Ehsan Nekouei

Game-Theoretic Frameworks for Demand Response in Electricity Markets

This paper presents game-theoretic frameworks for demand response at both electricity market and consumer levels. First, the interaction between a demand response aggregator (DRA) and electricity generators is modeled as a Stackelberg game in which the DRA, as the leader of the game, makes demand reduction bids, and generators, as followers, compete for maximizing their profits based on the reduced demand. Next, the interaction between the DRA and consumers is modeled as a mechanism design problem wherein the DRA seeks to minimize the aggregate inconvenience of consumers while achieving the targeted load curtailment. The inconvenience function of each consumer is captured by a type value, which is used by the DRA to solve the load curtailment problem. A Vickrey-Clarke-Groves-based mechanism is proposed, which guarantees that each consumer reveals its true type value to the DRA. A case study of the Stackelberg game shows that, in the South Australian electricity market where there is significant renewable penetration, peak period demand response provides the maximum potential profit, but off-peak demand response even in a concentrated market is not financially attractive.

Power Control and Asymptotic Throughput Analysis for the Distributed Cognitive Uplink

This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fashion by using local knowledge of its direct and interference channel gains such that the resulting aggregate (average) interference does not disrupt primarys communication. Then, the tight sum-rate scaling laws are derived as a function of the number of secondary users N under the optimum distributed power control policy. The fading models considered to derive sum-rate scaling laws are general enough to include Rayleigh, Rician and Nakagami fading models as special cases. When transmissions of secondary users are limited by both transmission and interference power constraints, it is shown that the secondary network sum-rate scales according to 1/enh log log (N), where n_h is a parameter obtained from the distribution of direct channel power gains. For the case of transmissions limited only by interference constraints, on the other hand, the secondary network sum-rate scales according to 1/eγg log (N), where γg is a parameter obtained from the distribution of interference channel power gains. These results indicate that the distributed cognitive uplink is able to achieve throughput scaling behavior similar to that of the centralized cognitive uplink up to a pre-log multiplier 1/e, whilst primarys quality-of-service requirements are met. The factor 1/e can be interpreted as the cost of distributed implementation of the cognitive uplink.

A game-theoretic analysis of demand response in electricity markets

This paper presents a game-theoretic approach to demand response in electricity markets. A Stackelberg game model is developed to capture the interplay between a Demand Response Aggregator (DRA) and electricity generators, where the DRA acts the leader of the game and makes demand reduction bids by taking into account their profitability. The classical generators respond by adjusting their electricity generation levels which leads to an equilibrium solution of the resulting strategic (non-cooperative) game in the competitive wholesale electricity market. A numerical analysis of the Stackelberg game shows that highly concentrated markets during the peak hours are the most profitable scenarios for the demand response from the DRAs perspective.

Asymptotically optimal channel feedback protocol design for cognitive multiple access channels

In cognitive multiple access networks, primary-secondary feedback links are needed to convey secondary transmitter primary base station (STPB) channel gains from the primary base station (PBS) to the secondary base station (SBS). To reduce the amount of feedback exchange between PBS and SBS, this paper proposes a feedback control protocol called K-smallest channel gains (K-SCG) feedback protocol in which the PBS feeds back the KN smallest STPB channel gains, out of N of them, to the SBS. We study the performance of K-SCG feedback protocol for total power and interference limited (TPIL) networks when transmit powers of secondary users (SUs) are optimally allocated. In TPIL networks, transmit powers of SUs are limited by an average total power constraint as well as a constraint on the average total interference power that they cause to the PBS. It is shown that for KN = Nδ with δ ∈ (0, 1), K-SCG feedback protocol is asymptotically optimal, i.e., secondary network throughput under K-SCG and full feedback protocols scales according to 1 over nhlog log (N) where nh is a parameter obtained from the distribution of secondary transmitter secondary base station (STSB) channel power gains, and N is the number of SUs. It is also shown that for KN = o(N), the interference power at the PBS converges to zero almost surely and in mean as N becomes large. This result implies that for N large enough, the secondary network just requires the indices of SUs corresponding to the KN smallest STPB channel gains for performing jointly optimal user scheduling and power allocation rather than the actual realizations of STPB channel gains.

Throughput Analysis for the Cognitive Uplink Under Limited Primary Cooperation

This paper studies the achievable throughput performance of the cognitive uplink under a limited primary cooperation scenario wherein the primary base station cannot feed back all interference channel gains to the secondary base station. To cope with the limited primary cooperation, we propose a feedback protocol called K-out-of-N feedback protocol, in which the primary base station feeds back only the KN smallest interference channel gains, out of N of them, to the secondary base station. We characterize the throughput performance under the K-out-of-N feedback protocol by analyzing the achievable multiuser diversity gains (MDGs) in cognitive uplinks for three different network types. Our results show that the proposed feedback mechanism is asymptotically optimum for interference-limited (IL) and individual-power-and-interference-limited (IPIL) networks for a fixed positive KN. It is further shown that the secondary network throughput in the IL and IPIL networks (under both the full and limited cooperation scenarios) logarithmically scales with the number of users in the network. In total-power-and-interference-limited (TPIL) networks, on the other hand, the K-out-of-N feedback protocol is asymptotically optimum for KN = Nδ, where δ ∈ (0, 1). We also show that, in TPIL networks, the secondary network throughput under both the limited and full cooperation scales logarithmically double with the number of users in the network. These results indicate that the cognitive uplink can achieve the optimum MDG even with limited cooperation from the primary network. They also establish the dependence of pre-log throughput scaling factors on the distribution of fading channel gains for different network types.

