M. Cenk Gursoy

Error Rate Analysis of Cognitive Radio Transmissions with Imperfect Channel Sensing

In this paper, error rate performance of cognitive radio transmissions is studied in the presence of imperfect channel sensing decisions. It is assumed that cognitive users first perform channel sensing, albeit with possible errors. Then, depending on the sensing decisions, they select the transmission energy level and employ MI × MQ rectangular quadrature amplitude modulation (QAM) for data transmission over a fading channel. In this setting, the optimal decision rule is formulated under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading. It is shown that the thresholds for optimal detection at the receiver are the midpoints between the signals under any sensing decision. Subsequently, minimum average error probability expressions for M-ary pulse amplitude modulation (M-PAM) and MI × MQ rectangular QAM transmissions attained with the optimal detector are derived. The effects of imperfect channel sensing decisions on the average symbol error probability are analyzed.

Wireless Throughput and Energy Efficiency With Random Arrivals and Statistical Queuing Constraints

Throughput and energy efficiency in fading channels are studied in the presence of randomly arriving data and statistical queuing constraints. In particular, Markovian arrival models, including discrete-time Markov, Markov fluid, and Markov-modulated Poisson sources, are considered. Employing the effective bandwidth of time-varying sources and the effective capacity of time-varying wireless transmissions, maximum average arrival rates in the presence of statistical queuing constraints are characterized. For the two-state (ON/OFF) source models, throughput is determined in a closed form as a function of the source statistics, channel characteristics, and quality of service (QoS) constraints. Throughput is further studied in certain asymptotic regimes. Furthermore, energy efficiency is analyzed by determining the minimum energy per bit and a wideband slope in the low signal-to-noise ratio regime. Overall, the impact of source characteristics, QoS requirements, and channel fading correlations on the throughput and energy efficiency of wireless systems is identified.

Energy-efficient power adaptation for cognitive radio systems under imperfect channel sensing

In this paper, energy efficient power adaptation is considered in sensing-based spectrum sharing cognitive radio systems in which secondary users first perform channel sensing and then initiate data transmission with two power levels based on the sensing decisions (e.g., idle or busy). It is assumed that spectrum sensing is performed by the cognitive secondary users, albeit with possible errors. In this setting, the optimization problem of maximizing the energy efficiency (EE) subject to peak/average transmission power constraints and average interference constraints is considered. The circuit power is taken into account for total power consumption. By exploiting the quasiconcave property of the EE maximization problem, the original problem is transformed into an equivalent parameterized concave problem and Dinkelbachs method-based iterative power adaptation algorithm is proposed. The impact of sensing performance, peak/average transmit power constraints and average interference constraint on the energy efficiency of cognitive radio systems is analyzed.

Throughput analysis of buffer-constrained wireless systems in the finite blocklength regime

In this paper, a single point-to-point wireless link operating under queueing constraints in the form of limitations on the buffer violation probabilities is considered. The achievable throughput under such constraints is captured by the effective capacity formulation. It is assumed that finite blocklength codes are employed for transmission. Under this assumption, a recent result on the channel coding rate in the finite blocklength regime is incorporated into the analysis, and the throughput achieved with such codes in the presence of queueing constraints and decoding errors is identified. The performance of different transmission strategies (e.g., variable-rate, variable-power, and fixed-rate transmissions) is studied. Interactions and tradeoffs between the throughput, queueing constraints, coding blocklength, decoding error probabilities, and signal-to-noise ratio are investigated, and several conclusions with important practical implications are drawn.

Energy-Efficient Power Allocation in Cognitive Radio Systems With Imperfect Spectrum Sensing

This paper studies energy-efficient power allocation schemes for secondary users in sensing-based spectrum sharing cognitive radio systems. It is assumed that secondary users first perform channel sensing possibly with errors and then initiate data transmission with different power levels based on sensing decisions. In this setting, the optimization problem is to maximize energy efficiency (EE) subject to peak/average transmission power constraints and peak/average interference constraints. By exploiting the quasi-concave property of the EE maximization problem, the original problem is transformed into an equivalent parameterized concave problem, and an iterative power allocation algorithm based on Dinkelbachs method is proposed. The optimal power levels are identified in the presence of different levels of channel side information (CSI) regarding the transmission and interference links at the secondary transmitter, namely, perfect CSI of both transmission and interference links, perfect CSI of the transmission link, imperfect CSI of the interference link, imperfect CSI of both links, or only statistical CSI of both links. Through numerical results, the impact of sensing performance, different types of CSI availability, and transmit and interference power constraints on the EE of the secondary users is analyzed.

