In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e., the base station (BS) is assumed to be able to acquire partial' instantaneous CSI of the cellular and D2D links, as well as, the interference links. In contrast to existing works, multiple D2D links are allowed to share the same channel, and the quality of service (QoS) requirements for both the cellular and D2D links are enforced. We first develop an optimal channel assignment algorithm based on dynamic programming (DP), which enjoys a much lower complexity compared to exhaustive search and will serve as a performance benchmark. To further reduce complexity, we propose a cluster-based sub-optimal channel assignment algorithm. New closed-form expressions for the expected weighted sum-rate and the successful transmission probabilities are also derived. Simulation results verify the effectiveness of the proposed algorithms. Moreover, by comparing different partial CSI scenarios, we observe that the CSI of the D2D communication links and the interference links from the D2D transmitters to the BS significantly affects the network performance, while the CSI of the interference links from the BS to the D2D receivers only has a negligible impact.
Most previous works have focused on the full CSI scenario, but, recently, some papers have considered the partial CSI case. Considering that the BS cannot acquire CSI of the interference links between user devices, a maximum weighted bipartite matching algorithm was applied in @cite_16 to get the optimal recourse allocation scheme in a D2D network. However, this work allowed at most one D2D link to access one channel. In @cite_25 , multiple D2D links were allowed to access the same channel with the assumption that the BS only has knowledge of the CSI of cellular links, and a centralized channel assignment algorithm and a distributed power control algorithm were developed in the high SINR region. However, the high SINR assumption does not usually hold in D2D communications. Furthermore, all the previous works considering partial CSI have only dealt with one particular partial CSI scenario, e.g., in @cite_16 , the BS was assumed to know the CSI of all the links except the interference links between user devices. Thus, the relative importance of the CSI of different links in D2D networks is still unknown, and it is the question that will be explored in this paper.
@cite_16 @cite_25
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In device-to-device (D2D) communications, channel state information (CSI) is exploited to manage the interference between D2D users and regular cellular users (CUs) and improve system performance. However, obtaining the accurate CSI is usually difficult and causes high overhead, particularly when the links are not connected to the base station (BS), such as the links between regular CUs and D2D receivers (CU-D links). In this paper, we investigate the signaling overhead and performance tradeoff in D2D communications with channel uncertainty. To limit interference to regular CUs, we only allow the resource of a CU to be reused by, at most, one D2D pair. We also assume that only partial CSI of the CU-D links is available at the BS and develop two different strategies to deal with the channel uncertainty, namely, probabilistic and partial feedback schemes. We first derive a probability-based resource-allocation scheme by utilizing channel statistical characteristics to maximize the overall throughput of the CUs and admissible D2D pairs while guaranteeing their quality of service (QoS) in terms of signal-to-interference-plus-noise ratio (SINR) and outage probability, respectively. Then, we propose an efficient feedback scheme to reduce the overhead of CSI feedback while providing near-optimal performance. In addition, we propose a combined scheme to take advantages of both probabilistic and partial feedback schemes. It is shown by simulation that there exists an optimal threshold of the outage probability for probabilistic scheme while the partial feedback scheme is robust to the channel models. Furthermore, the combined scheme outperforms the probabilistic and the partial feedback schemes in terms of overall throughput. In this paper, we study resource allocation in a downlink OFDMA system assuming imperfect channel state information (CSI) at the transmitter. To achieve the individual QoS of the users in OFDMA system, adaptive resource allocation is very important, and has therefore been an active area of research. However, in most of the the previous work perfect CSI at the transmitter is assumed which is rarely possible due to channel estimation error and feedback delay. In this paper, we study the effect of channel estimation error on resource allocation in a downlink OFDMA system. We assume that each user terminal estimates its channel by using an MMSE estimator and sends its CSI back to the base station through a feedback channel. We approach the problem by using convex optimization framework, provide an explicit closed form expression for the users' transmit power and then develop an optimal margin adaptive resource allocation algorithm. Our proposed algorithm minimizes the total transmit power of the system subject to constraints on users' average data rate. The algorithm has polynomial complexity and solves the problem with zero optimality gaps. Simulation results show that our algorithm highly improves the system performance in the presence of imperfect channel estimation. In this paper, we investigate secure transmission in untrusted amplify-and-forward half-duplex relaying networks with the help of cooperative jamming at the destination (CJD). Under the assumption of full channel state information (CSI), conventional CJD using self-interference cancelation at the destination is efficient when the untrusted relay has no capability to suppress the jamming signal. However, if the source and destination are equipped with a single antenna and the only untrusted relay is equipped with N multiple antennas, it can remove the jamming signal from the received signal by linear filters and the full multiplexing gain of relaying cannot be achievable with the conventional CJD due to the saturation of the secrecy rate at the high transmit power regime. We propose in this paper new CJD scheme where neither destination nor relay can acquire CSI of relay-destination link. Our proposed scheme utilizes zero-forcing cancelation based on known jamming signals instead of self-interference subtraction, while the untrusted relay cannot suppress the jamming signals due to the lack of CSI. We show that the secrecy rate of the proposed scheme can enjoy a half of multiplexing gain in half-duplex relaying while that of conventional CJD is saturated at high transmit power for N ≥2. The impact of channel estimation error at the destination is also investigated to show the robustness of the proposed scheme against strong estimation errors. In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well.