This paper studies cooperative resource allocation schemes for the inter-cell interference control in multi-cell orthogonal frequency division multiple access (OFDMA) systems. For the purpose of exposition, the downlink transmission in a simplified three-cell system is examined. It is assumed that all cells simultaneously access the same frequency band using OFDMA, which corresponds to a universal frequency reuse in cellular systems. Unlike the conventional approach that treats subcarriers as separate dimensions for user scheduling and power allocation in OFDMA, the paper exploits the interference alignment (IA) technique to formulate a new cooperative resource allocation scheme for multi-cell OFDMA by incorporating the frequency-domain precoding over parallel subcarriers. The joint optimization of frequency-domain precoders via IA, subcarrier user selection and power allocation for all cells is then investigated to maximize the system sum-throughput. Numerical results for a symmetric channel setup reveal that the IA-based scheme achieves notable throughput gains over the traditional scheme without frequency-domain precoding only when the inter-cell interference link has a comparable strength as the direct signal link, and the direct-link signal-to-noise ratio (SNR) is sufficiently high. Motivated by this observation, two hybrid schemes are proposed to exploit the benefits of both traditional and IA-based schemes, for practical cellular systems with heterogenous channel conditions.
In this paper, we consider a RF energy harvesting cognitive radio network (CRN) in which cooperating secondary users (SUs) are equipped with energy harvesting capabilities. The SUs can harvest energy by converting the received radio frequency (RF) signal into electricity. It is assumed that the SUs have different SNRs due to their distances from the primary user (PU). Our objective is to maximize the energy efficiency of the CRN subject to the sensing accuracy constraint and energy constraint by finding the optimal SUs to participate in the cooperative spectrum sensing. The problem is solved analytically based on the convex optimization framework. After acquiring the optimal conditions based on karush-Kuhn-Tucker (KKT)conditions, an iterative algorithm is proposed to find the SUs for spectrum sensing. Since the problem is a NP-complete problem, we simplify it through mapping discrete parameter into real domain. Therefore, we can decide which SUs are selected for spectrum sensing at a lower computational complexity. A priority-based SU selection algorithm in cooperative spectrum sensing is proposed to improve the energy efficiency. Simulation results show that the proposed approach can improve the energy efficiency.
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Two separate applications of Wyner-Ziv coding of motion video coding are considered. The Wyner-Ziv theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally could achieve efficiency comparable to current interframe video compression systems. First results on the Wyner-Ziv coding scheme for motion video are reported that use interframe encoding, but interframe decoding. Secondly, Wyner-Ziv coding for the transmission of compressed video over an error-prone channel is applied. This coding scheme is used to generate a supplementary bit stream, which contains a coarsely quantized representation of the transmitted video signal. Using the conventionally decoded, error-concealed video signal as side information to decode the Wyner-Ziv bits, the transmission errors in the decoded video waveform are corrected up to a certain residual distortion, significantly improving the visual quality of the decoded video.
In this paper, we propose a pricing mechanism for the uplink communication cooperation to save the energy of mobile terminals (MTs) in wireless cellular network. Under the uncertainties of the other MTs' channel and battery conditions, a source MT in low battery level or bad channel condition is allowed to select and pay another MT in proximity to help forward its data package to the base station (BS). We formulate the source MT's pricing and load sharing problem as an optimization problem to minimize its expected energy cost. When the source MT cannot split its data package for a certain multimedia application, we motivate the selected relay MT to forward the whole data package by obtaining the optimal pricing through a dichotomous search algorithm. When the source MT can split the data package, we jointly optimize the pricing and load sharing with the relay MT and propose an alternating optimization algorithm that achieves near-optimal solution. Extensive numerical results are provided to show that our proposed pricing mechanism can significantly decrease the source MT's expected energy cost, and load sharing is more cost-efficient when the size of the data package is large and the average number of helping MTs is small.
This paper studies a new unmanned aerial vehicle (UAV)- enabled wireless power transfer (WPT) system, where a UAV-mounted energy transmitter (ET) broadcasts wireless energy to charge distributed energy receivers (ERs) on the ground. In particular, we consider a basic two-user scenario, and investigate how the UAV can optimally exploit its mobility via trajectory design to maximize the amount of energy transferred to the two ERs during a finite charging period. We characterize the achievable energy region of the two ERs, by optimizing the UAV's trajectory subject to a maximum speed constraint. We show that when the distance between the two ERs is smaller than a certain threshold, the Pareto boundary of the energy region is achieved when the UAV hovers above a fixed location between them during the whole charging period; while when their distance is larger than the threshold, to achieve the boundary of the energy region, the UAV in general needs to hover and fly between two different locations above the line connecting them. Numerical results show that the optimized UAV trajectory can significantly improve the WPT efficiency and fairness of the two ERs, especially when the UAV's maximum speed is large and/or the charging duration is long.
MmWave communication is a promising technology for 5G wireless systems. However, the large number of antennas used and the wide signal bandwidth in mmWave systems render the conventional multi-antenna techniques increasingly costly in terms of signal processing complexity, hardware implementation, and power consumption. In this article, we investigate cost-effective mmWave communications by first providing an overview of the main existing techniques that offer different trade-offs between performance and cost, and then focusing our discussion on a promising new technique based on the advanced lens antenna array. It is revealed that by exploiting the angle-dependent energy focusing property of lens arrays, together with the angular sparsity of the mmWave channels, mmWave lens antenna system is able to achieve capacity-optimal performance with very few RF chains and using low-complexity single-carrier transmission, even for wideband frequency-selective channels. Numerical results show that the lens-based system outperforms the benchmark schemes for mmWave communications in both spectrum efficiency and energy efficiency.
This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number $M$ of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame). To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-$M$ regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system. We define a metric, namely, the massive MIMO degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by $\log(M)$. We show that the proposed WET-MM system is optimal in terms of MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.
In conventional terrestrial cellular networks, mobile terminals (MTs) at the cell edge often pose a performance bottleneck due to their long distances from the serving ground base station (GBS), especially in hotspot period when the GBS is heavily loaded. This paper proposes a new hybrid network architecture by leveraging the use of unmanned aerial vehicle (UAV) as an aerial mobile base station, which flies cyclically along the cell edge to offload data traffic for cell-edge MTs. We aim to maximize the minimum throughput of all MTs by jointly optimizing the UAV's trajectory, bandwidth allocation and user partitioning. We first consider orthogonal spectrum sharing between the UAV and GBS, and then extend to spectrum reuse where the total bandwidth is shared by both the GBS and UAV with their mutual interference effectively avoided. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional GBS-only network; while the spectrum reuse scheme provides further throughput gains at the cost of slightly higher complexity for interference control. Moreover, compared to the conventional small-cell offloading scheme, the proposed UAV offloading scheme is shown to outperform in terms of throughput, besides saving the infrastructure cost.