Performance Analysis of Cognitive Radio Systems With Imperfect Channel Sensing and Estimation

In cognitive radio systems, employing sensing-based spectrum access strategies, secondary users are required to perform channel sensing to detect the activities of licensed primary users in a channel, and in realistic scenarios, channel sensing occurs with possible errors due to miss-detections and false alarms. As another challenge, time-varying fading conditions in the channel between the secondary transmitter and the secondary receiver have to be learned via channel estimation. In this paper, performance of causal channel estimation methods in correlated cognitive radio channels under imperfect channel sensing results is analyzed, and achievable rates for reliable communication under both channel and sensing uncertainty are investigated by considering the input-output mutual information. Initially, cognitive radio channel model with channel sensing error and channel estimation is described. Then, using pilot symbols, minimum mean square error (MMSE) and linear-MMSE (L-MMSE) estimation methods are employed at the secondary receiver to learn the channel fading coefficients. Expressions for the channel estimates and mean-squared errors (MSE) are determined, and their dependencies on channel sensing results, and pilot symbol period and energy are investigated. Since sensing uncertainty leads to uncertainty in the variance of the additive disturbance, channel estimation strategies and performance are interestingly shown to depend on the sensing reliability. It is further shown that the L-MMSE estimation method, which is in general suboptimal, performs very close to MMSE estimation. Furthermore, assuming the channel estimation errors and the interference introduced by the primary users as zero-mean and Gaussian distributed, achievable rate expressions of linear modulation schemes and Gaussian signaling are determined. Subsequently, the training period, and data and pilot symbol energy allocations are jointly optimized to maximize the achievable rates for both signaling schemes.

Energy efficiency of hybrid-ARQ systems under QoS constraints

In this paper, energy efficiency of hybrid automatic repeat request (HARQ) schemes under QoS constraints is studied in the low power regime by characterizing the minimum energy per bit and wideband slope. The energy efficiency is investigated when either an outage constraint is imposed and (the transmission rate is selected accordingly) or the transmission rate is optimized to maximize the throughput. In both cases, it is also assumed that there is a limitation on the number of retransmissions due to deadline constraints. Under these assumptions, closed-form expressions are obtained for the minimum energy per bit and wideband slope for HARQ with chase combining (CC). Through numerical results, the performances of HARQ-CC and HARQ with incremental redundancy (IR) are compared. Moreover, the impact of deadline constraints, outage probability, QoS constraints on the energy efficiency is analyzed.

Throughput Regions of Multiple-Access Fading Channels with Markov Arrivals and QoS Constraints

In this paper, throughput regions of multiple-access fading channels are characterized when multiple users, experiencing random data arrivals, transmit to a common receiver under statistical quality of service (QoS) constraints. Random arrivals are modeled as discrete Markov or Markov fluid processes. Different multiple-access transmission strategies, namely time-division multiple access and superposition coding with fixed or variable decoding orders, are considered. For these arrival and transmission models, throughput in terms of maximum average arrival rates is formulated in the presence of buffer constraints, employing the notions of effective bandwidth of time-varying sources and effective capacity of time-varying transmissions. Throughput regions are computed for the two-user case and the impact of source burstiness, buffer constraints, signal-to-noise ratio, and different communication strategies is investigated.

Throughput of two-hop wireless channels with queueing constraints and finite blocklength codes

In this paper, throughput of two-hop wireless relay channels is studied in the finite blocklength regime. Half-duplex relay operation, in which the source node initially sends information to the intermediate relay node and the relay node subsequently forwards the messages to the destination, is considered. It is assumed that all messages are stored in buffers before being sent through the channel, and both the source node and the relay operate under statistical queueing constraints. After characterizing the transmission rates in the finite blocklength regime, the system throughput is formulated via queueing analysis. Subsequently, several properties of the throughput function in terms of system parameters are identified, and an efficient algorithm is proposed to maximize the throughput. Interplay between throughput, queueing constraints, relay location, time allocation, and code blocklength is investigated through numerical results.

Statistical Delay Tradeoffs in Buffer-Aided Two-Hop Wireless Communication Systems

This paper analyzes the impact of statistical delay constraints on the achievable rate of a two-hop wireless communication link in which the communication between a source and a destination is accomplished via a buffer-aided relay node. It is assumed that there is no direct link between the source and the destination, and the buffer-aided relay forwards the information to the destination by employing the decode-and-forward scheme. Given statistical delay constraints specified via maximum delay and delay violation probability, the tradeoff between the statistical delay constraints imposed on any two concatenated queues is identified. With this characterization, the maximum constant arrival rates that can be supported by this two-hop link are obtained by determining the effective capacity of such links as a function of the statistical delay constraints, signal-to-noise ratios at the source and relay, and the fading distributions of the links. It is shown that asymmetric statistical delay constraints at the buffers of the source and relay node can improve the achievable rate. Overall, the impact of the statistical delay tradeoff on the achievable throughput is provided.