A dynamic spectrum access scheme for cognitive radio networks

Abstract—In this paper, the dynamic spectrum access problem for cognitive radio (CR) networks is formulated as maximizing the sum channel capacity while satisfying the power budgets of individual secondary user radios as well as the SINR constraints on both the secondary and primary users. By applying the Karush-Kuhn-Tucker theorem, we derive a waterfilling soluton. An iterative water-filling algorithm is proposed for implementing joint channel and power allocation in a dynamically changing set of available channels. The proposed algorithm has a complexity that increases linearly with both the number of channels and the number of users

Cooperative power control approaches towards fair radio resource allocation for wireless network

Performance optimization in wireless networks is a complex problem due to variability and dynamics in network topology and density, traffic patterns, mutual interference, channel uncertainties, etc. Opportunistic or selfish approaches may result in unbalanced allocation of channel capacity where particular links are overshadowed. This degrades overall network fairness and hinders a multi-hop communication by creating bottlenecks. A desired approach should allocate channel capacity proportionally to traffic priority in a cooperative manner. This work consists of two chapters that address the fairness share problem in wireless ad hoc, peer-to-peer networks and resource allocation within Cognitive Radio network. In the first paper, two fair power control schemes are proposed and mathematically analyzed. The schemes dynamically determine the viable resource allocation for a particular peer-to-peer network. In contrast, the traditional approaches often derive such viable capacity for a class of topologies. Moreover, the previous power control schemes assume that the target capacity allocation, or signal-to-interference ratio (SIR), is known and feasible. This leads to unfairness if the target SIR is not viable. The theoretical and simulation results show that the capacity is equally allocated for each link in the presence of radio channel uncertainties. In the second paper, based on the fair power control schemes, two novel power control schemes and an integrated power control scheme are proposed regarding the resource allocation for Cognitive Radio network to increase the efficiency of the resource while satisfying the Primary Users\u27 Quality of Service. Simulation result and tradeoff discussion are given --Abstract, page iv

Pilot Aided Transmissions Technique to Achieve Optimal Effective Capacity Over Imperfect Channel Estimation in Cognitive Radio Networks

In cognitive radio networks, a secondary user (SU) can share the same frequency band with the primary user (PU) as long as the interference introduced to the later is below a predefined threshold. In this paper, the transmission performance in cognitive radio networks is studied assuming imperfect channel estimation, and taking quality of service (QoS) constraints into consideration. It is assumed that the cognitive transmitter can perform channel estimation and send the data at two different rates and power levels depending on the activity of the primary users. The existence of the primary user can be detected by channel sensing. A two-state Markov chain process is used to model the existence of the primary users. The cognitive transmission is also configured as a state transition model depending on whether the rates are higher or lower than the instantaneous rates values. This paper studies the maximum capacity of the cognitive user under the delay constraint. We use the new metric concept of effective capacity of the channel and introduce an optimization problem for rate and power allocation under interference power constraints. An numerical example illustrates the average effective capacity optimization and the impact of other system parameters.&nbsp

Spectral Efficiency of Multi-User Adaptive Cognitive Radio Networks

In this correspondence, the comprehensive problem of joint power, rate, and subcarrier allocation have been investigated for enhancing the spectral efficiency of multi-user orthogonal frequency-division multiple access (OFDMA) cognitive radio (CR) networks subject to satisfying total average transmission power and aggregate interference constraints. We propose novel optimal radio resource allocation (RRA) algorithms under different scenarios with deterministic and probabilistic interference violation limits based on a perfect and imperfect availability of cross-link channel state information (CSI). In particular, we propose a probabilistic approach to mitigate the total imposed interference on the primary service under imperfect cross-link CSI. A closed-form mathematical formulation of the cumulative density function (cdf) for the received signal-to-interference-plus-noise ratio (SINR) is formulated to evaluate the resultant average spectral efficiency (ASE). Dual decomposition is utilized to obtain sub-optimal solutions for the non-convex optimization problems. Through simulation results, we investigate the achievable performance and the impact of parameters uncertainty on the overall system performance. Furthermore, we present that the developed RRA algorithms can considerably improve the cognitive performance whilst abide the imposed power constraints. In particular, the performance under imperfect cross-link CSI knowledge for the proposed `probabilistic case' is compared to the conventional scenarios to show the potential gain in employing this scheme

Outage Capacity and Optimal Transmission for Dying Channels

In wireless networks, communication links may be subject to random fatal impacts: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably the information can be collected over attacked links. For a single point-to-point channel subject to a random attack, named as a \emph{dying channel}, we model it as a block-fading (BF) channel with a finite and random delay constraint. First, we define the outage capacity as the performance measure, followed by studying the optimal coding length $K$ such that the outage probability is minimized when uniform power allocation is assumed. For a given rate target and a coding length $K$, we then minimize the outage probability over the power allocation vector \mv{P}_{K}, and show that this optimization problem can be cast into a convex optimization problem under some conditions. The optimal solutions for several special cases are discussed. Furthermore, we extend the single point-to-point dying channel result to the parallel multi-channel case where each sub-channel is a dying channel, and investigate the corresponding asymptotic behavior of the overall outage probability with two different attack models: the independent-attack case and the $m$-dependent-attack case. It can be shown that the overall outage probability diminishes to zero for both cases as the number of sub-channels increases if the \emph{rate per unit cost} is less than a certain threshold. The outage exponents are also studied to reveal how fast the outage probability improves over the number of sub-channels.Comment: 31 pages, 9 figures, submitted to IEEE Transactions on Information Theor