This paper adopts a multiobjective optimization (MOOP) approach to investigate the optimal link adaptation problem of orthogonal frequency division multiplexing (OFDM)- based cognitive radio (CR) systems, where secondary users (SUs) can opportunistically access the spectrum of primary users (PUs). For such a scenario, we solve the problem of jointly maximizing the CR system throughput and minimizing its transmit power, subject to constraints on both SU and PUs. The optimization problem imposes predefined interference thresholds for the PUs, guarantees the SU quality of service in terms of a maximum bit-error-rate (BER), and satisfies a transmit power budget and a maximum number of allocated bits per subcarrier. Unlike most of the work in the literature that considers perfect SU spectrum sensing capabilities, the problem formulation takes into account errors due to imperfect sensing of the PUs bands. Closed-form expressions are obtained for the optimal bit and power allocations per SU subcarrier. Simulation results illustrate the performance of the proposed algorithm and demonstrate the superiority of the MOOP approach when compared to single optimization approaches presented in the literature, without additional complexity. Furthermore, results show that the interference thresholds at the PUs receivers can be severely exceeded due to the perfect spectrum sensing assumption or due to partial channel information on links between the SU and the PUs receivers. Additionally, the results show that the performance of the proposed algorithm approaches that of an exhaustive search for the discrete optimal allocations with a significantly reduced computational effort.
In this paper, we propose a framework for energy efficient resource allocation in multiuser localized SC-FDMA with synchronous HARQ constraints. Resource allocation is formulated as a two-stage problem where resources are allocated in both time and frequency. The impact of retransmissions on the time-frequency problem segmentation is handled through the use of a novel block scheduling interval specifically designed for synchronous HARQ to ensure uplink users do not experience ARQ blocking. Using this framework, we formulate the optimal margin adaptive allocation problem, and based on its structure, we propose two suboptimal approaches to minimize average power allocation required for resource allocation while attempting to reduce complexity. Results are presented for computational complexity and average power allocation relative to system complexity and data rate, and comparisons are made between the proposed optimal and suboptimal approaches
The development of evolutionary algorithms (EAs), such as genetic algorithms (GAs), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), and differential evolution algorithms (DEAs), have stimulated wide interests in the communication research community. However, the quantitative performance-versus-complexity comparison of GA, RWBS, PSO, and DEA techniques applied to the joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding in the context of orthogonal frequency-division multiplexing/space-division multiple-access systems is a challenging problem, which has to consider both the CE problem formulated over a continuous search space and the MUD optimization problem defined over a discrete search space. We investigate the capability of the GA, RWBS, PSO, and DEA to achieve optimal solutions at an affordable complexity in this challenging application. Our study demonstrates that the EA-assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér–Rao lower bound of the optimal CE and the bit error ratio (BER) performance of the idealized optimal maximum-likelihood (ML) turbo MUD/decoder associated with perfect channel state information, respectively, despite imposing only a fraction of the idealized turbo ML-MUD/decoder’s complexity.
We consider the joint subchannel allocation and power control problem for orthogonal frequency-division multipleaccess (OFDMA) femtocell networks in this paper. Specifically, we are interested in the fair resource-sharing solution for users in each femtocell that maximizes the total minimum spectral efficiency of all femtocells subject to protection constraints for the prioritized macro users. Toward this end, we present the mathematical formulation for the uplink resource-allocation problem and propose an optimal exhaustive search algorithm. Given the exponential complexity of the optimal algorithm, we develop a distributed and low-complexity algorithm to find an efficient solution for the problem. We prove that the proposed algorithm converges and we analyze its complexity. Then, we extend the proposed algorithm in three different directions, namely, downlink context, resource allocation with rate adaption for femto users, and consideration of a hybrid access strategy where some macro users are allowed to connect with nearby femto base stations (FBSs) to improve the performance of the femto tier. Finally, numerical results are presented to demonstrate the desirable performance of the proposed algorithms.
