Exploiting interactions among signals to decode interfering transmissions with fewer receiving antennas

Li, Zhao; Ding, Jiamin; Dai, Xiaoqin; Shin, Kang G.; Liu, Jia

doi:10.1016/j.comcom.2018.12.008

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…5. However, since the processing of symbol combinations (1,1) and (−1, −1), as well as (1, −1) and (−1, 1), are the same, the number of items of the structures can be reduced by a factor of 1/2 [32]. According to Fig.…”

Section: Design Of Reception Of Interfering Tx-rx Pairmentioning

confidence: 99%

“…Defining the k th interference employs L k -order modulation scheme, the size of the structures (without reduction) in Fig. 5 can then be calculated as However, since some symbol combinations are processed in the same way, the number of items of the structures can be reduced by a factor of 1/2 [32].…”

Section: Design Of Reception Of Interfering Tx-rx Pairmentioning

confidence: 99%

“…Then, a reception mechanism given in Fig. 7 which is similar to that in [32], can be used to decode the desired data. In the flowchart,P 1 andĜ e denote the possible inner and outer precoders determined by the (k, α, Flag) field in Fig.…”

Section: Design Of Reception Of Interfering Tx-rx Pairmentioning

confidence: 99%

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Inside-Out Precoding to Manage Multiple Interferences From the Same Source

Bai

Shin

et al. 2020

IEEE Trans. Veh. Technol.

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With the continually increasing demand for higher data rate and link density as well as better network coverage, wireless networks, including 5G-enabled vehicular communication network (VCN), will be designed/deployed to cover more overlapping areas, experiencing more severe interferences. As a result, interference becomes a key impediment to the improvement of network performance, thus requiring a thorough investigation. There have been numerous interference management (IM) methods, of which interference alignment (IA) has been receiving significant attention in recent years. IA compresses the dimension of interference subspace by confining multiple interfering signals into a finite subspace, so that the desired signal subspace may be maximized. However, when IA is applied to the situation where multiple interferences are from the same source, the interference subspace cannot be compressed due to the fact that if these interfering components are aligned in the same direction at their unintended receiver (Rx), they will become indistinguishable at their desired Rx. To solve this problem, we propose Inside-Out Precoding (IOP). With IOP, multiple data streams that may cause interference to the other Rx are at first pre-processed at the interfering transmitter (Tx), by employing an inner-precoder which makes the streams spatially separable, and then, by exploiting interactions among wireless signals, multiple interfering components are treated as an effective interference to which an outer-precoder is applied, so that multiple interferences can be compressed into one dimension at the interfered Rx while making them distinguishable at their desired Rx. We present two IOP realizations -forward IOP (F-IOP) and backward IOP (B-IOP) -and propose a protocol to realize the synchronization of processing parameters at the interfering Tx and its intended Rx so that the Rx can adapt itself to the precoding strategy employed at the Tx side. Our in-depth analysis and simulation results have shown that the proposed IOP can effectively manage multiple interferences from the same source while guaranteeing the performance of transmission from the interfering Tx to its intended Rx.

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Section: Design Of Reception Of Interfering Tx-rx Pairmentioning

confidence: 99%

Section: Design Of Reception Of Interfering Tx-rx Pairmentioning

confidence: 99%

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Inside-Out Precoding to Manage Multiple Interferences From the Same Source

Bai

Shin

et al. 2020

IEEE Trans. Veh. Technol.

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“…i to recover the desired data. u (1) i is the principal left singular vector (i.e., the first column of the left singular matrix V i ) of H i . So, SE of Rx i can be calculated according to Eq.…”

Section: A Basic Signal Processing Of Spaismentioning

confidence: 99%

“…The rapid growth of users' traffics, continually increasing number of online devices, and more emerging mobile services and applications, have made the improvement of communication system's capacity an important issue needs to be well addressed. Although deploying small cells densely in existing cellular networks and improving spectrum efficiency via dynamic spectral access, spatial spectrum reuse, etc., are regarded as effective means to enhance network performance, interference has become an impediment in further improving the network capacity [1].…”

Section: Introductionmentioning

confidence: 99%

Space-Power Adaptive Interference Steering to Manage Interference in XIC

Liu

Ding

et al. 2020

IEEE Access

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With the rapid development of wireless communication technologies, interference has become a key impediment in the improvement of network capacity. In practice, although dense deployment of network can effectively increase the spectrum efficiency, severe co-channel interference (CCI) is likely incurred, especially for mobile users in the overlapping areas of multiple access points, leading to serious deterioration of these users' spectral efficiency (SE). Interference steering (IS), emerging as a novel interference management (IM) scheme, can not only eliminate the impact of interference on the desired transmission, but also incur less power overhead than other IM methods. Although some works conducted flexible design of IS via power domain adaptation, the space domain flexibility has not been exploited. Moreover, existing IS works are mainly for the Z-interference channel (ZIC), while in practical use X-interference channel (XIC) is more common. To remedy the above deficiencies, this paper proposes a space-power adaptive interference steering (SPAIS) method in XIC. By adaptively adjusting the strength and spatial feature of the steering signal, SPAIS can not only effectively suppress interference imposed on the interfered receiver (Rx), but also make full use of the power of the steering signal, thus enhancing desired data transmission. Our in-depth simulation results show that compared to existing IM methods, SPAIS can significantly improve the users' SE performance.

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When and how much to neutralize interference? Balancing the benefits and cost of interference management

Ding

et al. 2020

Wireless Netw

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Exploiting interactions among signals to decode interfering transmissions with fewer receiving antennas

Cited by 8 publications

References 20 publications

Inside-Out Precoding to Manage Multiple Interferences From the Same Source

Inside-Out Precoding to Manage Multiple Interferences From the Same Source

Space-Power Adaptive Interference Steering to Manage Interference in XIC

When and how much to neutralize interference? Balancing the benefits and cost of interference management

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