Doha Hamza

An Optimal Probabilistic Multiple-Access Scheme for Cognitive Radios

We study a time-slotted multiple-access system with a primary user (PU) and a secondary user (SU) sharing the same channel resource. The SU senses the channel at the beginning of the slot. If found free, it transmits with probability 1. If busy, it transmits with a certain access probability that is a function of its queue length and whether it has a new packet arrival. Both users, i.e., the PU and the SU, transmit with a fixed transmission rate by employing a truncated channel inversion power control scheme. We consider the case of erroneous sensing. The goal of the SU is to optimize its transmission scheduling policy to minimize its queueing delay under constraints on its average transmit power and the maximum tolerable primary outage probability caused by the miss detection of the PU. We consider two schemes regarding the secondarys reaction to transmission errors. Under the so-called delay-sensitive (DS) scheme, the packet received in error is removed from the queue to minimize delay, whereas under the delay-tolerant (DT) scheme, the said packet is kept in the buffer and is retransmitted until correct reception. Using the latter scheme, there is a probability of buffer loss that is also constrained to be lower than a certain specified value. We also consider the case when the PU maintains an infinite buffer to store its packets. In the latter case, we modify the SU access scheme to guarantee the stability of the PU queue. We show that the performance significantly changes if the realistic situation of a primary queue is considered. In all cases, although the delay minimization problem is nonconvex, we show that the access policies can be efficiently obtained using linear programming and grid search over one or two parameters.

Impact of sensing errors on the queueing delay and transmit power in cognitive radio access

We study a multiple-access system with a primary user (PU) and a secondary user (SU) utilizing the same frequency band and communicating with a common receiver. Both users transmit with a fixed transmission rate by employing a channel inversion power control scheme. The SU transmits with a certain probability that depends on the sensing outcome, its queue length and whether it has a new packet arrival. We consider the case of erroneous sensing. The goal of the SU is to find the optimal transmission scheduling policy so as to minimize its queueing delay under constraints on its average transmit power and the maximum tolerable primary outage probability caused by miss-detection. The access probabilities are obtained efficiently using linear programming.

Wideband Spectrum Sensing Order for Cognitive Radios with Sensing Errors and Channel SNR Probing Uncertainty

A secondary user (SU) seeks to transmit by sequentially sensing statistically independent primary user (PU) channels. If a channel is sensed free, it is probed to estimate the signal-to-noise ratio between the SU transmitter-receiver pair over the channel. We jointly optimize the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order under imperfect synchronization between the PU and the SU. The sensing and probing times and the decision threshold are assumed to be the same for all channels. We maximize a utility function related to the SU throughput under the constraint that the collision probability with the PU is kept below a certain value and taking sensing errors into account. We illustrate the optimal policy and the variation of SU throughput with various system parameters.

Throughput maximization for cognitive radio networks using active cooperation and superposition coding

We propose a three-message superposition coding scheme in a cognitive radio relay network exploiting active cooperation between primary and secondary users. The primary user is motivated to cooperate by substantial benefits it can reap from this access scenario. Specifically, the time resource is split into three transmission phases. The first two phases are dedicated to primary communication, while the third phase is for the secondarys transmission. We formulate two throughput maximization problems for the secondary network subject to primary user rate constraints and per-node power constraints with respect to the time durations of primary transmission and the transmit power of the primary and the secondary users. The first throughput maximization problem assumes a partial power constraint such that the secondary power dedicated to primary cooperation, i.e. for the first two communication phases, is fixed apriori. In the second throughput maximization problem, a total power constraint is assumed over the three phases of communication. The two problems are difficult to solve analytically when the relaying channel gains are strictly greater than each other and strictly greater than the direct link channel gain. However, mathematically tractable lowerbound and upperbound solutions can be attained for the two problems. For both problems, by only using the lowerbound solution, we demonstrate significant throughput gains for both the primary and the secondary users through this active cooperation scheme. We find that most of the throughput gains come from minimizing the second phase transmission time since the secondary nodes assist the primary communication during this phase. Finally, we demonstrate the superiority of our proposed scheme compared to a number of reference schemes that include best relay selection, dual-hop routing, and an interference channel model.

