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Register a new userSynergistic Benefits in IRS- and RS-enabled C-RAN with Energy-Efficient Clustering
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The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, increases both, signal quality and interference at the users. To counteract this negative side effect, we employ rate splitting (RS), which inherently is able to mitigate the impact of interference. We facilitate practical implementation by considering a Cloud Radio Access Network (C-RAN) at the cost of finite fronthaul-link capacities, which necessitate the allocation of sensible user-centric clusters to ensure energy-efficient transmissions. Dynamic methods for RS and the user clustering are proposed to account for the interdependencies of the individual techniques. Numerical results show that the dynamic RS method establishes synergistic benefits between RS and the IRS. Additionally, the dynamic user clustering and the IRS cooperate synergistically, with a gain of up to 88% when compared to the static scheme. Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases. Around the resulting intersection, both methods affect the system concurrently, improving the energy efficiency drastically.
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The concept of intelligent reflecting surfaces (IRSs) is considered as a promising technology for increasing the efficiency of mobile wireless networks. This is achieved by employing a vast amount of low-cost individually adjustable passive reflect elements, that are able to apply changes to the reflected signal. To this end, the IRS makes the environment realtime controllable and can be adjusted to significantly increase the received signal quality at the users by passive beamsteering. However, the changes to the reflected signals have an effect on all users near the IRS, which makes it impossible to optimize the changes to positively influence every transmission, affected by the reflections. This results in some users not only experiencing better signal quality, but also an increase in received interference. To mitigate this negative side effect of the IRS, this paper utilizes the rate splitting (RS) technique, which enables the mitigation of interference within the network in such a way that it also mitigates the increased interference caused by the IRS. To investigate the effects on the overall power savings, that can be achieved by combining both techniques, we minimize the required transmit power, needed to satisfy per-user quality-of-service (QoS) constraints. Numerical results show the improved power savings, that can be gained by utilizing the IRS and the RS technique simultaneously. In fact, the concurrent use of both techniques yields power savings, which are beyond the cumulative power savings of using each technique separately.
This paper presents a wireless neural recording system featuring energy-efficient data compression and encryption. An ultra-high efficiency is achieved by leveraging compressed sensing (CS) for simultaneous data compression and encryption. CS enables sub-Nyquist sampling of neural signals by taking advantage of its intrinsic sparsity. It simultaneously encrypts the data with the sampling matrix being the cryptographic key. To share the key over an insecure wireless channel, we implement an elliptic-curve cryptography (ECC) based key exchanging protocol. The CS operation is executed in a custom-designed IC fabricated in 180nm CMOS technology. Mixed-signal circuits are designed to optimize the power efficiency of the matrix-vector multiplication (MVM) of the CS operation. The ECC algorithm is implemented in a low-power Cortex-M0 microcontroller (MCU). To be protected from timing and power analysis attacks, the implementation avoids possible data-dependent branches and also employs a randomized ECC initialization. At a compression ratio of 8x, the average correlated coefficient between the reconstructed signals and the uncompressed signals is 0.973, while the ciphertext-only attacks (CoA) achieve no better than 0.054 over 200,000 attacks. The prototype achieves a 35x power saving compared with conventional implementation in low-power MCUs. This work demonstrates a promising solution for future chronic neural recording systems with requirements in high energy efficiency and security.
In order to balance the interests of integrated energy operator (IEO) and users, a novel Stackelberg game-based optimization framework is proposed for the optimal scheduling of integrated demand response (IDR)-enabled integrated energy systems with uncertain renewable generations, where the IEO acts as the leader who pursues the maximization of his profits by setting energy prices, while the users are the follower who adjusts energy consumption plans to minimize their energy costs. Taking into account the inherent uncertainty of renewable generations, the probabilistic spinning reserve is written in the form of a chance constraint; in addition, a district heating network model is built considering the characteristics of time delay and thermal attenuation by fully exploiting its potential, and the flexible thermal comfort requirements of users in IDR are considered by introducing a predicted mean vote (PMV) index. To solve the raised model, sequence operation theory is introduced to convert the chance constraint into its deterministic equivalent form, and thereby, the leader-follower Stackelberg game is tackled into a mixed-integer quadratic programming formulation through Karush-Kuhn-Tucker optimality conditions and is finally solved by the CPLEX optimizer. The results of two case studies demonstrate that the proposed Stackelberg game-based approach manages to achieve the Stackelberg equilibrium between IEO and users by the coordination of renewable generations and IDR. Furthermore, the study on a real integrated energy system in China verifies the applicability of the proposed approach for real-world applications.
Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix under both the perfect channel state information (CSI) and the imperfect CSI. In order to tackle the challenging non-convex fractional problems, an algorithm based on semidefinite programming (SDP) relaxation is proposed for solving energy efficiency maximization problem under the perfect CSI case while an alternate optimization algorithm based on $mathcal{S}$-procedure is used for solving the problem under the imperfect CSI case. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in term of energy efficiency. Moreover, the tradeoff between energy efficiency and the secrecy rate is found in the IRS-assisted MISO network. Furthermore, it is shown that IRS can help improve energy efficiency even with the uncertainty of the CSI.
Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS.
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