Probabilistic Interference Constraint Research Articles

Robust beamforming for D2D multicast communication

In this paper, beamforming for device-to-device multicast communication under channel uncertainty arising from incomplete channel state information is considered. The problem is first formulated as a non-robust minimization problem subject to signal-to-interference-plus-noise ratio, transmit power and interference constraints. Using semi-definite programming relaxation, a tractable convex robust counterpart of the beamforming problem was then formulated. Cases of deterministic and probabilistic interference constraints were considered. To simplify the probabilistic constraint, a cumulative distribution function substitution was used. The results show that the channel uncertainty error affects the optimal beamforming vector compared to the negligible effect of the probabilistic constraint.

Energy-Efficient Multicasting in Hybrid Cognitive Small Cell Networks: A Cross-Layer Approach

We study the performance of a cognitive small cell network, catering multicast services to multiple groups of secondary users, using a pre-assigned set of orthogonal channels of primary users present in the corresponding macrocell. We consider the hybrid mode of cognitive radio operation for efficient spectrum access, where cooperative spectrum sensing is performed by the collocated secondary users of each group to ensure better protection to primary users’ communication. Considering delay-sensitive application for each multicast group and availability of a rate adaptive application layer and a power adaptive link layer, we aim to maximize the energy efficiency of the small cell base station using a cross-layer approach. More precisely, a joint optimization problem involving sensing time, rate and power allocation is formulated to maximize the energy efficiency of the small cell network under the probabilistic interference constraint of each primary user and heterogeneous quality of service constraints of each SU multicast group. The problem is found to be generally non-convex and an iterative algorithm is proposed to find either an optimal or a near-optimal solution. Simulation results are presented to analyze the performance of our proposed scheme in terms of energy efficiency concerning some key system parameters. The results confirm that our solution is much more energy-efficient than the conventional approach with only transmit power adaptation.

A nonorthogonal cooperative scheme for multiuser CRN using probabilistic interference constraint

AbstractThe predicted exponential increase in data traffic for future 5G networks demands for increased wireless spectrum capacity. The unlicensed spectrum bands are overburdened, whereas the licensed spectrum bands are underutilized. Cognitive radio systems (CRSs) can solve the problem of spectrum scarcity by allowing the reuse of the underutilized licensed spectrum bands. However, efficient resource allocation schemes in CRS are inevitable before we can reap the benefits of CRS. In this article, we have formulated an optimization problem for multiuser cooperative CRS, which considers relay selection and power allocation to maximize the sum capacity of the system. The optimization problem is a mixed‐integer nonlinear program, and deriving its optimal solution is extremely difficult. Therefore, we propose an iterative joint multiple relay selection and power allocation (IJRSPA) algorithm for multiuser CRSs. The proposed IJRSPA has low computational complexity, and simulation results verify its effectiveness.

Robust Transmissions in Wireless-Powered Multi-Relay Networks With Chance Interference Constraints

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput.

MSE Balancing in the MIMO BC: Unequal Targets and Probabilistic Interference Constraints

Second order cone (SOC) restrictions have been widely employed in the design of wireless communication systems to recast precoding optimizations in convex form. This paper highlights the flexibility of this framework to handle various transmit power and interference limitations also in the multistream multiple-input multiple-output broadcast channel with imperfect channel state information at the transmitter. In particular, we propose an iterative solution for mean square error (MSE) balancing with such constraints via the Lagrangian dual formulation. The MSE balancing criteria, contrary to signal-to-interference plus-noise ratio and rate criteria, exhibit an interesting property when considering different targets for the receivers. Users with low target values are strictly prioritized in comparison to users with large MSE targets that can even be switched off. Therefore, only a subset of the receivers is served at the solution with stringent limitations for the precoders. An example of such stringent limitations are small power constraints and probabilistic interference temperature constraints that set a measurable threshold on the generated interference in a cognitive radio system. We provide four different approaches to approximate these probabilistic constraints with deterministic SOC constraints, and compare their accuracy.

Kriging-Based Interference Power Constraint: Integrated Design of the Radio Environment Map and Transmission Power

This paper proposes a probabilistic interference constraint method with a radio environment map (REM) for spatial spectrum sharing. The REM stores the spatial distribution of the average received signal power. We can optimize the accuracy of the measurement-based REM using the Kriging interpolation. Although several researchers have maintained a continuous interest in improving the accuracy of the REM, sufficient study has not been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary Kriging interpolation for the spectrum cartography. According to the predicted distribution of the estimation error, the allowable interference power to the primary user is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically. Additionally, we compare the performance of the proposed technique with three methods: the perfect estimation, the path loss-based method, and the Kriging-based method without the error prediction. The comparison results show that the proposed method has a higher spectrum sharing opportunity than the path loss-based method, even if only a small amount of measurement data is available. It is also shown that the proposed method dramatically improves the outage probability of the interference power compared to the conventional Kriging-based method.