Secondary access based on sensing and primary ARQ feedback in spectrum sharing systems

In the context of primary/secondary spectrum sharing, we propose a randomized secondary access strategy with access probabilities that are a function of both the primary automatic repeat request (ARQ) feedback and the spectrum sensing outcome. The primary terminal operates in a time slotted fashion and is active only when it has a packet to send. The primary receiver can send a positive acknowledgment (ACK) when the received packet is decoded correctly. Lack of ARQ feedback is interpreted as erroneous reception or inactivity. We call this the explicit ACK scheme. The primary receiver may also send a negative acknowledgment (NACK) when the packet is received in error. Lack of ARQ feedback is interpreted as an ACK or no-transmission. This is called the explicit NACK scheme. Under both schemes, when the primary feedback is interpreted as a NACK, the secondary user assumes that there will be retransmission in the next slot and accesses the channel with a certain probability. When the primary feedback is interpreted as an ACK, the secondary user accesses the channel with either one of two probabilities based on the sensing outcome. Under these settings, we find the three optimal access probabilities via maximizing the secondary throughput given a constraint on the primary throughput. We compare the performance of the explicit ACK and explicit NACK schemes and contrast them with schemes based on either sensing or primary ARQ feedback only.

Throughput maximization over temporally correlated fading channels in cognitive radio networks

We consider a primary link and a secondary link, each composed of a transmitter and a receiver. The primary channel and the channel between the secondary transmitter and the primary receiver follow a first-order Markov model for channel variation over time. Under this assumption of temporal correlation and via exploiting the channel state information (CSI) feedback, we pose the cognitive power control problem as the maximization of secondary throughput subject to a constraint on the primary outage. To solve this problem, we assume that the primary transmitter sends with a constant-rate and with a constant-power, and we consider two types of feedback: perfect delayed CSI and one-bit automatic repeat request (ARQ) CSI. To reduce the computational complexity in the case of ARQ-CSI, we reformulate the cognitive power control problem as the maximization of the weighted sum of primary and secondary throughput which we solve optimally for delayed-CSI and greedily for ARQ-CSI. We also solve the cognitive power control problem for a constant-power variablerate primary link where the primary transmitter exploits the channel temporal correlation. In this scheme, we only consider a temporally correlated primary link and perfect delayed CSI. We solve the weighted sum throughput maximization problem under two scenarios. In the first, the primary rate is adapted without considering the secondary link. In the second, the primary rate and secondary power are determined simultaneously assuming a central controller.

On the ARQ protocols over the Z-interference channels: Diversity-multiplexing-delay tradeoff

We characterize the achievable three-dimensional tradeoff between diversity, multiplexing, and delay of the single antenna Automatic Retransmission reQuest (ARQ) Z-interference channel. Non-cooperative and cooperative ARQ protocols are adopted under these assumptions. Considering no cooperation exists, we study the achievable tradeoff of the fixed-power split Han-Kobayashi (HK) approach. Interestingly, we demonstrate that if the second user transmits the common part only of its message in the event of its successful decoding and a decoding failure at the first user, communication is improved over that achieved by keeping or stopping the transmission of both the common and private messages. Under cooperation, two special cases of the HK are considered for static and dynamic decoders. The difference between the two decoders lies in the ability of the latter to dynamically choose which HK special-case decoding to apply. Cooperation is shown to dramatically increase the achievable first user diversity.

User Matching with Relation to the Stable Marriage Problem in Cognitive Radio Networks

We consider a network comprised of multiple primary users (PUs) and multiple secondary users (SUs), where the SUs seek access to a set of orthogonal channels each occupied by one PU. Only one SU is allowed to coexist with a given PU. We propose a distributed matching algorithm to pair the network users, where a Stackelberg game model is assumed for the interaction between the paired PU and SU. The selected secondary is given access in exchange for monetary compensation to the primary. The PU optimizes the interference price it charges to a given SU and the power allocation to maintain communication. The SU optimizes its power demand so as to maximize its utility. Our algorithm provides a unique stable matching. Numerical results indicate the advantage of the proposed algorithm over other reference schemes.

A blind matching algorithm for cognitive radio networks

We consider a cognitive radio network where secondary users (SUs) are allowed access time to the spectrum belonging to the primary users (PUs) provided that they relay primary messages. PUs and SUs negotiate over allocations of the secondary power that will be used to relay PU data. We formulate the problem as a generalized assignment market to find an epsilon pairwise stable matching. We propose a distributed blind matching algorithm (BLMA) to produce the pairwise-stable matching plus the associated power allocations. We stipulate a limited information exchange in the network so that agents only calculate their own utilities but no information is available about the utilities of any other users in the network. We establish convergence to epsilon pairwise stable matchings in finite time. Finally we show that our algorithm exhibits a limited degradation in PU utility when compared with the Pareto optimal results attained using perfect information assumptions.