Mobile data traffic is projected to grow at a phenomenal rate in the years to come. To cope with such growth, cellular network operators and equipment vendors are exploring various technologies to significantly improve network capacity. Utilization of more radio spectrum, heterogeneous network (HetNet) deployment, cell site densification, and coordinated multiple-point (CoMP) transmission and reception are among the ones that are currently being explored.
Interference mitigation receiver is also a focus area. It can improve the network capacity of existing deployments. It can also maximize the benefit of HetNet and CoMP. A post-decoding successive interference cancellation (IC) receiver can achieve open-loop MIMO capacity under the condition of perfect per-layer rate control. A MIMO stream is detected and decoded first and then cancelled from the received composite signal before the next stream is detected.
The traditionally hard-decision based interference cancellation scheme has been extended to soft-decision based interference cancellation schemes. This allows cancellation to still be performed even when the previously decoded stream is not error free, e.g., when the per-layer rate control is not perfect. Soft symbol estimates can be formed based on the decoder output bit likelihood ratios (or bit log likelihood ratios). When the bit likelihood ratios are of high magnitudes, indicating strong confidence in the bit estimates, the soft symbol estimates are likely to be identical to, or in the vicinity of, the actual transmitted symbols. In such cases, interference contributed by these symbols can be very much cancelled. On the other hand, if the bit likelihood ratios are of low magnitudes, indicating weak confidence in the bit estimates, the soft symbol estimates will be in the vicinity of the origin, resulting in almost no cancellation.
Regardless of whether hard or soft decision based symbols are used in interference cancellation, forming interfering symbol estimates using the decoder outputs is an important aspect. This allows the quality of interference symbol estimates to be boosted by the coding gain. If the interfering symbols come from a MIMO stream intended for the same user equipment (UE) as seen in FIG. 1A, the UE 120 (of network 100) is informed of the interfering data stream's modulation and coding rate, so-called transport format (TF) or modulation and coding scheme (MCS). In this instance, post-decoding interference cancellation can readily be performed. In this Figure, a cell 130 is associated with the serving base station 110, and streams 1 and 2 are respectively assumed to be the desired and interfering data streams from the perspective of the UE 120.
However, in multi-user MIMO (MU-MIMO) scenarios, as illustrated in FIG. 1B, the interfering data stream is intended for another UE 122. In this figure, it is assumed that the UEs 120, 122 are co-scheduled UEs and data streams 1 and 2 are respectively intended for UE's 120 and 122. Thus, from the perspective of the UE 120, stream 2 is again the interfering stream. Since the interfering data stream is intended for another UE 122, the co-scheduled UE 120 does not have the knowledge of the TF or MCS used in the interfering data stream. It is therefore more difficult to cancel the interfering data stream. In LTE, the encoded bits of a physical data channel are scrambled by, or according to, an identity number associated with the intended UE. Thus, a descrambling step is needed before decoding. It is therefore more difficult to cancel the interfering data stream in the MU-MIMO case.
Severe inter-cell interference can be experienced by a UE 120 at a cell edge. Such interference can originate from different base stations as illustrated in FIG. 1C. In this Figure, it is assumed that the UE 120 is at an edge of the cell 130 being served by the base station 130 and the base station 130 is transmitting data stream 1 intended to the UE 120. It is also assumed that the base station 112 corresponding to a cell 132 is transmitting data stream 132 to its UE 122. Thus, again from the perspective of the UE 120, data stream 2 is an interfering stream. Like the case of MU-MIMO, the TF, MCS or UE ID of the interfering data stream is not known to the victim UE 120 at the cell edge.
The problem of inter-cell interference can be more pronounced in a range-expansion zone of a HetNet deployment as illustrated in FIG. 1D. In this Figure, the serving base station is the pico base station 110 associated with cell 130, which is within a cell 132 served by a macro base station 112. A range expansion zone 140, which is the striped area within the cell 130, represents a zone where the path loss to the macro base station 112 is higher than that to the closest pico base station 110 while the received power from the macro base station 112 is higher than that from the closest pico base station 110 due to large transmit power difference.
When the UE 120 is in the range-expansion zone 140, it can be beneficial to offload the traffic to the pico base station 110 as this allows the macro and other spatially isolated pico base stations to serve other UEs using the same radio resources (code, time and frequency allocation). However, the victim UE 120 in the range-expansion zone can be subject to severe interference from the macro base station 112 if one or more data streams (e.g., data stream 2) are transmitted by the macro base station 112.
For MU-MIMO and other-cell interference, it is beneficial to cancel the interfering data stream based on post-decoding symbol estimates. Such cancellation can provide dramatic throughput increases for an affected UE in many typical scenarios.
Without the knowledge of the TF, MCS or UE ID used in the interfering data stream, a victim UE has to detect this information blindly or through eavesdropping. However, the complexity of blindly detecting the coding rate is very high. Eavesdropping on the signalling channel carrying TF or MCS is also very difficult as such signalling channel is masked by the intended UE's identity, which is not known to the victim UE. The UE IDs may be inferred by using blind (hypothesis-testing) approaches, but the related complexity is again high and the detection robustness in environments with fast user turn-around may be low. Similarly, blindly inferring the UE ID from an LTE data channel is extremely difficult.
In addition to TF, MCS or UE ID, radio resource allocation parameters also need to be known. In high-speed packet access (HSPA), this includes the information about scrambling and channelization codes allocated to the interfering data stream, whereas in LTE, this includes the information about the radio resource elements (REs) allocated to the interfering data stream. Blindly detecting such radio resource allocation parameters is also extremely difficult.
In addition to blind detection complexity issues, obtaining the configuration information via eavesdropping is complicated by the fact that the downlink (DL) control channel for the other UE(s) may be received with insufficient signal-to-interference-and-noise ratio (SINR) by the victim UE, due to worse effective geometry and/or transmit power control (TPC) being applied to the control channel.
A base station can signal its current antenna configuration information to inactive UEs for facilitating channel quality indicator (CQI) estimation and has been proposed in Grant et al., US Publication 2007/0286124 A1 herein incorporated by reference in its entirety. According to Grant et al., such signaling is broadcasted. Furthermore, a special group identifier may be used to reach a group of mobile UEs. However, broadcasting antenna configuration information alone does not help much in facilitating interference cancellation at mobile UEs.