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Register a new userAn Uplink Control Channel Design with Complementary Sequences for Unlicensed Bands
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In this paper, two modulation schemes based on complementary sequences (CSs) are proposed for uplink control channels in unlicensed bands. These schemes address high peak-to-average-power ratio (PAPR) under non-contiguous resource allocation in the frequency domain and reduce the maximum PAPR to 3 dB. The first scheme allows the users to transmit a small amount of uplink control information (UCI) such as acknowledgment signals and does not introduce a trade-off between PAPR and co-channel interference (CCI). The second scheme, which enables up to 21 UCI bits for a single user or 11 UCI bits for three users in an interlace, is based on a new theorem introduced in this paper. This theorem leads distinct CSs compatible with a wide variety of resource allocations while capturing the inherent relationship between CSs and Reed-Muller (RM) codes, which makes CSs more useful for practical systems. The numerical results show that the proposed schemes maintain the low-PAPR benefits without increasing the error rate for non-contiguous resource allocations in the frequency domain.
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In this study, we propose two schemes for uplink control channels based on non-contiguous complementary sequences (CSs) where the peak-to-average-power ratio (PAPR) of the resulting orthogonal frequency division multiplexing (OFDM) signal is always less than or equal to 3 dB. To obtain the proposed schemes, we extend Golays concatenation and interleaving methods by considering extra upsampling and shifting parameters. The proposed schemes enable a flexible non-contiguous resource allocation in frequency, e.g., an arbitrary number of null symbols between the occupied resource blocks (RBs). The first scheme separates the PAPR minimization and the inter-cell interference minimization problems. While the former is solved by spreading the sequences in a Golay complementary pair (GCP) with the sequences in another GCP, the latter is managed by designing a set of GCPs with low cross-correlation. The second scheme generates reference symbols (RSs) and data symbols on each RB as parts of an encoded CS. Therefore, it enables coherent detection at the receiver side. The numerical results show that the proposed schemes offer significantly improved PAPR and cubic metric (CM) results in case of non-contiguous resource allocation as compared to the sequences defined in 3GPP New Radio (NR) and Zadoff-Chu (ZC) sequences.
The 3rd Generation Partnership Project (3GPP) recently started standardizing the Licensed-Assisted Access using LTE for small cells, referred to as Dual Band Femtocell (DBF) in this paper, which uses LTE air interface in both licensed and unlicensed bands based on the Long Term Evolution (LTE) carrier aggregation feature. Alternatively, the Small Cell Forum introduced the Integrated Femto-WiFi (IFW) small cell which simultaneously accesses both the licensed band (via cellular interface) and the unlicensed band (via WiFi interface). In this paper, a practical algorithm for IFW and DBF to automatically balance their traffic in licensed and unlicensed bands, based on the real-time channel, interference and traffic conditions of both bands is described. The algorithm considers the fact that some smart devices (sDevices) have both cellular and WiFi radios while some WiFi-only devices (wDevices) may only have WiFi radio. In addition, the algorithm considers a realistic scenario where a single small cell user may simultaneously use multiple sDevices and wDevices via either the IFW, or the DBF in conjunction with a Wireless Local Area Network (WLAN). The goal is to maximize the total user satisfaction/utility of the small cell user, while keeping the interference from small cell to macrocell below predefined thresholds. The algorithm can be implemented at the Radio Link Control (RLC) or the network layer of the IFW and DBF small cell base stations. Results demonstrate that the proposed traffic-balancing algorithm applied to either IFW or DBF significantly increases sum utility of all macrocell and small cell users, compared with the current practices. Finally, various implementation issues of IFW and DBF are addressed.
In this study, we propose partitioned complementary sequences (CSs) where the gaps between the clusters encode information bits to achieve low peak-to-average-power ratio (PAPR) orthogonal frequency division multiplexing (OFDM) symbols. We show that the partitioning rule without losing the feature of being a CS coincides with the non-squashing partitions of a positive integer and leads to a symmetric separation of clusters. We analytically derive the number of partitioned CSs for given bandwidth and a minimum distance constraint and obtain the corresponding recursive methods for enumerating the values of separations. We show that partitioning can increase the spectral efficiency (SE) without changing the alphabet of the nonzero elements of the CS, i.e., standard CSs relying on Reed-Muller (RM) code. We also develop an encoder for partitioned CSs and a maximum-likelihood-based recursive decoder for additive white Gaussian noise (AWGN) and fading channels. Our results indicate that the partitioned CSs under a minimum distance constraint can perform similar to the standard CSs in terms of average block error rate (BLER) and provide a higher SE at the expense of a limited signal-to-noise ratio (SNR) loss.
Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.
This paper provides the signal-to-interference-plus-noise ratio (SINR) complimentary cumulative distribution function (CCDF) and average data rate of the normalized SNR-based scheduling in an uplink cellular network using stochastic geometry. The uplink analysis is essentially different from the downlink analysis in that the per-user transmit power control is performed and that the interferers are composed of at most one transmitting user in each cell other than the target cell. In addition, as the effect of multi-user diversity varies from cell to cell depending on the number of users involved in the scheduling, the distribution of the number of users is required to obtain the averaged performance of the scheduling. This paper derives the SINR CCDF relative to the typical scheduled user by focusing on two incompatible cases, where the scheduler selects a user from all the users in the corresponding Voronoi cell or does not select users near cell edges. In each case, the SINR CCDF is marginalized over the distribution of the number of users involved in the scheduling, which is asymptotically correct if the BS density is sufficiently large or small. Through the simulations, the accuracies of the analytical results are validated for both cases, and the scheduling gains are evaluated to confirm the multi-user diversity gain.
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