Information Rate Constraints Research Articles

Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

Efficient algorithms for physical layer security in one-way relay systems

In this paper, we solve two problems aiming at improving the secure communications in a one-way relay system. The system under consideration consists of one source, one destination, N cooperative relays that use amplify and forward, and one eavesdropper. First, we minimize the relays power under information rate constraints for both the legitimate destination and the eavesdropper. When the source power is given, the problem is formulated as a non-convex quadratically constraint quadratic programming, which can be solved using a semidefinite programming (SDP). However, since SDP suffers from high complexity, we propose a novel approach, using generalized eigenvalue, that provides a closed form of the optimal solution in most cases. Then we prove that the relays power can be further decreased as the source power increases. Second, we solve the problem of maximizing the secrecy rate by looking for the optimal relays beamforming vector. We show that the optimization problem of the beamforming vector is a product of two correlated generalized Rayleigh quotients. For this problem, we show that the optimal beamforming vector can be obtained using a series of SDPs, then we significantly simplify the problem by solving it using a series of eigenvalue problem. Numerical results show that the proposed approaches achieve the optimal solution of the relay beamforming vector and enhance the physical layer security with power allocation.

Virtual Resource Allocation in Virtualized Small Cell Networks with Physical-Layer Network Coding Aided Self-Backhauls

Virtualized small cell network is a promising architecture which can realize efficient utilization of the network resource. However, conventional full duplex self-backhauls lead to residual self-interference, which limits the network performance. To handle this issue, this paper proposes a virtual resource allocation, in which the residual self-interference is fully exploited by employing a physical-layer network coding (PNC) aided self-backhaul scheme. We formulate the features of PNC as time slot and information rate constraints, and based on that, the virtual resource allocation is formulated as a mixed combinatorial optimization problem. To solve the problem efficiently, it is decomposed into two sub problems, and a two-phase iteration algorithm is developed accordingly. In the algorithm, the first sub problem is approximated and transferred into a convex problem by utilizing the upper bound of the PNC rate constraint. On the basis of that, the convexity of the second sub problem is also proved. Simulation results show the advantages of the proposed scheme over conventional solution in both the profits of self-backhauls and utility of the network resource.

Flash Memories: ISPP Renewal Theory and Flash Design Tradeoffs

In the write process of multilevel per cell (MLC) flash memories, an iterative approach is used to mitigate the monotonicity problem. The monotonicity in programming is considered to be the major restriction in MLC flash. To solve this issue, an iterative approach called incremental step pulse programming (ISPP) is used to concurrently program lots of cells in small steps. In this paper, we are mostly concerned with deriving a mathematical model for iterative programming using the framework of renewal theory . We obtain a closed-form approximation for the probability distribution of the number of steps required in the ISPP process. We also bound the maximal error between the true distribution and our approximation. Moreover, the results obtained help to accurately analyze the effect of inter-cell interference in this type of memory. Finally, we devise an adaptive step size approach for write process to strike a balance between latency and lifetime under fixed bit error rate constraints or information rate constraints.

Bayesian Design of Tandem Networks for Distributed Detection With Multi-Bit Sensor Decisions

We consider the problem of decentralized hypothesis testing under communication constraints in a topology where several peripheral nodes are arranged in tandem. Each node receives an observation and transmits a message to its successor, and the last node then decides which hypothesis is true. We assume that the observations at different nodes are, conditioned on the true hypothesis, independent and the channel between any two successive nodes is considered error-free but rate-constrained. We propose a cyclic numerical design algorithm for the design of nodes using a person-by-person methodology with the minimum expected error probability as a design criterion, where the number of communicated messages is not necessarily equal to the number of hypotheses. The number of peripheral nodes in the proposed method is in principle arbitrary and the information rate constraints are satisfied by quantizing the input of each node. The performance of the proposed method for different information rate constraints, in a binary hypothesis test, is compared to the optimum rate-one solution due to Swaszek and a method proposed by Cover, and it is shown numerically that increasing the channel rate can significantly enhance the performance of the tandem network. Simulation results for $M$-ary hypothesis tests also show that by increasing the channel rates the performance of the tandem network significantly improves.

Link energy minimization for wireless networks

In this paper, we formalize the problem of minimizing the energy dissipated to successfully transmit a single information bit over a link, considering circuit power consumption, packetization and retransmission overhead, bit/packet error probability, and the duty cycle of the transceiver. We optimize the packet length and transmit power as a function of distance between the transmitter and the receiver for different modulation schemes. We propose a general unconstrained energy consumption model that provides a lower bound on the energy dissipated per information bit. A practical unconstrained physical layer optimization scheme is also provided to illustrate the utilization of the model. Furthermore, minimized energy consumptions of different modulation schemes are compared over an additive white Gaussian noise (AWGN) channel. We extend this general energy consumption model by considering two particular constraints: fixed average power and fixed average rate. We explore the impact of the average power and the information rate constraints on energy consumption and determine the optimum constellation size, packet length, and duty cycle.

Linear stochastic optimal control under information rate constraints †

The discrete-time, linear, stochastic optimal control problem is considered under information rate constraints on the feedback loop. The feedback loop, including sensor, is modelled as a communication channel which provides a specified amount of information (in the Shannon sense) about the state of the linear plant at each discrete-time instant given the current and past observations and past controls. No further specific structure for the sensor is assumed. The expected value of a positive definite quadratic loss function is used as the performance criterion to be minimized. This leads to a double minimization problem in which the performance criterion is minimized over the set of admissible controls and the set of conditional probability densities for the state given the observations and controls which achieve the specified information. A set of recursion relationships for the solution of this problem is derived using the techniques of calculus of variations and dynamic programming. The solution indicat...