Energy-Efficient Chance-Constrained Resource Allocation for Multicast Cognitive OFDM Network

In this paper, an energy-efficient resource allocation problem is modeled as a chance-constrained programming for multicast cognitive orthogonal frequency division multiplexing (OFDM) network. The resource allocation is subject to constraints in service quality requirements, total power, and probabilistic interference constraint. The statistic channel state information (CSI) between cognitive-based station (CBS) and primary user (PU) is adopted to compute the interference power at the receiver of PU, and we develop an energy-efficient chance-constrained subcarrier and power allocation algorithm. Support vector machine (SVM) is employed to compute the probabilistic interference constraint. Then, the chance-constrained resource allocation problem is transformed into a deterministic resource allocation problem, and Zoutendijk’s method of feasible direction is utilized to solve it. Simulation results demonstrate that the proposed algorithm not only achieves a tradeoff between energy efficiency and satisfaction index, but also guarantees the probabilistic interference constraint very well.

Resource allocation algorithm based on hybrid particle swarm optimization for multiuser cognitive OFDM network

Resource allocation plays a critical role to enhance the performance of cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to lack the cooperation between cognitive network and primary network, the channel state information (CSI) between cognitive radio (CR) user and primary user (PU) could not be estimated precisely. In this work, a resource allocation problem over the power and subcarrier allocation based on chance-constrained programming is formulated to maximize the average weighted sum-rate throughput and guarantee the probabilistic interference constraint condition for PU. In order to solve the above resource allocation problem, the probabilistic interference constraint condition is computed by support vector machine (SVM) and we combine particle swarm optimization (PSO) and SVM to develop hybrid particle swarm optimization (HPSO). Simulation results verify HPSO not only yields the higher average weighted sum-rate throughput than other algorithms, but also satisfies the probabilistic interference constraint condition.

Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network

Proportional fair resource allocation plays a critical role to balance the spectrum efficiency and fairness for cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to the lack of cooperation between cognitive radio (CR) network and primary network, channel state information between CR base station (CRBS) and primary user (PU) could not be estimated precisely. Therefore, the interference of CRBS---PU couldn't be computed precisely and chance-constrained programming is adopted to formulate the resource allocation problem. In this work, we study the proportional fair resource allocation problem based on chance-constrained programming for cognitive OFDM network. The objective function maximizes the spectral efficiency of cognitive OFDM network over subcarrier and power allocation. The constraint conditions include the interference constraint of PU with the target probability requirement and the proportional fair rate requirement of CR users. In order to solve the above optimization problem, two steps are taken to develop hybrid immune optimization algorithm (HIOA). In the first step, the probabilistic interference constraint condition is transformed as an uncertain function which is computed by a generalized regression neural network (GRNN). In the second step, we combine immune optimization algorithm and GRNN to develop HIOA. Simulation results demonstrate that HIOA yields higher spectral efficiency while the probabilistic interference constraint condition and the proportional fair rate constraint condition could be satisfied very well.

Resource Allocation for Interweave and Underlay CRs Under Probability-of-Interference Constraints

Efficient design of cognitive radios (CRs) calls for secondary users implementing adaptive resource allocation schemes that exploit knowledge of the channel state information (CSI), while at the same time limiting interference to the primary system. This paper introduces stochastic resource allocation algorithms for both interweave (also known as overlay) and underlay cognitive radio paradigms. The algorithms are designed to maximize the weighted sum-rate of orthogonally transmitting secondary users under average-power and probabilistic interference constraints. The latter are formulated either as short- or as long-term constraints, and guarantee that the probability of secondary transmissions interfering with primary receivers stays below a certain pre-specified level. When the resultant optimization problem is non-convex, it exhibits zero-duality gap and thus, due to a favorable structure in the dual domain, it can be solved efficiently. The optimal schemes leverage CSI of the primary and secondary networks, as well as the Lagrange multipliers associated with the constraints. Analysis and simulated tests confirm the merits of the novel algorithms in: i) accommodating time-varying settings through stochastic approximation iterations; and ii) coping with imperfect CSI.