Convergence analysis of quantized primal-dual algorithm in quadratic network utility maximization problems

This paper examines the effect of quantized communications on the convergence behavior of the primal-dual algorithm in quadratic network utility maximization problems with linear equality constraints. In our set-up, it is assumed that the primal variables are updated by individual agents, whereas the dual variables are updated by a central entity, called system, which has access to the parameters quantifying the system-wide constraints. The notion of differential entropy power is used to establish a universal lower bound on the rate of exponential mean square convergence of the primal-dual algorithm under quantized message passing between agents and the system. The lower bound is controlled by the average aggregate data rate under the quantization, the curvature of the utility functions of agents, the number of agents and the number of constraints. An adaptive quantization scheme is proposed under which the primal-dual algorithm converges to the optimal solution despite quantized communications between agents and the system. Finally, the rate of exponential convergence of the primal-dual algorithm under the proposed quantization scheme is numerically studied.

Optimal power policies and throughput scaling analyses in fading cognitive broadcast channels with primary outage probability constraint

This paper focuses on a spectrum-sharing-based fading cognitive radio broadcast channel (BC) with a single-antenna secondary base station (SBS) and M single-antenna secondary receivers (SRs) utilizing the same spectrum band with a delay-sensitive primary user (PU). The service-quality requirement for the primary user is set by an outage probability constraint (POC). We address the optimal power allocation problem for the SBS ergodic sum capacity (ESC) maximization in the secondary BC network subject to POC and a transmit power constraint at SBS specified by either a long-term or a short-term power constraint. The optimality conditions reveal that in each joint channel state, the SBS allocates transmission power to the only one selected SR with the highest value of a certain metric consisting of the ratio of the SR’s direct channel power gain and the sum of interference power and noise power at the SR. Then, the secondary network throughput scaling analysis as the number of SRs becomes large, is also investigated, showing that if PU applies a truncated channel inversion (TCI) power policy, the SBS ESC scales like εp log(logM) where εp is the PU outage probability threshold. To reduce the amount of channel side information (CSI) transferred between the two networks, we propose a suboptimal transmission scheme which requires only 1-bit feedback from the delay-sensitive PR (partial CSI). We show that the new power control policy is asymptotically optimal, i.e. the SBS ESC under this reduced feedback scheme still scales like εp log(logM).

An information lossless, full diversity, full rate, linear dispersion STBC

A full rate linear dispersion space time block code is proposed. It is also shown that the code is information lossless. Using algebraic number theory, we present algebraic construction of the code for 3 antennas and also discuss the situation that the code achieves the maximum of transmit diversity. The code performance is studied using Monte Carlo simulation method. Simulation results show that the code outperforms linear dispersion code proposed by Hassibi at the same condition.

Multiuser Detection in MC-CDMA Communication Systems Using MPEM Algorithm

In this paper, we propose a sub-optimum multiuser detection technique in multicarrier code division multiple access (MC-CDMA) communication systems based on missing parameter expectation maximization (MPEM) algorithm. In the proposed detection procedure, first initial values for the bits of all users are estimated from received signal. Then, the proposed MPEM based algorithm uses outputs of carries’ demodulators to improve the accuracy of the initial estimates of the bits. In this paper, the expectation and maximization steps’ functions of the MPEM algorithm for MC-CDMA multiuser detection are derived and the performance of the proposed algorithm is analyzed. Our presented numerical results demonstrate the efficiency of the proposed detection algorithm both in bit error rate performance and computational complexity points of view.

Distributed cognitive multiple access networks: Power control, scheduling and multiuser diversity

This paper studies optimal distributed power allocation and scheduling policies (DPASPs) for distributed total power and interference limited (DTPIL) cognitive multiple access networks in which secondary users (SU) independently perform power allocation and scheduling tasks using their local knowledge of secondary transmitter secondary base-station (STSB) and secondary transmitter primary base-station (STPB) channel gains. In such networks, transmission powers of SUs are limited by an average total transmission power constraint and by a constraint on the average interference power that SUs cause to the primary base-station. We first establish the joint optimality of water-filling power allocation and threshold-based scheduling policies for DTPIL networks. We then show that the secondary network throughput under the optimal DPASP scales according to 1/enh log log (N), where nh is a parameter obtained from the distribution of STSB channel power gains and N is the total number of SUs. From a practical point of view, our results signify the fact that distributed cognitive multiple access networks are capable of harvesting multiuser diversity gains without employing centralized schedulers and feedback links as well as without disrupting primarys quality-of-service (QoS).