rotating (ST-CR) block codes. With an arbitrary number of M transmit- and N receive-antennas, our ST-CR designs achieve a rate of 1 symbol/second and enjoy maximum diversity gains MN over quasi-static fading channels. When M is an Euler number, /spl phi/(P) for P/spl ne/0 (mod 4), or, when M=2/sup m/ for some positive integer m, the designed ST-CR precoders also maximize coding gains over QAM constellations. When M takes other integer values, we construct a method to design precoders with large coding gains that can be computed explicitly. Simulations corroborate our theoretical findings.

We present a unified approach to constructing linear space-time (ST) block codes based on unitary constellation-rotating (ST-CR) precoders. We show that with an arbitrary number of M-transmit and N-receive antennas, ST-CR precoders achieve 1 symbol/sec rate and enjoy maximum diversity gain MN over both quasi-static and fast fading channels. We also compare real with complex rotations to delineate the tradeoff between performance and complexity. Based on a simplified decoder, we study diversity and coding gains as well as information-theoretic aspects of the proposed ST-CR scheme. Compared with ST orthogonally designed (ST-OD) codes, ST-CR precoding provides larger coding gain and maximum mutual information. Though ST-OD codes afford simpler decoding, the tradeoff between performance and rate versus complexity favors the ST-CR codes when M, N or the spectral efficiency of the system increase.

The average bit error rate (BER) performance of M-ary orthogonal noncoherent frequency-shift-keying (FSK) over frequency-selective Nakagami-m fading channels with an exponentially decaying power delay profile is studied. Exact analytical results when suboptimum square-law combining type of reception is used are first presented. Metrics and performance of maximum likelihood (ML) type of receivers are then discussed. The performance results of optimum ML receivers are obtained via extensive Monte-Carlo computer simulations, and are compared with the the analytical average BER results obtained for the suboptimum square-law combining receivers. Numerical and simulation results confirm that the optimum ML receivers outperform square law receivers for strictly positive values of the power decay factor and do not suffer from the well-known "noncoherent combining loss" which affects the performance of square law receivers at low signal-to-noise ratio values. In addition, these results show that suboptimum receivers perform actually quite well with respect to their optimum counterpart in general but the performance difference between them becomes more significant as the Nakagami (1960) fading parameter and/or the number of diversity paths increases.