946 publications from this institution
The dual-decomposition-based sum capacity maximization algorithm for the vector broadcast channels consists of the following two nested loops: The inner loop is an iterative water-filling-like algorithm that optimizes the covariance matrices for a fixed water level, and the outer loop finds the optimum water level by using a bisection search. Recently, an improved version has been proposed, where the inner loop is optimized only to a certain tolerance that still guarantees algorithm convergence. This requires tracking the instantaneous error of the inner loop, and this issue has not yet been addressed. In this paper, we provide a tight upper bound of the instantaneous error of the inner loop, which can be used to construct a nonheuristic stopping criterion. Specifically, we derive the Lagrange dual function of the inner optimization problem and provide a method to obtain a feasible dual variable from which a tight upper bound of the instantaneous error can easily be computed. We also show that the rate of convergence of the global algorithm can substantially be improved by introducing a covariance normalization step between two successive outer iterations.
In this paper, a new practical method for determining the bit-error rate (BER) of equal gain combining (EGC) receivers for multicarrier code-division multiple-access (MC-CDMA) systems is presented. This new method is based on an alternative Gaussian approximation (AGA) and it applies to the system with multiple active users in frequency selective Nakagami-m fading channels. This new procedure takes advantage of an alternate integral representation of the conditional BER along with characteristic functions. The AGA method has the advantage of simplicity in expression and computational efficiency. The accuracy of the proposed approach is demonstrated with computer simulations in a fading channel.
We consider the problem of cross-layer utility maximization subject to stability constraints for a multicommodity wireless network where all links are sharing a single channel. We assume a time slotted network and only one node is allowed to transmit at any given slot. The optimal cross-layer network control policy can be decomposed into three subproblems: (1) flow control at the transport and network layers, (2) routing and scheduling at the network layer, and (3) resource allocation (RA) at the medium access control and physical layers. Every time slot, a network controller decides the transmitter node, amount of each commodity data admitted to the network layer, schedules different commodities over networks' links and controls the power and rate allocated to every link. In this paper we provide solutions for the RA subproblem for two scenarios: (1) when the nodes are equipped with standard single user receivers, and (2) when the nodes are equipped with multiuser receivers performing successive interference cancelation. In addition we propose decentralized algorithms to obtain the solution of RA subproblem for each scenario. The numerical results show that as the signal-to-noise ratio increases, using multiuser receivers can provide significant gains at the network layer in terms of end-to-end rates and network congestion as compared to that of single user receivers.
The cross-layer utility maximization problem subject to stability constraints is considered for a multi-hop wireless network. A time slotted network, where the channel gains are changing randomly from slot to slot is assumed. The optimal cross-layer network control policy can be decomposed into three subproblems: 1) flow control, 2) next-hop routing and in-node scheduling, and 3) power and rate control, also known as resource allocation (RA). In the case of multi-hop networks, RA subproblem is particularly difficult to solve due to the self interference problem which arises when a node simultaneously transmits and receives in the same channel. According to relative distances between networks nodes, the self interference coefficients can be several order of magnitude larger than the power gains between distinct nodes. Thus, standard RA methods for bipartite networks can not be applied directly. The main contribution of this paper is to derive a novel RA algorithm for multi-hop wireless networks which is capable to deal with the self interference problem and does not rely on combinatorial constraints for finding the set of links which can be simultaneously activated. The numerical results show that the proposed RA algorithm can provide significant gains at network layer in terms of end-to-end rates and network congestion, even though the solution is local.
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This paper investigates the performance of a two-user downlink non-orthogonal multiple access (NOMA) system using hybrid automatic repeat request with chase combining (HARQ-CC) in finite blocklength. First, an analytical framework is developed by deriving closed-form approximations for the individual average block error rate (BLER) of the near and the far user. Based upon that, the performance of NOMA is discussed in comparison to orthogonal multiple access (OMA), which draws the conclusion that NOMA outperforms OMA in terms of user fairness. Further, an algorithm is devised to determine the required blocklength and power allocation coefficients for NOMA that satisfies reliability targets for the users. The required blocklength for NOMA is compared to OMA, which shows NOMA has a lower blocklength requirement in high transmit signal-to-noise ratio (SNR) conditions, leading to lower latency than OMA when reliability requirements in terms of BLER for the two users are in the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> .
Ambient radio frequency (RF) energy harvesting (EH) technology is key to realize self-sustainable, always-on, low-power, massive Internet of Things networks. Typically, rigid (non-adaptable to channel fluctuations) multi-antenna receive architectures are proposed to support reliable EH operation. Herein, we introduce a dynamic RF combining architecture for ambient RF EH use cases, and exemplify the attainable performance gains via three simple phase shifts' exploration mechanisms, namely, brute force (BF), sequential testing (ST) and codebook based (CB). Among the proposed mechanisms, BF demands the highest power consumption, while CB requires the highest-resolution phase shifters, thus tipping the scales in favor of ST. Finally, we show that the performance gains of ST over a rigid RF combining scheme increase with the number of receive antennas and energy transmitters' deployment density.
Ambient radio frequency (RF) energy harvesting (EH) technology is key to realize self-sustainable, always-on, low-power, massive Internet of Things networks. Typically, rigid (non-adaptable to channel fluctuations) multi-antenna receive architectures are proposed to support reliable EH operation. Herein, we introduce a dynamic RF combining architecture for ambient RF EH use cases, and exemplify the attainable performance gains via three simple phase shifts' exploration mechanisms, namely, brute force (BF), sequential testing (ST) and codebook based (CB). Among the proposed mechanisms, BF demands the highest power consumption, while CB requires the highest-resolution phase shifters, thus tipping the scales in favor of ST. Finally, we show that the performance gains of ST over a rigid RF combining scheme increase with the number of receive antennas and energy transmitters' deployment density.