—In a random sector graph, the presence of an edge between two nodes depends on their distance and spatial orientation. This kind of graph is widely used for modeling wireless sensor networks where communication among nodes is directional. In particular, it is applied to describe both the radio frequency transmission among nodes equipped with directional antennas and the line-of-sight transmission in optical sensor networks. Important properties of a wireless sensor network, such as connectivity and coverage, can be investigated by studying the degree of the nodes of the corresponding random sector graph. In detail, the in-degree value represents the number of incoming edges, whereas the out-degree considers the outgoing edges. This paper mathematically characterizes the average degree of a random sector graph and the probability distributions of the in-degree and out-degree of the nodes. Furthermore, it derives the coverage probability of the network. All the formulas are validated through extensive simulations, showing an excellent match between theoretical results and experimental data
Despite many attractive features of an orthogonal frequency-division multiplexing (OFDM) system, the signal detection in an OFDM system over multipath fading channels remains a challenging issue, particularly in a relatively low signalto-noise ratio (SNR) scenario. This paper presents an iterative synchronization-assisted OFDM signal detection scheme for cognitive radio (CR) applications over multipath channels in low-SNR regions. To detect an OFDM signal, a log-likelihood ratio (LLR) test is employed without additional pilot symbols using a cyclic prefix (CP). Analytical results indicate that the LLR of received samples at a low SNR can be approximated by their log-likelihood (LL) functions, thus allowing us to estimate synchronization parameters for signal detection. The LL function is complex and depends on various parameters, including correlation coefficient, carrier frequency offset (CFO), symbol timing offset, and channel length. Decomposing a synchronization problem into several relatively simple parameter estimation subproblems eliminates a multidimensional grid search. An iterative scheme is also devised to implement a synchronization process. Simulation results confirm the effectiveness of the proposed detector.
This paper presents small world in motion (SWIM), a new mobility model for ad hoc networking. SWIM is relatively simple, is easily tuned by setting just a few parameters, and generates traces that look real—synthetic traces have the same statistical properties of real traces in terms of intercontact times, contact duration, and frequency among node couples. Furthermore, it generates social behavior among nodes and models networks with complex social communities as the ones observed in the real traces. SWIM shows experimentally and theoretically the presence of the power-law and exponential decay dichotomy of intercontact times, and, most importantly, our experiments show that predicts very accurately the performance of forwarding protocols for PSNs like Epidemic, Delegation, Spray&Wait, and more complex, social-based ones like BUBBLE. Moreover, we propose a methodology to assess protocols on model with a large number of nodes. To the best of our knowledge, this is the first such study. Scaling of mobility models is a fundamental issue, yet never considered in the literature. Thanks to SWIM, here we present the first analysis of the scaling capabilities of Epidemic Forwarding, Delegation Forwarding, Spray&Wait, and BUBBLE
Iterative channel estimation (ICE) usually exploits soft information of unknown data symbols as references to improve estimation performance. This paper investigates ICE for orthogonal frequency division multiplexing (OFDM) over wireless channels. The optimum ICE is derived in terms of maximum a posteriori (MAP) criterion, which can be solved using fixedpoint iteration (FPI). Furthermore, the derived MAP ICE is closely related to the well-known expectation-maximization (EM) estimation. We also demonstrate that the MAP ICE converges within only one step when the signal-to-noise ratio (SNR) is large through analysis and simulation results.
—In multi-cellular WiMAX systems based on orthogonal frequency-division multiple-access (OFDMA), the training preamble is chosen from a set of known sequences so as to univocally identify the transmitting base station. Therefore, in addition to timing and frequency synchronization, preamble index identification is another fundamental task that a mobile terminal must successfully complete before establishing a communication link with the base station. In this work we investigate the joint maximum likelihood (ML) estimation of the carrier frequency offset (CFO) and preamble index in a multicarrier system compliant with the WiMAX specifications, and derive a novel expression of the relevant Cramer-Rao bound (CRB). Since the exact ML solution is prohibitively complex in its general formulation, suboptimal algorithms are developed which can provide a reasonable trade-off between estimation accuracy and processing load. Specifically, we show that the fractional CFO can be recovered by combining the ML estimator with an existing algorithm that attains the CRB in all practical scenarios. The integral CFO and preamble index are subsequently retrieved by a suitable approximation of their joint ML estimator. Compared to existing alternatives, the resulting scheme exhibits improved accuracy and reduced sensitivity to residual timing errors. The price for these advantages is a certain increase of the system complexity.
Space–frequency block coding with orthogonal frequency-division multiplexing (SFBC-OFDM) suffers from the effect of intercarrier interference (ICI) in doubly selective channels. In this paper, a scheme is proposed in which windowing is applied to the received signal to reduce the effect of ICI to a limited number of neighboring subcarriers. The subcarriers holding the SFBC components of each codeword are separated by a number of subcarriers larger than the ICI range, and hence, they do not interfere with each other. To preserve the structure of the SFBC, the separation between the codeword components is also selected within the coherence bandwidth of the channel. As a result, the diversity gain of the SFBC is preserved. By proper selection of the pilot locations, each OFDM symbol can be divided into subsymbols that can be decoded independently. We show that the proposed windowing technique allows the use of decision feedback equalization to estimate the data transmitted in each subsymbol with low complexity. Simulation results are presented showing the ability of the proposed scheme to significantly improve the performance of SFBC-OFDM and preserve its diversity gain.
