946 publications from this institution
We consider the problem of weighted sum-rate maximization (WSRMax) in wireless networks. This problem is known to be NP-hard and it plays a central role in resource allocation, link scheduling or in finding achievable rate regions for both singlecast and multicast networks. We propose a solution method, based on the branch and bound technique, which solves globally the WSRMax problem with an optimality certificate. Efficient bounding techniques are introduced as well.
A near-far resistant iterative algorithm for multiuser signature sequence delay estimation is presented. It is shown that the maximum-likelihood delay estimator can be replaced by the so-called blind ML delay estimator which depends on delays only, not on transmitted symbols and complex channel coefficients. A variable projection type algorithm is presented to compute the blind ML estimator in an iterative way. According to the simulation results, the delays for all users can be estimated reliably both in AWGN and fading channels by using the scheme being proposed.
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^(-5).
The current autonomous driving architecture places a heavy burden in signal processing for the graphics processing units (GPUs) in the car. This directly translates into battery drain and lower energy efficiency, crucial factors in electric vehicles. This is due to the high bit rate of the captured video and other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor at the top of the car which is an essential feature in autonomous vehicles. LiDAR is needed to obtain a high precision map for the vehicle AI to make relevant decisions. However, this is still a quite restricted view from the car. This is the same even in the case of cars without a LiDAR such as Tesla. The existing LiDARs and the cameras have limited horizontal and vertical fields of visions. In all cases it can be argued that precision is lower, given the smaller map generated. This also results in the accumulation of a large amount of data in the order of several TBs in a day, the storage of which becomes challenging. If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud. However, the required data rates in the order of several Gbps are difficult to be met even with the advent of 5G. Therefore, we propose to have a coordinated set of LiDAR's outside at an elevation which can provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency. The calculations we have based on industry standard equipment from several manufacturers show that this is not just a concept but a feasible system which can be implemented.The proposed system can play a supportive role with existing autonomous vehicle architecture and it is easily applicable in an urban area.
We consider the problem of MaxWeight scheduling in wireless multihop networks. This problem is known to be NP-hard. We propose a solution method, based on the branch and bound technique, which solves globally the MaxWeight scheduling problem with an optimality certificate. Efficient analytic bounding techniques are introduced as well.
Polar codes have received growing attention in the past decade and have been selected as the coding scheme for the control channel in the fifth generation (5G) wireless communication systems. However, the conventional polar codes have only been constructed by binary ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> ) kernel, which poses block length limitation to powers of 2. To attain more flexible block lengths, multi-kernel polar codes are proposed. In this paper, a combinational architecture for multi-kernel polar codes with high throughput is proposed based on successive cancellation decoding algorithm. The proposed scheme can decode pure-binary, pure-ternary ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> ), and binary-ternary mixed polar codes. The decoder's architecture is rate-flexible, meaning that a new code rate can be assigned to the decoder at every clock cycle. The proposed architecture is validated by FPGA implementation, and the results reveal that a code of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=81$ </tex-math></inline-formula> achieves the coded throughput of 1664.5 Mbps. A Python-based polar compiler is also proposed to automatically generate the HDL modules for target decoders. A designer can input the target block length and kernel ordering of a polar code and get the required VHDL files automatically.
We consider the problem of multicasting from a single source to a set of destinations. We assume that the source can either reach the destinations directly or forward its traffic through a set of relays. Due to the non-linear attenuation of the signal with distance, employing the relays can help to improve the signal quality at the destinations. Meanwhile, relays also consume energy for retransmission of the received information and the rate of communication can be decreased due to the multi-hop transmission. Under the performance objective of maximizing the common amount of information (number of bits) that the source sends to all destinations per Joule of the total energy spent, we wish to identify whether direct transmission from the source to the destinations is preferable as opposed to multi-hop forwarding through the relays. In the latter case, we also identify a) which subset of the relays should be activated, b) for how long, and c) the respective destinations that each relay has to serve. Finally, we provide a set of numerical results to support our analysis.
This paper addresses energy efficiency and throughput performance of joint LTE-A macrocell and femtocell deployment. We consider two techniques discontinuous transmission (DTX) and offloading of users from macrocells to lower power femtocells. When the DTX is applied base stations delay transmissions until sufficient user data is available to transmit using 40 percent or more of the system bandwidth. This allows the base stations to remain inactive for larger periods of time, and therefore reducing energy consumption. The target is to reduce the overall network energy consumption per area with minimal impact to the throughput. The numerical results show that when the majority of macrocell users can be offloaded to be served by femtocells the mean power per area consumption will decrease by up to 34.7 percent and at the same time a larger portion of users are able to meet the intended throughput target. When DTX is applied energy consumption is reduced further still to 45.3 percent.
Radio frequency (RF) wireless power transfer (WPT) is a promising technology to seamlessly charge low-power devices, but its low end-to-end power transfer efficiency remains a critical challenge. To address the latter, low-cost transmit/radiating architectures, e.g., based on reconfigurable intelligent surfaces (RISs), have shown great potential. Beyond diagonal (BD) RIS is a novel branch of RIS offering enhanced performance over traditional diagonal RIS (D-RIS) in wireless communications, but its potential gains in RF-WPT remain unexplored. Motivated by this, we analyze a BD-RIS-assisted single-antenna RF-WPT system to charge a single rectifier, and formulate a joint beamforming and multi-carrier waveform optimization problem aiming to maximize the harvested power. We propose two solutions relying on semi-definite programming for fully connected BD-RIS and an efficient low-complexity iterative method relying on successive convex approximation. Numerical results show that the proposed algorithms converge to a local optimum and that adding transmit sub-carriers or RIS elements improves the harvesting performance. We show that the transmit power budget impacts the relative power allocation among different sub-carriers depending on the rectifier's operating regime, while BD-RIS shapes the cascade channel differently for frequency-selective and flat scenarios. Finally, we verify by simulation that BD-RIS and D-RIS achieve the same performance under pure far-field line-of-sight conditions (in the absence of mutual coupling). Meanwhile, BD-RIS outperforms D-RIS as the non-line-of-sight components of the channel become dominant.
Bit error probability of linear receivers for multiuser communications is analysed. The results can be applied both for centralised multiuser or joint receivers as well as for decentralised single-user receivers. The analysis is presented for AWGN and known or perfectly estimated fading multipath channels. The analysis is also extended to the estimated fading multipath channel case with data-aided (DA) channel estimation. Two ways to approximate the averaging over the interfering data symbol combinations are considered and compared. One is a semi-analytic method, where only part of the interfering data symbol combinations are taken into consideration in the averaging. The other is the Gaussian approximation. In addition to MAI, the analysis directly incorporates the impact of the interpath interference (IPI) or inter symbol interference (ISI) to the receiver performance. Numerical examples are presented to demonstrate the usefulness of the method.
