Cooperation between the primary and secondary systems can improve the spectrum efficiency in cognitive radio networks. The key idea is that the secondary system helps to boost the primary system's performance by relaying and in return the primary system provides more opportunities for the secondary system to access the spectrum. In contrast to most of existing works that only consider information cooperation, this paper studies joint information and energy cooperation between the two systems, i.e., the primary transmitter sends information for relaying and feeds the secondary system with energy as well. This is particularly useful when the secondary transmitter has good channel quality to the primary receiver but is energy constrained. We propose and study three schemes that enable this cooperation. Firstly, we assume there exists an ideal backhaul between the two systems for information and energy transfer. We then consider two wireless information and energy transfer schemes from the primary transmitter to the secondary transmitter using power splitting and time splitting energy harvesting techniques, respectively. For each scheme, the optimal and zero-forcing solutions are derived. Simulation results demonstrate promising performance gain for both systems due to the additional energy cooperation. It is also revealed that the power splitting scheme can achieve larger rate region than the time splitting scheme when the efficiency of the energy transfer is sufficiently large.Comment: 13 page, 9 figures. To appear in IEEE Transactions on Signal Processin
On a single-input-single-out (SISO) interference channel (IC), conventional non-cooperative strategies encourage players selfishly maximizing their transmit data rates, neglecting the deficit of performance caused by and to other players. In the case of proper complex Gaussian noise, the maximum entropy theorem shows that the best-response strategy is to transmit with proper signals (symmetric complex Gaussian symbols). However, such equilibrium leads to degrees-of-freedom zero due to the saturation of interference.With improper signals (asymmetric complex Gaussian symbols), an extra freedom of optimization is available. In this paper, we study the impact of improper signaling on the 2user SISO IC. We explore the achievable rate region with noncooperative strategies by computing a Nash equilibrium of a non-cooperative game with improper signaling. Then, assuming cooperation between players, we study the achievable rate region of improper signals. We propose the usage of improper rank one signals for their simplicity and ease of implementation. Despite their simplicity, rank one signals achieve close to optimal sum rate compared to full rank improper signals. We characterize the Pareto boundary, the outer-boundary of the achievable rate region, of improper rank one signals with a single real-valued parameter; we compute the closed-form solution of the Pareto boundary with the non-zero-forcing strategies, the maximum sum rate point and the max-min fairness solution with zero-forcing strategies. Analysis on the extreme SNR regimes shows that proper signals maximize the wide-band slope of spectral efficiency whereas improper signals optimize the high-SNR power offset. Index Terms-asymmetric complex signaling, improper signaling, SISO, interference channel
This paper considers the so-called Multiple-Input-Multiple-Output interference channel (MIMO-IC) which has relevance in applications such as multi-cell coordination in cellular networks as well as spectrum sharing in cognitive radio networks among others. We address the design of precoding (i.e. beamforming) vectors at each sender with the aim of striking a compromise between beamforming gain at the intended receiver (Egoism) and the mitigation of interference created towards other receivers (Altruism). Combining egoistic and altruistic beamforming has been shown previously to be instrumental to optimizing the rates in a Multiple-Input-Single-Output (MISO) interference channel (i.e. where receivers have no interference canceling capability) [1], [2]. Here we explore these game-theoretic concepts in the more general context of MIMO channels and use the framework of Bayesian games [3] which allows us to derive (semi-)distributed precoding techniques. We draw parallels with important existing work on the MIMO-IC, including rate-optimizing and interference-alignment precoding techniques, and show how such techniques may be re-interpreted through a common prism based on balancing egoistic and altruistic beamforming. Our analysis and simulations attest the improvements in terms of complexity and performance.
Coordination in a multi-cell/link environment has been attracting a lot of attention in the research community recently. In this paper, we consider the problem of coordinated beamforming where base stations (BS) equipped with multiple antennas attempt to serve a separate user each despite the interference generated by the other bases. We propose a framework for a distributed optimization of the beamformers at each base, where distributed is defined as using "local CSIT" only. We present and compare two distributed approaches (one iterative and another direct approach) which have in common the optimization of the beamformers as a combination of so-called egoistic and altruistic solutions for this problem. We provide the intuitions behind these approaches and some theoretical grounds for optimality in certain cases. Performance is finally illustrated through numerical simulations.
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