in Spectrum Sharing System
Due to limited cooperation between the primary users and the secondary users (SUs) in practical spectrum sharing systems, the secondary transmitters and receivers are assumed to have partial channel state information related to the primary receiver. Under such an assumption, this work investigates power allocation strategies for the SUs subject to an outage probability constraint on the primary transmission and a peak transmit power constraint on the secondary transmission. The challenge lies in the non-convexity of the outage probability constraint. Firstly, we prove that strong duality holds and that the Karush-Kuhn-Tucker (KKT) conditions are necessary for optimality. The optimal solution is then derived by addressing the optimality issues of the KKT solutions. Secondly, in order to further reduce the algorithmic complexity, two suboptimal strategies are proposed. The first one is designed based on several simplifications of the optimal strategy. The second one is derived from the convex relaxation of the non-convex primal problem, which corresponds to the problem with the conventional interference temperature constraint. The performance for both suboptimal strategies is derived in closed form. All proposed strategies are shown to outperform non-adaptive power transmission. The near-optimality of the two suboptimal strategies is also validated, in particular for the first one.
Carrier frequency offset (CFO) and phase noise (PN) are major oscillator impairments in direct-conversion transceivers, and single-carrier frequency-division multiple access (SC-FDMA) is the uplink transmission scheme in the Long-Term Evolution (LTE) standard. We derive a new analytical expression for the normalized mean square error (NMSE) in asynchronous SC-FDMA systems under CFO and joint transmit–receive PN. The derived NMSE expression reveals an interesting cross-layer relationship between the subcarrier mapping scheme at the medium-access-control layer and the immunity to CFO and PN at the physical layer. Furthermore, we propose an iterative reducedcomplexity joint decoding and PN compensation scheme that does not require any pilots in PN tracking and exploits the low-pass nature of the PN process without assuming a specific PN model. Simulation results show the effectiveness of our proposed digital baseband compensation scheme in PN mitigation.
So far, complex valued orthogonal codes have been used differentially in cooperative broadband networks. These codes however achieve less than unitary code rate when utilized in cooperative networks with more than two relays. Therefore, the main challenge is how to construct unitary rate codes for non-coherent cooperative broadband networks with more than two relays while exploiting the achievable spatial and frequency diversity. In this paper, we extend full rate quasi-orthogonal codes to differential cooperative broadband networks where channel information is unavailable. From this, we propose a generalized differential distributed quasi-orthogonal space-frequency coding (DQSFC) protocol for cooperative broadband networks. Our proposed scheme is able to achieve full rate, and full spatial and frequency diversity in cooperative networks with any number of relays. Through pairwise error probability analysis we show that the diversity gain of our scheme can be improved by appropriate code construction and sub-carrier allocation. Based on this, we derive sufficient conditions for the proposed code structure at the source node and relay nodes to achieve full spatial and frequency diversity.
We propose a training sequence that can be used at the handshaking stage for multi-cell networks. The proposed sequence is theoretically proved to enjoy several nice properties including constant amplitude, zero autocorrelation, and orthogonality in multipath channels. Moreover, the analytical results show that the proposed sequence can greatly reduce the multi-cell interference (MCI) induced by carrier frequency offset (CFO) to a negligible level. Therefore, the CFO estimation algorithms designed for single-user or single-cell environments can be slightly modified, and applied in multi-cell environments; an example is given for showing how to modify the estimation algorithms. Consequently, the computational complexity can be dramatically reduced. Simulation results show that the proposed sequences and the CFO estimation algorithms outperform conventional schemes in multi-cell environments.
This paper presents a spectrum monitoring algorithm for Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radios by which the primary user reappearance can be detected during the secondary user transmission. The proposed technique reduces the frequency with which spectrum sensing must be performed and greatly decreases the elapsed time between the start of a primary transmission and its detection by the secondary network. This is done by sensing the change in signal strength over a number of reserved OFDM sub-carriers so that the reappearance of the primary user is quickly detected. Moreover, the OFDM impairments such as power leakage, Narrow Band Interference (NBI), and Inter-Carrier Interference (ICI) are investigated and their impact on the proposed technique is studied. Both analysis and simulation show that the energy ratio algorithm can effectively and accurately detect the appearance of the primary user. Furthermore, our method achieves high immunity to frequency-selective fading channels for both single and multiple receive antenna systems, with a complexity that is approximately twice that of a conventional energy detector.